Cover

ISBN print: 978-3-0364-0988-7
ISBN eBook: 978-3-0364-1988-6
DOI book: 10.4028/b-X4tqQD
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Machining, Cutting and
Severe Plastic Deformation Processes

Edited by
Domenico Umbrello
Takashi Matsumura
Pedro José Arrazola

Machining, Cutting and Severe Plastic Deformation Processes

Special topic volume with invited peer-reviewed papers only

Edited by
Domenico Umbrello, Takashi Matsumura and Pedro José Arrazola
Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

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Volumes 450 of
Defect and Diffusion Forum
ISSN print 1012-0386
ISSN web 1662-9507
(Pt. A of Diffusion and Defect Data - Solid State Data ISSN 0377-6883)

Full text available online at https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp -1-0

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Accepted:
2026-04-09

Online:
2026-04-15

Preface

The original version of this paper is available on https://www.scientific.net/DDF.450.-1.pdf

The issue on Machining, Cutting, Blanking, and Severe Plastic Deformation (SPD) Processes will address a broad spectrum of topics central to advanced manufacturing and material processing. It focuses on both experimental and numerical investigations aimed at enhancing the understanding of machining and SPD mechanisms. Particular emphasis will be placed on accurate material characterization and the formulation of robust constitutive models to predict material behavior under extreme deformation conditions such it occurs in machining, cutting and blanking. The issue is also exploring stress distribution in SPD techniques such as burnishing and stir processing, assessing their impact on mechanical performance. Critical aspects such as tool wear, frictional phenomena, and fracture mechanisms will also be examined. Additionally, the issue covers surface integrity, with attention to residual stress prediction, surface roughness, hardness, and microstructural transformations. Lastly, innovative cooling and lubrication techniques-including near-dry machining, Minimum Quantity Lubrication (MQL), and cryogenic methods-will be highlighted for their role in improving machinability, workability, and product performance.

Editors

The original version of this paper is available on https://www.scientific.net/DDF.450.-1.pdf

Umbrello, Domenico
Matsumura, Takashi
Arrazola, Pedro J.

Co-editors

The original version of this paper is available on https://www.scientific.net/DDF.450.-1.pdf

Courbon, Cédric
Germain, Guenael
Ducobu, Francois
Rotella, Giovanna
Ortiz de Zarate, Gorka
Tamura, Soichi

University of Calabria
Tokyo Denki University
Mondragon University

École Nationale d'Ingénieurs de Saint-Étienne
Arts et Métiers Institute of Technology
University of Mons
University LUM Giuseppe Degennaro
Mondragon University
Tokyo Denki University

Table of Contents

Preface

Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

Christoph Lerez, Viktor Artiushenko, Matthias Hackert-Oschätzchen

The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Anna Bottin, Rachele Bertolini, Haizea Gonzalez, Eneko Ukar, Stefania Bruschi

Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Stefania Ferrisi, Sebastian Lang, Mohamadreza Afrasiabi, Rosita Guido, Domenico Umbrello, Giuseppina Ambrogio, Markus Bambach

Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Rachele Bertolini, Toushiqul Islam, Shuaihang Pan, Edoardo Ghinatti, Stefania Bruschi

On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

François Ducobu, Olivier Pantalé, Laurent Spitaels, Domenico Umbrello

Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

Martin Beutner, Matthias Hackert-Oschätzchen, Marco Eich, Bernhard Karpuschewski

Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Nils Paucke, Martin Beutner, Andreas Lümkemann, Keith Thomas, Martin Učík, Hamid Bolvardi, Matthias Hackert-Oschätzchen

Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

Mikel Etxebeste, Iñaki M. Arrieta, Gorka Ortiz de Zarate, Pedro José Arrazola

Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Shoichi Tamura, Takashi Matsumura

Static Finite Element Modeling of Milling in Flexible Composite Plates

Matthias Nutte, Edouard Rivière-Lorphèvre, Valentin Dambly, Pedro José Arrazola, Ismail Lazoglu, Aurélie Granjon, François Ducobu

Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Agata Sposato, Maria Rosaria Saffioti, Chiara Morano, Francesco Modica, Giovanna Rotella, Irene Fassi, Vito Basile

Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Agata Sposato, Maria Rosaria Saffioti, Zheng Liu, Enrico Simonetto, Giovanna Rotella, Domenico Umbrello

Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Dimitrios Apollon Gartzonikas, Nikolaos E. Karkalos, Angelos P. Markopoulos, Panorios Benardos

Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

Gorka Ortiz de Zarate, Iñigo Rodriguez, Ainara Oruna, Harry Otalora, Mikel Etxebeste, Pedro José Arrazola

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 1-8

doi: 10.4028/p-sP9lea

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-27

Revised:
2026-02-17

Accepted:
2026-02-22

Online:
2026-04-15

Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

Christoph Lerez 1,a*, Viktor Artiushenko 2, b, Matthias Hackert-Oschätzchen 1,c

1 Chair of Manufacturing Technology with Focus Machining, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

2 Junior Professorship for Al Application in Production and Logistics, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

a*lerez@ovgu.de, b viktor.artiushenko@ovgu.de, c matthias.hackert-oschaetzchen@ovgu.de

*corresponding author

Keywords: turn-milling, process monitoring, artificial intelligence, machine learning.

Abstract

In modern precision manufacturing, optimizing complex processes like turn-milling is crucial for reducing production costs and ensuring high surface integrity. In this study the application of artificial intelligence, specifically machine learning (ML), for modeling turn-milling processes is investigated. The complexity of machining operations and the multitude of influencing input parameters often lead to time-consuming setups, particularly in single-part or small series manufacturing. Traditional process monitoring methods frequently fall short due to system complexity, prompting the exploration of ML for process optimization and automation. Focusing on orthogonal turn-milling, experimental data was collected to address regression problems such as tool wear and surface roughness, as well as tool condition classification. Three regression models - linear, polynomial, and support vector regression (SVR) - and four classification models - logistic regression, neural networks, support vector machines (SVM), and decision trees - were trained and validated using k-fold cross-validation. For regression models, root mean square error (RMSE) was used as the performance evaluation metric, while accuracy and F1-score were employed for classification problems. The results indicate that ML algorithms provide enhanced flexibility and accuracy compared to traditional statistical techniques, offering potential reductions in time and costs in process setups. By optimizing parameters iteratively, ML models demonstrate higher precision, reducing the need for extensive empirical research and the associated experimental costs. The developed models can be adapted to various manufacturing processes with minimal code adjustments, broadening their applicability and efficiency.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

The turn-milling process can be considered an alternative to turning and grinding. Compared to grinding, the turn-milling process allows for deterministic changes in the surface structure because a tool with geometrically defined cutting edges is applied [1]. This machining process also has ecological and economic advantages over grinding. Purchasing a separate grinding machine is unnecessary, and there is no need for the disposal of grinding sludge [2]. Moreover, turn-milling can be operated under dry conditions [1].

In contrast to turning, turn-milling utilizes a tool with multiple cutting edges. This reduces contact time and increases tool life [3]. Using orthogonal turn-milling, a tenfold improvement in surface quality can be achieved compared to turning, while maintaining the same productivity [4, 5]. Based on its kinematics, orthogonal turn-milling is more suitable for high-speed cutting (HSC) than conventional turning, where centrifugal force impedes the process [6, 7].

However, modeling the turn-milling process is challenging and expensive because extensive machining tests are required [8]. Furthermore, the material properties of both the workpiece and the tool significantly affect the accuracy of the model [4].

ML techniques are already widely applied to support critical decisions in medicine, financial markets, and the energy sector. Although ML is increasingly used in mechanical engineering,
manufacturing technology is currently in an early phase of ML application [9]. This is due to the complex, dynamic, and even chaotic nature of manufacturing systems [10]. Traditional methods based on modeling cause-and-effect relationships are reaching their limits due to the rapidly growing complexity and high dimensionality of modern manufacturing processes [11]. ML techniques can address many of today's major challenges in complex manufacturing systems because they offer the following advantages:

The ability to handle high-dimensional problems and data

The ability to discover previously unknown (implicit) knowledge and identify implicit relationships in datasets

Real-time processing

The capability to handle various data types (numeric, nominal, text, and images)

Enhanced user-friendliness of algorithms

Reduced data requirements in certain cases

These advantages motivate the continued implementation of ML in manufacturing. However, several key issues create uncertainty and sometimes prevent companies from making meaningful investments in ML. These include unclear legal aspects regarding responsibility of decisions delegated from humans to machines and the time-consuming nature of data preparation [9].

Collecting relevant data can be challenging in manufacturing, as necessary data may not always be available, and high-dimensional data may contain large amounts of irrelevant and redundant information. This negatively impacts the performance of ML algorithms [12].

Another challenge is the interpretation of results. Not only does the representation of the output affect interpretation, but also the peculiarities of the chosen algorithm and hyperparameter settings must be considered [10].

Nevertheless, the general ability of ML algorithms to achieve desired results in a manufacturing environment has been successfully demonstrated [10].

To address these challenges, this study investigates the potential of Machine Learning to automate and optimize the modeling of orthogonal turn-milling processes. Based on experimental data, three regression models were developed to predict tool wear (flank wear) and surface roughness, alongside four classification models to evaluate tool condition. To ensure statistical reliability despite a limited dataset - a common constraint in small-series manufacturing - a k -fold cross-validation methodology was implemented. The main contribution of this work lies in demonstrating that classical ML algorithms can outperform traditional statistical methods in capturing non-linear process behaviors with high precision. Furthermore, the developed framework offers a flexible, code-agnostic approach that can be adapted to various manufacturing processes, significantly reducing the time and cost associated with empirical process setup.

Experimental Methodology

The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

Cutting conditions in orthogonal turn-milling include tool and workpiece rotation speed, depth of cut, tool diameter, axial feed rate, eccentricity, and cutting speed. The arithmetic mean roughness and flank wear on two surfaces of the two inserts were selected as target variables because they characterize the quality of the produced components and the economic efficiency of the machining process. In this study, the axial depth of cut was varied to minimize the amount of data and allow for initial assessments of algorithm performance.

The following dependencies were examined:

Flank wear on two surfaces of the tool as a function of depth of cut

Surface roughness parameters as a function of flank wear

Surface roughness parameters as a function of depth of cut

In Fig. 1 the experimental setup is shown in a DMG MORI CTX Beta 1250 TC. The tool used was a ∅10 mm Sandvik CoroMill 390 milling cutter with two ISO 390R-070202M-PM 1130 P2.1.Z.AN indexable inserts with a maximum depth of cut of 5.8 mm and a feed per tooth of fz =0.03 mm−0.1 mm . The tool was clamped in the intelligent tool holder ProMicron Spike. It is equipped with sensors
for measuring cutting forces, bending moment, torsion and temperature. A workpiece made of 100 Cr 6 bearing steel was machined. In this experiment, a section with a diameter of ∅65 mm and a length of 25 mm was machined. To ensure the objectivity of the wear measurements, the indexable inserts were replaced after each machining cycle, regardless of whether they were worn or suitable for further machining. In total 11 experiments were initially carried out. The axial depth of cut was changed each time and varied in the range ap =0.05−0.45 mm. The cutting speed of 285 m/min recommended by the tool manufacturer was used.

[picture]

Fig. 1. Experimental setup in a DMG MORI CTX Beta 1250 TC lathe.

Fig. 1. Experimental setup in a DMG MORI CTX Beta 1250 TC lathe.

The resulting wear on the tool was measured on the main and secondary cutting edges. The Keyence VHX 5000 digital microscope was used for this purpose, see Fig. 2. W feed represents the width of the flank wear land in feed direction and Wap in axial direction respectively. The NanoFocus confocal microscope for three-dimensional optical surface measurement was used to measure the surface roughness of the workpiece. A photo of the measurement process is shown in Fig. 3.

[picture]

Fig. 2. a) Wear measurement with a Keyence VHX 5000, b) Wear in feed direction Wfeed ,c ) Wear in axial direction top ( Wap  ).

Fig. 2. a) Wear measurement with a Keyence VHX 5000, b) Wear in feed direction W feed , c ) Wear in axial direction top ( W ap ).

[picture]

Fig. 3. Surface roughness measurement using confocal microscope NanoFocus and exemplary measurement results of the workpiece.

Fig. 3. Surface roughness measurement using confocal microscope NanoFocus and exemplary measurement results of the workpiece.

Features relevant for the defined target functions were selected. The goal was to remove irrelevant features that could impair model accuracy. The algorithms should be capable of creating regression models and accurately predicting targets after learning. Particularly with small datasets, a limited number of parameters was employed. Classification models for tool condition monitoring were developed to classify wear.

In data cleaning, missing, faulty, or redundant data were handled to ensure model accuracy. Problems with missing data were largely excluded thanks to attentive data collection. Results from experiments under different conditions or with peculiar outcomes were removed to avoid biases. Datasets were processed and sorted using programming commands, with feature and target values stored as matrices or vectors for ML algorithms, and encoding was applied to transform categorical variables into binary form.

Data scaling with methods such as normalization and standardization was used to minimize negative effects of varying scales on model performance. Correlation between variables was evaluated using Pearson and Spearman correlation coefficients [13]. For evaluating model accuracy, the K-fold cross-validation method [14] was used in small datasets to avoid surveillance errors during model verification.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

The resulting surface roughness Ra of the workpiece, the wear in feed direction Wfeed  and the wear in cutting axial direction Wap  are the targeted output parameters and are shown in Table 1 together with the corresponding input parameter ap.

Table 1. Experimental parameters and measured output variables (flank wear and surface roughness) for the orthogonal turn-milling tests.

# | ap [mm] | Wfeed (Insert1) [μm] | Wfeed (Insert2) [μm] | Wap (Insert1) [μm] | Wap (Insert2) [μm] | Ra [μm]
1 | 0.45 | 917 | 932 | 1018 | 1012 | 2.887
2 | 0.25 | 368 | 480 | 530 | 599 | 0.769
3 | 0.05 | 17 | 7 | 0 | 0 | 0.163
4 | 0.1 | 36 | 52 | 0 | 0 | 0.163
5 | 0.15 | 74 | 81 | 74 | 63 | 0.131
6 | 0.2 | 77 | 55 | 118 | 0 | 0.212
7 | 0.25 | 93 | 204 | 0 | 187 | 0.286
8 | 0.3 | 127 | 125 | 92 | 75 | 0.177
9 | 0.35 | 109 | 105 | 159 | 122 | 0.312
10 | 0.4 | 0 | 56 | 0 | 108 | 0.167
11 | 0.45 | 166 | 257 | 115 | 211 | 0.272

To utilize the results of the experiments, the data collected there was processed as described in [15] to make it usable for various machine learning approaches. This included selecting functions and characteristics, cleaning up the data, and partially converting the data into binary form-e.g., tool wear status-as worn (1) for a wear mark width >100μ m or not worn ( 0 ). In addition, it was checked whether the recorded data correlate. Both Pearson and Spearman correlation coefficients [13] were determined for this purpose. It was found that surface roughness and tool wear correlate with depth of cut, see Fig. 4.

[picture]

Fig. 4. Examples of training results: (a) linear regression; (b) polynom regression.

Fig. 4. Examples of training results: (a) linear regression; (b) polynom regression.

According to [14], the following criteria were used to select possible machine learning approaches: explainability, dimensionality of the data, non-linearity of the data and training and prediction speed. Machine learning regression algorithms such as linear regression [16], polynomial regression [17] and SVR [18] were compared with a statistical regression method. Fig. 5 shows an example of the training results of the linear and polynomial regression. K-fold cross-validation was used to validate the algorithms due to the small amount of data [14]. The RMSE was chosen as comparison value. The lower the RMSE value during validation, the more accurate the model is.

[picture]

Fig. 5. Validation results for the dependence of wear (a) and surface roughness (b) on the axial depth of cut.

Fig. 5. Validation results for the dependence of wear (a) and surface roughness (b) on the axial depth of cut.

In all cases, a significantly higher RMSE value was achieved using polynomial regression. The possible reason for this is the high sensitivity of the polynomial function with small data sets. This means that the parameter values are very dependent on each point in the training set. Moreover, the Pearson coefficient values are high in the considered dependencies, which means strong linear correlation.

The models generated by the least squares method and linear regression using gradient descent are almost the same for all data sets. This confirms the accuracy of the ML algorithms developed.
For the dependencies wear of depth of cut and surface roughness of depth of cut, the SVR algorithm produced the best accuracy. However, the SVR algorithm is a "black box" algorithm and accordingly has a hidden model. Therefore, for further theoretical studies of these dependencies, the use of linear regression by optimization methods is recommended as an alternative to the least squares calculation
method. However, the SVR algorithm is well suited for practical applications where only the model performance is important.

The achieved accuracy of the developed algorithms is not yet sufficient to implement the generated models. To increase the accuracy, the training data set must be significantly enlarged.

The same was done with machine learning classification algorithms such as logistic regression, neural networks [19], SVM [20] and decision trees [21]. Two criteria were used to assess the accuracy of classification algorithms: Accuracy and F-Score. The accuracy shows how many examples out of the total number were correctly classified, see Table 2.When analyzing the two criteria, it was found that logistic regression, SVM and neural network perform with almost equal accuracy for the data set at hand. The highest accuracy was achieved using the decision tree, which is very suitable for theoretical research. For practical purposes, SVM is also a possible variant because it is trained much faster compared to the decision tree.

Overall, the average accuracy of the developed algorithms is not sufficient for validation. This is due to the fact that the data set under consideration contains a small number of data points. This means that algorithms are heavily penalized for each classification error during validation. The analysis performed can only be used to compare the algorithms. For the objective accuracy assessment of the obtained models, several data points must be tested during validation.

Table 2. Validation results of the classification algorithms.

Logistic

Regression

Neural

Network

SVM

Decision

Tree (ID3)

Accuracy | 0.73 | 0.73 | 0.75 | 0.84
F-Score | 0.64 | 0.64 | 0.63 | 0.78

Conclusion and Future Work

The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

In this work ML was investigated as a tool for modeling of orthogonal turn-milling. The aim was to determine whether ML algorithms could optimize and automate the research process.
At the beginning, the relevant dependencies were defined, and experiments were conducted to collect data. The flank wear as a function of the axial depth of cut and the surface roughness as a function of the axial depth of cut were considered as regression problems. Additionally, the classification of the tool condition based on the machining parameters was examined. The data collected during the experiments were analyzed, processed, and prepared for use in the ML algorithms.

Three ML algorithms were developed to model the regression dependencies: linear regression, polynomial regression, and SVR. In parallel, four classification algorithms were implemented to predict the tool condition, including logistic regression, neural networks, SVM, and a decision tree. The trained models were validated using the k -fold method to assess their accuracy and generalization capability. Based on the validation results, the optimal algorithm for each dependency was selected.

A key difference between ML algorithms and traditional statistical modeling lies in the way parameters are determined. While conventional statistical methods calculate parameters using analytical formulas (e.g. the least-squares method), ML algorithms adjust these parameters iteratively through optimization techniques such as gradient descent. This iterative approach provides greater flexibility for adapting to different datasets and function types. Whereas traditional statistical modeling often requires extensive manual programming and is time-consuming, ML algorithms automate the model creation process and help to avoid programming errors. Furthermore, ML algorithms can be applied to various processes and data dimensions without requiring modifications to the source code.

When comparing the models generated by ML algorithms with those obtained using the leastsquares method, it was found that in three out of four cases, the ML models were more accurate. SVR, in particular, achieved higher accuracy despite being based on different mathematical principles. However, the SVR algorithm is not suitable for examining theoretical relationships due to its blackbox nature, meaning the structure of the final model is not interpretable. Nevertheless, it is particularly
well-suited for practical applications, such as adaptive control and quality monitoring systems, due to its high accuracy and efficiency.

Although the developed classification models achieved relatively high prediction accuracy, they cannot yet be directly implemented in production. Further experiments are required to account for additional influencing factors, such as machining duration. In future developments, an optimized model could potentially detect tool breakage at an early stage, thereby increasing production safety. For this purpose, real-time data from sensors integrated into the manufacturing process must be processed. To handle this data, other data-driven approaches based on deep learning methods, as described in [22], can be used.

While the scope of this study is focused on a defined set of orthogonal turn-milling parameters, the results demonstrate a significant methodological advantage: the ability of ML algorithms to provide higher predictive accuracy than conventional methods even under data-sparse conditions. This suggests that the proposed framework is not merely a specific solution for one process, but a scalable approach for industrial environments where extensive empirical testing is economically unfeasible.

The results of this study highlight the potential of Machine Learning in modeling of orthogonal turn-milling. By using ML algorithms, the creation of empirical models can be automated, reducing the time and cost associated with investigating new machining processes. Furthermore, ML algorithms are more flexible and can produce accurate predictions even when traditional statistical methods reach their limits. This facilitates a more efficient design of research processes and reduces the number of required experiments, which in turn lowers overall costs.

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The original version of this paper is available on https://www.scientific.net/DDF.450.1.pdf

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The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 9-18

doi: 10.4028/p-gXx7ph

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-19

Revised:
2026-02-13

Accepted:
2026-02-22

Online:
2026-04-15

The Role of Tool Texture Design on the Chip Formation in PEEK Machining

The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

Anna Bottin 1,a*, Rachele Bertolini 1, b, Haizea Gonzalez 2,c, Eneko Ukar 2, d and Stefania Bruschi 1,e

¹Department of Industrial Engineering, University of Padova, via Venezia 1, 35131, Padova, Italy

2 Department of Mechanical Engineering - CFAA, University of the Basque Country UPV/EHU, PI. Torres Quevedo 1, 48013 Bilbao, Spain

a  anna.bottin.1@phd.unipd.it, b  rachele.bertolini@unipd.it, chaizea.gonzalez@ehu.eus, deneko.ukar@ehu.eus, e  stefania.bruschi@unipd.it

Keywords: textured tools, chip breakability, polymer turning.

Abstract

Biocompatible, chemical-resistant, and low-wear-rate materials are essential in biomedical applications to produce durable components that can withstand the conditions of the human body. PEEK is increasingly used due to its mechanical properties that are similar to those of human bones, making it a common material in orthopaedic prostheses. However, its low thermal conductivity, coupled with limited ductility, makes it difficult to machine. One of the main issues is the formation of continuous chips, which can reduce productivity and compromise final product quality. Innovative approaches, such as cryogenic machining and textured tools, have been recently studied to overcome this issue. Cryogenic machining can actively change the chip morphology from continuous, obtained in dry machining, to fragmented. On the other hand, textured tools can alter the chip flow, acting as chip breakers. This work examines how variations in texture design, specifically the distance of the texture from the cutting edge and the groove depth, may affect chip formation. To do that, turning trials were conducted under both dry and cryogenic cooling conditions using different textured tools. The results were assessed based on chip-tool contact length, chip dimensions, and morphology. Forces and temperatures were also acquired during the turning trials. The findings are that textured tools modify chip morphology in both dry and cryogenic conditions by altering chip flow on the insert. Deeper textures placed close to the cutting edge enhance chip breakability during cryogenic cooling and modify chip morphology in both machining environments.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

Prosthetic materials must exhibit biocompatibility, high chemical stability, and wear resistance to ensure safety in contact with human tissues, and sufficient mechanical strength to guarantee durability. In addition, their mechanical properties must be compatible with those of bones to avoid stress shielding and ensure proper load transfer [1]. Within the class of polymers that are potential candidates for biomedical use, polyetheretherketone (PEEK) has emerged as one of the most promising materials due to its resistance to degradation, biocompatibility, and human bone-like elastic modulus [2]. These characteristics make PEEK particularly suitable for dental implants and load-bearing implants such as orthopedic and spinal prostheses.
The machining of PEEK can provide high dimensional accuracy and customization for small batches or patient-specific parts, offering an alternative to injection molding. However, PEEK's low thermal conductivity and very ductile behavior make its machining challenging [3]. The accumulation of heat in the cutting zone can lead to thermal degradation, while its ductility promotes the formation of long, continuous chips that may entangle around the tool and damage the machined surface. Moreover, the use of conventional cutting fluids is not suitable for biomedical manufacturing because of contamination risks associated with flood coolants, which require time-consuming and costly cleaning steps to remove residues prior to sterilization.

To overcome these limitations, cryogenic cooling has been investigated as a sustainable strategy to improve polymer machinability [4]. The use of liquid nitrogen ( LN2 ) has been shown to reduce cutting temperature and suppress thermal softening, inducing a brittle response in PEEK [5]. Due to the different behavior of the material under cryogenic temperatures, the main effect of the LN2 was to obtain the chip breakage, transitioning from continuous to discontinuous chip formation.
Meanwhile, the use of innovative cutting tools has shown promising results in metal machining, where surface textures on the tool rake face can alter tool-chip contact, reduce friction, and improve chip flow. Surface texturing is a promising new technique for reducing or eliminating the need for cutting fluids [6]. As reported in [7], tool surface texturing can also affect chip formation mechanisms, promoting smoother chip flow and modifying chip morphology. In particular, grooves on the rake face have been shown to alter chip segmentation, leading to thinner, more regularly shaped chips than with conventional tools.
Similar benefits can be achieved in polymer machining, as reported in previous work by the authors [8]. It was demonstrated that texturing the rake face of the cutting tool improved chip fragmentation in cryogenic machining of PEEK, while also influencing chip formation and chip flow direction in dry machining. This can be seen as an improvement in polymer machining, and investigating the influence of texture design can enhance the effectiveness of the tool texture. However, the specific influence of texture design parameters on chip segmentation and flow under both dry and cryogenic conditions has not been systematically assessed. In fact, the efficacy of textured cutting tools depends on the texture design. Parameters such as texture width, spacing, depth, shape, orientation, and distance from the cutting edge must be optimized to achieve the intended benefits, such as reducing chip-tool contact, improving chip control, and enhancing heat dissipation [9].
In this framework, the present study aims to investigate further the influence of texture configuration on chip formation in PEEK machining, focusing specifically on the distance of the texture from the cutting edge and the groove depth. Turning trials were conducted under both dry and cryogenic conditions to identify the most effective design for promoting chip breakage.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

Workpiece material. The material investigated in this study is a biomedical-grade PEEK, supplied by Ensinger Plastics with the commercial name of TECAPEEK TM . Cylindrical bars of 42 mm diameter and 1000 mm length were provided in the annealed state, obtained by heat treatment at 221∘C for four hours to ensure thermal stability and minimize residual stresses. The thermal properties of PEEK were characterized using a DSC TM 200 system, with a controlled heating rate of 10∘C/min from 40∘C to 360∘C. The main thermal properties obtained were a glass transition temperature of 154∘C and a melting temperature of 344∘C. The degree of crystallinity was 24%.
Tool texturing. Tungaloy™ VNMA 160402-330.5 TH10 uncoated turning inserts with a 0∘ rake angle were used for the turning trials. Laser texturing of the rake face was performed with a Trumpf TM TruMark 5050 nanosecond-pulsed laser system, using consistent parameters: mean power of 50 W , a scanning speed of 100 mm/s, a frequency of 6250 Hz , and a pulse duration of 250 ns . Based on previous results from the authors [8], which showed better performance for textures oriented perpendicular to the cutting edge, a constant 90∘ orientation with respect to the cutting edge was adopted. The main investigated parameter (see Fig. 1) was the distance of the texture from the cutting edge (D), which was varied to 70μ m,100μ m, and 130μ m. Furthermore, two texture depths were investigated: 10μ m and 40μ m, obtained respectively with two ( 2 p ) and eight ( 8 p ) laser passes using a single track strategy. The selected groove depths provided two controlled levels of rake-face modification to compare their effects on chip formation without weakening the cutting edge. While a constant groove pitch (P) of 100μ m and a nominal groove width ( Gw ) and depth ( Gd ) of 40μ m were maintained. All the texture parameters are reported in Table 1.

[picture]

Fig.1. Textured tools. (a) 3D surface topographies of a tool showing the distance of the texture from the cutting edge and the pitch between grooves. (b) Texture profile (along the line in Fig. 1a) highlighting the groove width and depth.

Fig.1. Textured tools. (a) 3D surface topographies of a tool showing the distance of the texture from the cutting edge and the pitch between grooves. (b) Texture profile (along the line in Fig. 1a) highlighting the groove width and depth.

Table 1. Tools nomenclature.

Tool design | Untextured | 40-130 | 40-100 | 40-70 | 10-130 | 10-100 | 10-70
D (μm) | - | 130 | 100 | 70 | 130 | 100 | 70
P (μm) | - | 100 | 100 | 100 | 100 | 100 | 100
Gw (μm) | - | 40 | 40 | 40 | 40 | 40 | 40
Gd (μm) | - | 40 | 40 | 40 | 10 | 10 | 10

Turning experiments and post-machining analysis. The turning trials were conducted on a Mori Seiki TM NL 1500 CNC lathe using the textured inserts. Two machining environments were investigated: dry, the standard practice in machining polymers in biomedical applications, and cryogenic cooling, a sustainable alternative that enhances surface integrity while maintaining a contaminant-free surface. During cryogenic machining, liquid nitrogen ( LN2 ) was supplied from a 15 bar pressurized dewar and delivered to the cutting zone through two custom-designed copper nozzles, directed toward the tool rake and flank faces (see Fig. 2a and 2b).

[picture]

Fig. 2. (a) Set-up for the cryogenic cooling turning trials. (b) Customised nozzles for cryogenic delivery. (c) Tool-chip contact length and width measurements. (d) Thermocouple housing holes in the back of the insert.

Fig. 2. (a) Set-up for the cryogenic cooling turning trials. (b) Customised nozzles for cryogenic delivery. (c) Tool-chip contact length and width measurements. (d) Thermocouple housing holes in the back of the insert.

The machining parameters shown in Table 2 were selected based on a previous study by the authors [10] to ensure a sufficiently high surface finish.

Table 2 . Machining parameters.

Cutting speed (m/min) | 200
Depth of cut (mm) | 0.5
Feed (mm/rev) | 0.1
Sample length (mm) | 5

To evaluate the tool-chip contact area, additional cutting tests were carried out under dry conditions, using the machining parameters listed in Table 2, but with a reduced cutting length of 0.5 mm . Before
machining, each insert was pre-coated with a blue oil-based paint. The cutting action caused the coating to be removed in the contact area between the chip and the tool rake face, enabling measurements of the tool-chip contact length ( λ ) and width (c) imprints using a Keyence TM  VHXS770E/S750E optical microscope (see Fig. 2c).
After each trial, the produced chips were collected and analyzed using the optical microscope and a scanning electron microscope (SEM) FEI TM QUANTA 450. The analysis focused on overall morphology, specifically chip fragmentation and chip dimensions as width (W) and length (L), where applicable.
The cutting force, defined as the main tangential force component measured along the cutting speed direction, was measured using a Kistler TM type 9129AA three-component piezoelectric dynamometer and recorded with Dynoware TM  software at a sampling rate of 2500 Hz . The signals were processed using a 12 Hz low-pass filter to obtain mean force values and standard deviations. To prevent cooling damage, a glass fibre reinforced thermoset polymer panel was used as an insulating layer between the tool holder and the dynamometer. To monitor temperature during machining, two K-type thermocouples, each 0.4 mm in diameter, were embedded in blind EDM-drilled holes near the cutting edge. The holes were spaced 0.75 mm apart and positioned 0.4 mm from the cutting edge, reaching approximately 0.2 mm from the rake face to ensure proximity to the cutting zone (see Fig. 2d). Temperature signals were acquired using a National Instruments TM NI 9211 module connected to a Compact-RIO 9035 controller and monitored in real time through the LabVIEW TM software at a sampling interval of 0.05 s .

Experimental Results

The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

Chip morphology. The morphology of the produced chips depended on both the cooling strategy and the tool texture design. Two main chip types were identified: long, continuous chips, which tended to wrap around the workpiece or the tool, and short, discontinuous chips obtained under cryogenic conditions, regardless of the texture design. These results are consistent with previous findings and are due to low temperatures and high deformation rates, which suppress PEEK's viscoelastic response, promoting a stiffer and more brittle material behavior that facilitates chip breakage [11]. Although cryogenic cooling was the primary factor determining chip breakage, the introduction of surface textures also influenced chip formation in both machining environments, altering chip morphology and contributing to measurable changes in chip geometry.
The SEM images in Fig. 3 confirmed these differences. Under dry cutting, the chips exhibited a smooth, continuous surface with no visible signs of fracture. On the contrary, cryogenic chips, especially those produced with textured tools, exhibited more irregular surfaces. Even if the microscopic appearance of chips produced with the different textured tools was similar, specific features associated with the texture were visible. Texture marks (TM), which are imprints corresponding to the tool grooves, were visible on the back surface of chips produced in both cutting conditions, becoming more evident for deeper grooves and texture closer to the cutting edge. Serrations (S), which are fractures on the chip, appear on the free surfaces of cryogenic chips only when using a textured tool, indicating a local interaction between the chip and the groove that facilitated fragmentation.
The quantitative analysis of the chip length (L, in Fig. 3, available only for cryogenic chips) confirmed that tool texture reduced chip length compared to the untextured geometry. For the deepest texture, chip length decreased by 68%,74%, and 85% for texture distances of 130μ m,100μ m, and 70μ m from the cutting edge, respectively. The shallowest texture showed a similar trend, although with less pronounced reductions of 63%,67%, and 71%.

[picture]

Fig. 3. Chip morphology under different cooling conditions using untextured and the deepest textured tools. Chip width (W) and chip length (L) are reported when applicable.

Fig. 3. Chip morphology under different cooling conditions using untextured and the deepest textured tools. Chip width (W) and chip length (L) are reported when applicable.

Another parameter showing a similar trend is the chip width ( W , in Fig. 3). In both machining environments, the introduction of textures on the tool reduced the chip width compared to the untextured geometry. In dry machining, the deepest texture reduced the chip width by 15%,21% and 24% decreasing the distance of the texture from the cutting edge. The shallowest followed the same trend, but with small reductions of 16%,18%, and 19%. Under cryogenic cooling, this effect was amplified, reaching 19%,32%, and 38% for the deepest texture compared to the untextured tool. The same also for the shallower textures, which reached 17%,28%, and 29%.
Overall, these results show that the combination of deeper grooves and shorter distances from the cutting edge promotes effective chip control. Deeper grooves enhance chip-tool interaction and favor fragmentation, particularly when located closer to the cutting edge. This aligns with previous findings [12], which reported that when the textured zone is positioned nearer the cutting edge, the chip engages the micro-grooves earlier in its formation, promoting smoother chip flow. Moreover, as discussed in [13], deeper textures can promote the formation of micro air gaps between the chip and the tool surface, decreasing adhesion and altering the chip flow, whereas shallower textures maintain more continuous contact and have a reduced influence on chip segmentation. At the same time, the reduction in chip width can be associated with increased chip curvature, which, under cryogenic conditions, contributes to chip fragmentation. Although the chips produced in dry cutting remained continuous, the decrease in width suggests that the textured surface still influenced chip flow, even if insufficiently to promote complete fracture.

Chip-tool contact analysis. The results of the tool-chip contact area tests are summarized in Table 3, reporting the measured contact length and width under dry cutting conditions. In general, tool textures reduced the overall contact area compared to the untextured geometry. For the deepest texture, the contact length decreased progressively with decreasing the distance from the cutting edge, resulting in a reduction of about 26% for the texture closest to the cutting edge compared to the untextured insert. A similar trend was observed for the contact width, which decreased by 24% when using the tool texture with the lowest distance from the cutting edge. The shallowest texture produced smaller variations, with average reductions of about 18% and 10% in contact length and width, respectively, compared to the untextured tool.

Table 3. Chip-tool contact length and width.

Tool design | Contact length (μm) | Contact width (μm)
Untextured | 276 ± 19 | 660 ± 24
40-130 | 261 ± 5 | 657 ± 12
40-100 | 225 ± 12 | 551 ± 15
40-70 | 205 ± 6 | 502 ± 10
10-130 | 228 ± 8 | 632 ± 6
10-100 | 226 ± 14 | 599 ± 17
10-70 | 224 ± 5 | 596 ± 12

This reduction in contact length when using textured tools is consistent with the shorter chip lengths and narrower widths observed, suggesting that an optimized texture geometry can effectively control tool-chip contact. Deeper textures positioned closer to the cutting edge reduce the effective contact area, likely because chips engage the micro-grooves earlier. This behavior is in line with previous findings [14], where a shorter chip-tool contact length was shown to reduce plastic deformation to a narrower region, leading to thinner and more easily segmented chips, thus improving chip breakability.

[picture]

Fig. 4. Mean cutting force and relative standard deviation under dry and cryogenic machining with the various tool designs.

Fig. 4. Mean cutting force and relative standard deviation under dry and cryogenic machining with the various tool designs.

Force and temperature results. Fig. 4 presents the mean and standard deviation of the cutting force measured under dry and cryogenic machining with the various tool designs. As expected, the cryogenic condition resulted in higher cutting forces compared to dry cutting, with an average increase of approximately 35%. This increase aligns with the literature [15], which reports that PEEK becomes stiffer and more resistant to plastic deformation at cryogenic temperatures, requiring the tool to apply greater force to shear the material and, consequently, increasing the cutting force.
The influence of texture design on the cutting force was relatively limited. Compared to the untextured tool, the maximum variation across all textures remained within 10%. No clear correlation with either the texture depth or the distance from the cutting edge was observed. This limited variation is because the adhesive tool-chip contact zone, where the highest mechanical stresses develop, occupies approximately half of the total contact length [16]. Even when the texture is positioned close to the cutting edge, a significant portion of chip formation still occurs within this adhesive region, whereas the grooves primarily interact with the sliding portion of the contact, where forces are lower [17]. Despite the minimal changes in force magnitude, the use of textured tools generally reduced the variability of the cutting force signals, indicating a more stable cutting process. This trend is consistent with previous findings [18], which showed that textures interrupt continuous tool-chip contact, reducing the effective contact area and thus the friction coefficient and promoting chip segmentation, thereby producing a more stable cutting.
Fig. 5 presents the evolution and the average temperatures recorded by the two thermocouples (TC1, nearer to the cutting edge, and TC2, farther away) embedded in the insert. In dry machining, both sensors registered a temperature rise after the tool engagement, followed by a steady plateau (see Fig. 5a). As expected, TC1 measured higher values than TC2, confirming the presence of a thermal gradient across the insert.
Under dry conditions, for the untextured tool, the maximum temperatures measured by TC1 and TC2 were 53.0±1.3∘C and 46.1±2.6∘C, respectively (see Fig. 2b). The introduction of surface textures slightly lowered these values and reduced the temperature difference between the two thermocouples, suggesting a more uniform heat distribution along the rake face. This effect was most pronounced for the deepest texture positioned 70μ m from the cutting edge, where the temperature gradient between TC1 and TC2 decreased from about 7∘C to 1∘C. The shallowest textures produced smaller variations. Similar findings were reported in [19], where textured tools were shown to enhance heat transfer from the cutting zone by creating cavities on the rake face, promoting more efficient heat removal at the interface.

[picture]

Fig. 5. Temperature evolution during cutting in (a) dry and (c) cryogenic machining for the insert 40-100. Mean temperatures and relative standard deviations under (b) dry and (d) cryogenic machining with the various tool designs.

Fig. 5. Temperature evolution during cutting in (a) dry and (c) cryogenic machining for the insert 40-100. Mean temperatures and relative standard deviations under (b) dry and (d) cryogenic machining with the various tool designs.

As visible in Fig. 5c under cryogenic conditions, both thermocouples recorded nearly constant temperatures during cutting, with average values around −105∘C, confirming the dominant cooling effect. Variations under texture configurations remained below 10%, indicating that the influence of tool geometry on thermal behavior is negligible under liquid nitrogen cooling (see Fig. 5d).
Correlation between chip formation and contact conditions. The relationship between chip length and chip-tool contact length is shown in Fig. 6, which compares these parameters across the various tool designs. A general decreasing trend is observed as the texture is positioned closer to the cutting edge in both chip length and contact length, indicating that a shorter contact promotes earlier chip detachment and fragmentation.
With the deepest texture, reducing the distance from the cutting edge from 130μ m to 70μ m decreased the contact length by 20% and accordingly the chip length by 43%. The shallowest texture followed the same trend, though less pronounced, with average decreases of about 4% and 16%, respectively. Despite being obtained under different cutting conditions (dry for contact length and cryogenic for chip length), the two values show a consistent trend, with a correlation coefficient of R=0.82.
These results can be supported by previous studies [20], which reported that both dry and cryogenic turning exhibited systematic variation in the tool-chip contact length with cutting parameters, with cryogenic cooling producing overall shorter contact lengths while maintaining the same trend. A similar behavior is expected when varying the texture geometry; although the absolute values differ, the same relationship between chip-tool contact reduction is observed.

[picture]

Fig. 6. Correlation between chip-tool contact length measured under dry cutting and chip length measured under cryogenic cutting.

Fig. 6. Correlation between chip-tool contact length measured under dry cutting and chip length measured under cryogenic cutting.

Conclusions

The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

This work investigated the influence of tool texture design on chip formation mechanisms, contact conditions, and thermal and mechanical responses during PEEK turning in dry and cryogenic conditions. Textured tools with different groove depths and distances from the cutting edge were compared. Based on the experimental analysis, the following main conclusions can be drawn: Textured tools modified the chip morphology under both dry and cryogenic conditions. Furthermore, in cryogenic cooling, when the texture was positioned closer to the cutting edge, shorter chips were produced.

The chip-tool contact length and width decreased with increasing texture depth and decreasing distance from the cutting edge, confirming the influence of texture geometry on chip formation.

Cutting forces were not significantly affected by the texture design, although textured tools exhibited lower force variability, indicating a more stable cutting process.

Tool textures reduced the temperature gradient within the insert under dry cutting, while no significant differences were observed under cryogenic cooling.
Overall, deeper textures positioned closer to the cutting edge were the most effective configuration. In dry machining, improving chip flow, while in cryogenic machining, promoting chip breakage while keeping the cutting forces unchanged. This confirms that an optimized texture geometry can enhance chip control in both environments.

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The original version of this paper is available on https://www.scientific.net/DDF.450.9.pdf

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 19-30

doi: 10.4028/p-xi5kxU

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-22

Revised:
2026-02-23

Accepted:
2026-02-24

Online:
2026-04-15

Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

Stefania Ferrisi 1,a*, Sebastian Lang 2, b, Mohamadreza Afrasiabi 2,c, Rosita Guido 1, d, Domenico Umbrello 1,e, Giuseppina Ambrogio 1,f and Markus Bambach 2, g

¹Department of Mechanical, Energetic and Management Engineering, University of Calabria, Ponte P. Bucci, Rende, 87036, CS, Italy

2 Advanced Manufacturing Lab, ETH Zurich, Zurich, Switzerland

a stefania.ferrisi@unical.it, b selang@ethz.ch, c afrasiabi@ethz.ch, d rosita.guido@unical.it, e domenico.umbrello@unical.it, f giuseppina.ambrogio@unical.it, 9 mbambach@ethz.ch

*corresponding author

Keywords: milling process, remaining useful life, tool wear, deep learning.

Abstract

Accurate prediction of tool Remaining Useful Life (RUL) is essential for reliable and costeffective milling, particularly when machining commercially pure titanium (CP-Ti), where tool wear is highly irregular. In industrial practice, continuously varying cutting conditions further complicate tool condition monitoring and life prediction. This paper proposes a vibration-based monitoring framework for RUL prediction under strongly variable milling conditions. A hybrid deep learning model based on CNN-BiLSTM is developed to capture the non-stationary relationship between vibration signals and tool degradation. Performance is compared between a spindle-mounted, noninvasive sensor and a tri-axial accelerometer mounted on the machine table, and the benefit of sensor fusion is assessed. Results show that spindle vibration correlates strongly with tool degradation and achieves predictive performance close to that of multi-sensor configurations, while requiring minimal instrumentation. The proposed approach remains robust under variations in both operating conditions and wear mechanisms, enabling reliable RUL estimation in non-stationary milling environments.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

Commercially pure Titanium (CP-Ti) is the ideal titanium-based material for medical applications due to its excellent properties in terms of biocompatibility and corrosion resistance. It is widely used for dental and bone implants [1]. Due to the unique attributes of the CP-Ti, such as light weight, hypoallergenicity, and durability, it is also used to make products primarily used in everyday life, including titanium bottles, thermos, kitchen utensils, tea sets, sporting goods, etc [2]. In contrast, several studies have demonstrated that the use of titanium alloy (especially the most used Ti-6Al-4V) might cause some long-term health problems for the patient due to the release of aluminum and vanadium inside the human body [3].

The machinability of titanium presents several challenges, such as its low thermal conductivity, adhesion to the cutting tool, and the significant impact of machining parameters. Titanium is considered one of the hardest-to-machine materials for milling. The chips formed during machining are highly flammable and require adequate safety precautions. For this reason, low cutting speeds, heavy feed rates, and the usage of cutting fluid are recommended for its machining. Titanium is a poor heat conductor, unable to dissipate heat quickly, concentrating it on the cutting edge and surface of the tool. Consequently, the cutting tool undergoes intense wear. Different wear mechanisms may be observed during the milling of CP-Ti material, including adhesive wear, diffusion wear, abrasive wear, and thermal cracking. During adhesive wear, a rapid build-up of agglomerated workpiece material adheres to the cutting edge of the tool [4], thereby generating the so-called built-up edge (BUE) effect. This type of wear changes the shape of the cutting tool, altering its size and preventing efficient chip formation during machining. When working under such conditions, the tool can suffer catastrophic wear, resulting in the breakage of one or more teeth. To avoid these effects, cooling
methods are usually employed [5], although they come with drawbacks such as coolant contamination, increased cost and energy consumption, environmental concerns, and health risks associated with coolant residue [6]. However, Assis et al. [7] demonstrated that dry micro-cutting is feasible at adequate feed rates. Finally, changes in chip formation can increase cutting forces during machining, generate excited vibrations, and increase the temperature of the process, exerting a microfatigue loading on the cutting tool and decreasing the tool life [8]. As a result of the complex wear mechanisms and the strong dependence of tool degradation on cutting parameters, the accurate monitoring and prediction of tool wear during CP-Ti machining becomes essential to ensure surface quality, dimensional accuracy, and process sustainability. Khan et al. [9] employed an RSM-based quadratic model for the prediction of flank tool wear, obtaining lower prediction error. Nevertheless, as emerged from the analysis of tool wear progression, several tool degradation effects were exhibited (i.e., BUE, chipping, and notch wear).

With the introduction of smart machines in the industrial context, new approaches to tool wear monitoring primarily rely on indirect signal analyses (e.g., cutting forces, acoustic emissions, vibrations, temperatures). These signals can be acquired with the use of Industrial Internet of Things (IIoT), enabling the collection of sensor data useful for the construction of condition-based monitoring systems. By constructing a predictive model for the end-of-life of industrial assets, manufacturers can plan the budget for replacement or repair, reduce maintenance costs, and maximize resource allocation [10]. The extraction of non-stationary and nonlinear signal characteristics from acquired signals enables the construction of accurate prediction models for tool life. The phase of signal processing for the extraction of meaningful information from sensor data plays a critical role in tool condition monitoring (TCM) [11]. Representative features may be extracted or constructed during the signal processing stage as a compact and informative representation for the monitored variables [12]. Assafo et al. [13] analyzed three cutting force signals and extracted time-domain and multiscale features for the prediction of the remaining useful life (RUL) of cutting tools for a milling process. Guo et al. [14] created a time-frequency feature dataset from a multi-sensor signal acquisition system that consists of seven sensors (including acoustic emission sensor, microphone, and accelerometers). Chen et al. [15] proposed an innovative predictive model for RUL based on a data fusion process through a Graph Neural Network combined with a Transformer.

However, these methods encounter considerable difficulties in real-time implementation due to the high cost and invasiveness of the sensors [16], and in defining a robust model under variable working conditions [17]. The ability of a TCM system mainly relies on the number and type of sensors adopted to monitor the process. This, in turn, involves expensive hardware, which influences the cost of the system [18]. Additionally, to satisfy the industrial environment requirements, there is a need to train the predictive model under a different set of cutting conditions [12].

Despite the significant progress achieved in recent years, the literature highlights that most TCM and RUL prediction strategies still struggle to maintain accuracy and reliability when machining conditions vary. This difficulty becomes even more pronounced in the case of CP-Ti, where the progression of tool wear is highly irregular and difficult to model due to the complex tribological behaviour of this material and the strong influence of cutting parameters on chip formation, adhesion, and thermal load on the cutting edge. To address these limitations, the present work proposes a robust data-driven framework for the prediction of RUL of milling cutting tools during CP-Ti machining, explicitly designed to remain stable under a broad range of machining parameters and under the intrinsic wear variability of this material. An extensive experimental campaign was carried out to support the development and validation of the proposed approach. Vibrational data were collected using two complementary sensing systems: (i) a smart tool holder equipped with an embedded radial acceleration sensor capable of wireless data transmission to a tablet device, providing a non-invasive solution, and (ii) a tri-axial accelerometer mounted on the machine table, which offers a full threeaxis acquisition but introduces a more invasive installation due to the need for cabling and fixturing. The experimental plan includes variations in machining parameters both between different new tools and dynamically applied to the same tool across successive passes, enabling the construction of a heterogeneous dataset that captures diverse and highly irregular degradation patterns typical of CP-

Ti machining. Based on the acquired dataset, prediction models were trained and evaluated to identify the most effective strategy for real-time RUL estimation. The models were first assessed using the signals from each sensor independently, allowing the standalone predictive capability of the smart spindle and the table-mounted accelerometer to be compared. A further evaluation was then conducted by combining the signals from both sensing systems to understand whether sensor fusion could enhance robustness and accuracy. This comparative analysis provides insights into the specific contribution of each sensor placement and highlights the potential benefits of leveraging heterogeneous vibration sources for reliable tool-life prediction.

The remainder of the paper is organized as follows. Section Experimental Setup describes materials, tools, and sensors used for the experimental campaign and the strategy adopted for the setting of variable cutting conditions. Section Methodology provides background about the algorithms adopted in this study. The results obtained are analyzed and compared in Section Results and Discussion, where the performance of the different approaches is critically assessed. Finally, Section Conclusions and Future Work summarizes the main findings of the study and outlines potential directions for future research.

Experimental Setup

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

An experimental campaign was conducted over a period of three months with the objective of acquiring a heterogeneous dataset with varying cutting conditions. The DMG Mori NMV 5000 DCG, a 5 -axis Machine Tool (MT), was employed for the milling operations on a CP-Ti workpiece with dimensions 270 mm×270 mm×26 mm. Carbide end mills with a diameter of 10 mm and unequal helices were used. Table 1 provides the specifications of the machine tool, workpiece, and cutting tools adopted in this study.

Table 1. Specifications of the machine tool, workpiece, and cutting tools adopted for the experimentation.

Component | Specification | Value
Machine tool |  | DMG Mori NMV 5000 DCG Vertical milling machine
Travel dimension | 730 mm (X-axis), 510 mm (Y-axis), 510 mm (Z-axis)
Tool capacity | 31-tool automatic tool changer (ATC)
Spindle speed | Up to 1200 rpm
CNC control | FANUC 31iA5
Milling cutter |  | Carbide end mill
Helix angles | A= 40°, B= 42°
Cutting diameter | 10 mm
Number of cutting edges | 4
Bar material | tungsten carbide
Hardness | HRC72
Workpiece | Coating | CVD TiCN+Al2O3+TiN
| CP-Ti plate
Size | 270 x 270 x 26 mm

The MT was equipped with three sensors for the acquisition of signals and inspection of the cutting tools' condition. In detail, two indirect sensors were installed for the acquisition of real-time vibration signals during milling. The first device is a tri-axial accelerometer (model 356B18 by PCB Piezotronics) installed on the worktable, near the workpiece, enabling the acquisition of vibrations transmitted through the workpiece-machine structure. Data were sampled at a rate of 2048 Hz and collected using a National Instruments NI 9234 Data Acquisition System (DAQ). The second device is the REGO-FIX ToolVibe, a sensory tool holder capable of measuring the radial acceleration of the tool. The data were sampled at 15385 Hz . ToolVibe is a wireless sensor that transmits data to a tablet on a frequency band of 2.4 GHz .

[picture]

Fig. 1. Sensor configuration adopted during the experimentation.

Fig. 1. Sensor configuration adopted during the experimentation.

Unlike the invasive tri-axial accelerometer, which requires wired connections for signal transmission, the ToolVibe system is a non-invasive device, making it more suitable for industrial environments. The third sensor is a direct sensor adopted for the monitoring of the cutting tools' condition after each milling pass. A Dino-Lite AM8917MZTL microscope was installed inside the MT on the right hand side, using a bracket fastening it to the inner wall of the machine with six screws. For protection, the microscope was housed in an Autovimation Salamander M protective casing, while the lens was protected from coolant by a pneumatically actuated cap. The microscope has a resolution of up to 8.0 Megapixels and a magnification set to 140 x . The DNX64 Python module was used for controlling it. Finally, a nozzle was installed near the microscope to clear the cutting tool from residual lubricants and material chips before each image acquisition. This nozzle is controlled by an Arduino that controls two solenoid valves connected to the nozzle and the actuators for the microscope cap. A single Python script was used to coordinate machine movements using the FOCAS2 interface as well as data collection and imaging. At the end of each milling pass, the cutting tool reaches the nozzle for cleaning. Then the microscope cap is opened, and the cutting tool reaches the right position for capturing images of the edge, rotating to obtain the four edge images. Fig. 1 shows the sensors used during the experimental campaign. Five cutting tools were consumed during the milling process to acquire data, with varying cutting parameter settings. Table 2 illustrates the combination of parameters adopted for each experiment. In addition, a sixth cutting tool was employed in a series of milling operations where the cutting parameters were varied to evaluate the predictive performance of the model under variable conditions. The cutting parameters for these variable experiments were selected using a Sobol quasi-random search [19] within predefined ranges (cutting speed between 25 and 80 m/min, feed between 0.05 and 0.115 mm/ tooth, axial depth of cut between 0.6 and 3 mm , radial depth of cut between 0.1 and 5 mm ). The experiments were executed in blocks, with each block defined by a fixed radial depth of cut maintained constant until the full depth of the workpiece was machined. After completing a block, a new radial depth of cut was selected for the subsequent block. For each block, the presence of cutting oil was assigned randomly as either yes or no. This approach allowed for the systematic variation of process parameters while ensuring that any potential residual oil on the workpiece could be cleaned between blocks, minimizing cross-contamination and maintaining consistent surface and tool conditions throughout the experiments.

Table 2. Combination of cutting parameters adopted.

Cutting tool | Cutting speed (V) [m/min] | Feed (f) [mm/tooth] | Axial depth of cut (ap) [mm] | Radial depth of cut (ae) [mm] | Oil [Yes/No]
1 | 65 | 0.095 | 3 | 2.4 | No
2 | 40 | 0.095 | 2 | 2.4 | Yes
3 | 40 | 0.095 | 2 | 2.4 | No
4 | 65 | 0.115 | 2 | 1.5 | Yes
5 | 80 | 0.095 | 4 | 1.5 | Yes

During milling operations, the cutting edges exhibited several tool wear phenomena that affected the performance of the process. Fig. 2 shows some examples of tool wear, measured on the lateral cutting edges of the end mills. In addition to the expected increase in flank wear, a pronounced BUE formation was frequently observed. The presence of BUE significantly altered the cutting mechanics, leading to unstable chip formation, irregular force variations, and a local increase in thermal and mechanical loading on the cutting edge, making BUE a critical factor in the overall process quality. Therefore, the affected tools experienced an accelerated degradation process, often culminating in catastrophic wear, which caused pronounced deterioration of the surface quality of the machined workpiece.

[picture]

Fig. 2. Tool wear types: a) Flank wear and fatigue wear, b) Built-up edge and edge chipping/fracture.

Fig. 2. Tool wear types: a) Flank wear and fatigue wear, b) Built-up edge and edge chipping/fracture.

The evolution of tool wear and the corresponding surface integrity of the machined workpiece were continuously monitored throughout the experiments, and the end of tool life was identified for each tool. During the experiments, surface defects such as tearing or material adhesion caused by BUE were systematically assessed through visual inspection, while flank wear was quantitatively measured at the end of each milling operation from the acquired images. The end of tool life was defined either when the flank wear reached the ISO 8688-2-defined limit or when surface quality was compromised due to BUE. These measurements provided the basis for defining the RUL variable, which quantifies the residual time before the tool reaches its predefined wear limit and must be replaced to ensure safe and stable machining operations.

Fig. 3a shows the evolution of the RUL, expressed as a percentage of cumulative cutting time, for each tool, highlighting pronounced variability among them. This discrepancy is largely attributed to differences in cutting conditions, such as the presence or absence of oil. Specifically, as shown in Fig. 3b, tools operating without oil generally exhibit a significantly reduced maximum cutting time, highlighting the strong influence of lubrication on tool performance and degradation.

Methodology

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

This section provides a background of the AI-based techniques adopted for the construction of a predictive model for RUL under variable cutting conditions. Vibration signals were cleaned and preprocessed to obtain the input dataset for the predictive models. Several deep learning algorithms and architectures were implemented, evaluated, and compared to identify the most effective predictive model.

Since the experiments involved different combinations of cutting parameters, the acquired vibration signals exhibit variable lengths. In addition, the two accelerometers employed in the setup operate at different sampling rates, further increasing the heterogeneity of the raw signals. Such discrepancies in signal duration and sampling characteristics make direct use of the full-length time series impractical for machine learning models, which typically require fixed-size inputs. To address this issue and enable consistent data representation across all operating conditions, a sliding-window segmentation strategy was adopted. Each vibration signal was partitioned into overlapping windows of fixed length, ensuring that every sample fed to the predictive model maintains identical dimensionality while preserving the temporal structure of the original measurements.

[picture]

Fig. 3. Cutting tool degradation trend within the dataset. a) Temporal distribution of RUL across cutting tools and b) influence of lubrification on tool degradation.

Fig. 3. Cutting tool degradation trend within the dataset. a) Temporal distribution of RUL across cutting tools and b) influence of lubrification on tool degradation.

Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) were employed for the construction of the predictive model. LSTM network is a recurrent neural network (RNN)-based architecture designed to overcome key limitations of conventional RNNs, particularly in modeling long-range temporal dependencies. [20]. In standard RNNs, the model relies on a shortterm memory, which corresponds to a rapidly decaying internal state updated at each time step. Because this memory is overwritten quickly, the network struggles to retain relevant information across longer temporal spans and requires extensive training to learn which information should be stored and for how long. This often results in vanishing gradient problems, inefficient learning, and degraded performance when long-range temporal dependencies are involved. LSTMs address these issues by introducing a more sophisticated memory mechanism, composed of explicit gating units that regulate the flow of information. These gates enable the network to preserve important features over extended sequences, selectively forget irrelevant content, and update its internal state efficiently.

Originally defined for pattern recognition within images, CNNs are one of the most impressive forms of artificial neural networks [21]. Their architecture consists of three types of layers: convolutional, pooling, and fully-connected layers. Convolutional layers constitute the core of a CNN and are responsible for extracting local features from the input data by connecting each neuron to a small part of the input data. This operation generates a set of feature maps, each highlighting specific patterns learned by the corresponding convolutional filter. Pooling layers operate directly on these feature maps, performing a downsampling procedure that reduces their spatial resolution and
consequently, the computational complexity while preserving the most salient information. Finally, fully-connected layers integrate the high-level features extracted by previous layers and combine them to produce the final output. Although CNNs were initially developed for computer vision tasks, their ability to learn spatially correlated structures has made them highly effective in a wide range of applications, including time-series analysis, signal processing, and industrial diagnostics [22].

In this work, a hybrid architecture combining the strengths of LSTM and CNN networks is proposed to construct an effective predictive model for RUL prediction. The performance of the hybrid LSTM-CNN model was assessed considering the three standard regression performance metrics listed in Table 3.

Table 3. Regression performance metrics.

Mean Absolute Error

(MAE)

Root Mean Squared Error

(RMSE)

R-squared superscript number

Results and Discussion

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

The vibration signals obtained during experimentation were used to develop a predictive model for estimating the RUL of the cutting tool when a CP-Ti plate is machined under variable cutting conditions. The preprocessing of the raw vibration signals and the development of the predictive model were carried out using the Python programming language and the PyTorch package [23]. From the acquired vibration data, the segments corresponding to the actual milling operation were first identified and isolated. Only the intervals in which the tool was effectively cutting the CPTi workpiece and completing a milling pass were retained, while idle or transient periods were removed.

[picture]

Fig. 4. Segment isolation from the spindle-vibration signal.

Fig. 4. Segment isolation from the spindle-vibration signal.

To achieve this, a combination of amplitude-based thresholds and temporal markers linked to the machine tool kinematics was applied. Fig. 4 shows an example of segment isolation from a vibration signal acquired by using the ToolVibe sensor.
A windowing strategy was implemented for the isolated segments to convert each variable-length signal into a structured set of fixed-length samples suitable for training the data-driven model. Specifically, each signal segment was divided into windows of 4096 samples with a step size of 2048
samples, corresponding to a 50% overlap. These windowed samples were subsequently fed into the neural architecture as input instances. Additionally, the available dataset was partitioned into three subsets for model training, validation, and testing, following a 70−15−15% split. The proposed neural architecture consists of a hybrid deep-learning architecture obtained by combining a bidirectional recurrent layer with a convolutional feature extractor, followed by a feature-fusion module. Fig. 5 illustrates the architecture of the developed predictive model. It was trained and tested on four input signal configurations: (a) radial spindle vibration, (b) three axes of the worktable independently, (c) three worktable axes jointly, and finally, (d) all available signals. This evaluation strategy allowed assessing the contribution of each signal source to the predictive performance of the model. The results obtained on the test set are reported in Table 4, with the best-performing configurations highlighted in bold.

[picture]

Fig. 5. Hybrid deep learning model architecture developed.

Fig. 5. Hybrid deep learning model architecture developed.

Table 4. Predictive results on the test set.

Input signals | MAE | RMSE | R²
Radial spindle vibration | 0.098 | 0.148 | 0.704
X-vibration worktable | 0.111 | 0.151 | 0.692
Y-vibration worktable | 0.084 | 0.123 | 0.798
Z-vibration worktable | 0.218 | 0.256 | 0.120
Three-axis vibration worktable | 0.065 | 0.093 | 0.883
All signals | 0.056 | 0.085 | 0.904

Among the single-sensor inputs, the Y -axis vibration of the worktable achieves the best performance, with lower MAE and RMSE values and a higher R2, immediately followed by the spindle-vibration signal. Although the Y-axis worktable vibration provides slightly better accuracy, the spindle-vibration signal represents a particularly attractive solution, as it is measured in the radial direction and can be acquired in a non-invasive manner, without requiring any modification or instrumentation of the worktable. This characteristic makes spindle vibration especially suitable for industrial applications, where ease of integration and minimal interference with the machining setup are critical. The best overall performance is obtained when all available signals are combined. This
highlights the benefit of sensor fusion, as the complementary information provided by different vibration sources improves the model's predictive capability and robustness.

To provide a more robust assessment of the model's generalization capability and to quantify performance variability, a 5-fold cross-validation procedure was additionally performed for each input configuration. For each fold, the model was trained from scratch, and the mean and standard deviation of MAE, RMSE, and R2 were computed. Furthermore, 95% confidence intervals (CI) were estimated assuming normality of the performance distribution across folds. Results are presented in Table 5.

Table 5. 5-fold cross-validation results (mean ± standard deviation and 95% confidence intervals).

Input signals | MAE (mean ± std) | 95% CI | RMSE (mean ± std) | 95% CI | R²(mean ± std) | 95% CI
Radial spindle vibration | 0.1186 ± 0.0117 | ± 0.0103 | 0.1753 ± 0.0109 | ± 0.0096 | 0.6311 ± 0.0460 | ± 0.0403
X-vibration worktable | 0.0966 ± 0.0407 | ± 0.0357 | 0.1327 ± 0.0471 | ± 0.0412 | 0.7707 ± 0.1726 | ± 0.1513
Y-vibration worktable | 0.1075 ± 0.0264 | ± 0.0231 | 0.1452 ± 0.0323 | ± 0.0283 | 0.7342 ± 0.1203 | ± 0.1055
Z-vibration worktable | 0.1393 ± 0.0661 | ± 0.0579 | 0.1765 ± 0.0696 | ± 0.0610 | 0.5791 ± 0.3031 | ± 0.2657
Three-axis vibration worktable | 0.0685 ± 0.0272 | ± 0.0238 | 0.0977 ± 0.0305 | ± 0.0267 | 0.8775 ± 0.0730 | ± 0.0640
All signals | 0.0585 ± 0.0290 | ± 0.0254 | 0.0814 ± 0.0330 | ± 0.0579 | 0.9072 ± 0.0856 | ± 0.0750

The cross-validation analysis confirms the general trends observed in the fixed test set, while providing additional insight into the stability of each configuration. The model exploiting all available signals achieves the best overall performance in terms of mean MAE, RMSE, and R2, thus confirming the benefit of sensor fusion. However, the associated standard deviations indicate a moderate variability across folds, suggesting a certain sensitivity to the specific data partitioning. Among the single-sensor configurations, the radial spindle-vibration signal exhibits comparatively low standard deviations for both MAE and RMSE, together with a limited dispersion in R2 values. This behavior highlights its robustness and consistent predictive capability across different training subsets, further reinforcing its suitability for industrial deployment where reliability is critical. On the contrary, a reduced generalization stability is observed when worktable vibration signals are considered.
Additionally, a sensitivity analysis was performed by varying the window size while keeping all other parameters fixed, in order to systematically assess its impact on the predictive performance and to identify the most suitable configuration. Specifically, different window lengths were evaluated while maintaining a constant overlap ratio and identical training settings, so that any performance variation could be directly attributed to the window-size selection. Fig. 6 reports the MAE, RMSE, and R2 values obtained on the test set for the considered window sizes by considering the radial spindle vibration and the Y -vibration worktable as input signals, respectively. For both signals, relatively low MAE and RMSE values and comparatively high R2 were obtained with smaller window sizes. As the window size increases, the model performance deteriorates significantly, with a marked increase in prediction errors and a substantial drop in R2. Based on these results, a window size of 4096 samples was selected for the final model configuration. This value ensures a balanced trade-off for both signal types, providing low MAE and RMSE together with a satisfactory R2. For smaller window sizes, performance remains comparable but with slightly higher errors, whereas larger windows lead to a clear and consistent degradation of predictive accuracy.

[picture]

Fig. 6. Predictive performance to the selected window size for the two considered single-sensor configurations: (a) radial spindle vibration and (b) Y-axis worktable vibration.

Fig. 6. Predictive performance to the selected window size for the two considered single-sensor configurations: (a) radial spindle vibration and (b) Y-axis worktable vibration.

Finally, an additional validation of the proposed model was carried out using the experiments performed with the sixth tool, under fully varying cutting conditions at each pass. This test represents a particularly challenging scenario, as none of the process parameters remain constant between consecutive passes. The results of this final validation are reported in Table 6.

Table 6. Predictive results under fully varying cutting conditions.

Input signals | MAE | RMSE | Mean Residual
Radial spindle vibration | 0.248 | 0.300 | 0.255
X-vibration worktable | 0.359 | 0.416 | 0.174
Y-vibration worktable | 0.538 | 0.583 | 0.549
Z-vibration worktable | 0.189 | 0.236 | 0.141
Three-axis vibration worktable | 0.358 | 0.426 | 0.216
All signals | 0.220 | 0.290 | 0.163

Even under these highly variable cutting conditions, the configuration exploiting all available signals still provides the best overall performance, achieving the lowest error metrics. However, it is worth noting that the model based solely on spindle vibration yields performance very close to that obtained with all signals combined, with comparable MAE and RMSE values, highlighting the high sensitivity of the spindle-vibration signal to process variations, even in strongly non-stationary conditions.

Conclusions and Future Work

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

This work investigated a vibration-based monitoring approach for milling tools under highly variable cutting conditions, with experimental validation carried out on CP-Ti, a material known for its challenging machinability. The main objective of the study was to identify and develop a predictive model based on a vibration signal capable of effectively capturing tool degradation and monitoring its RUL over time under variable cutting conditions. Accurate RUL prediction is of critical importance in industrial contexts, as it allows for optimized tool replacement strategies, reduced unplanned downtime, and improved process reliability, particularly when machining difficult-to-cut materials. Different vibration sources were analyzed and compared, including spindle and worktable sensors, in both single-sensor and multi-sensor configurations. The results showed that, while the combination of all available signals provides the highest overall accuracy, spindle vibration exhibits the best compromise between predictive performance and robustness under highly variable cutting conditions. Despite its non-invasive nature, this signal demonstrated a strong sensitivity to process
variations and delivered performance close to that of multi-sensor solutions, especially during the final validation on unseen tools and fully varying operating conditions.

Despite the encouraging results obtained in this study, the proposed approach has been validated on a limited number of cutting tools and workpiece materials, which represents an inherent limitation of the present work. Future research will focus on extending the proposed model to a wider range of variable operating conditions. In particular, the model will be validated using different tool types, characterized by varying materials and geometries, as well as across different workpiece materials. This extension will allow for a more comprehensive assessment of the model's generalization capability and robustness in heterogeneous industrial scenarios. Additionally, studies on uncertainty-aware RUL estimation will provide confidence bounds suitable for informed decisionmaking in industrial environments. Furthermore, optimised tool replacement strategies based on the predicted RUL will be developed by integrating RUL estimates into decision-making frameworks, with the aim of facilitating condition-based maintenance policies that optimise tool utilisation while minimising production downtime and associated financial expenditure.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/DDF.450.19.pdf

This work contributes to the basic research activities of the WP9.6: "AI for Green" supported by the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU. We thank Lennart Schinzel for his support with the experimental setup. We appreciate the Tool Vibe and support provided by REGOFIX AG.

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 31-39

doi: 10.4028/p-9AOMkv

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-22

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-15

Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

Rachele Bertolini 1,a*, Toushiqul Islam 2, b, Shuaihang Pan 2,c, Edoardo Ghinatti 1, d and Stefania Bruschi 1,e

1 Department of Industrial Engineering, University of Padova, Via Venezia, 1, 35131, Padova, Italy

2 Lab of Advanced Manufacturing (LoAM), Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA

a  rachele.bertolini@unipd.it, b  toushiqul.islam@utah.edu, c  Shuaihang.Pan@utah.edu, d edoardo.ghinatti@phd.unipd.it, e stefania.bruschi@unipd.it

Keywords: Laser powder bed fusion, aluminum alloy, machinability, surface integrity, EBSD, layer thickness.

Abstract

Additive manufacturing by laser powder bed fusion (LPBF) is increasingly applied to aluminium alloys; however, the resulting surface quality and machining behaviour remain critical challenges, particularly when post-processing is required. In this context, the interaction between LPBF process parameters and advanced cooling strategies during machining remains largely unexplored. This study examines the impact of cryogenic machining on the surface integrity of LPBF-produced AlSi7Mg components, fabricated with varying layer thicknesses. Specimens were machined under fixed cutting parameters using either conventional flood cooling or cryogenic cooling. Cutting forces, surface roughness, defect morphology, and subsurface microstructure were systematically evaluated. Cryogenic cooling consistently reduced cutting forces and improved surface quality, effectively suppressing tearing formation. In contrast, under flood cooling, the influence of the microstructural differences induced by layer thickness remained significant, with increasing LPBF layer thickness further enhancing both surface and subsurface integrity. Overall, the results reveal a strong interaction between LPBF parameters and cooling strategy, highlighting the unexpectedly beneficial role of cryogenic machining in improving the surface integrity of LPBF-processed AlSi7Mg alloys.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

Aluminum alloys offer attractive properties such as an excellent strength-to-weight ratio and good corrosion resistance. In recent years, additive manufacturing, particularly laser powder bed fusion (LPBF), has been increasingly adopted for the production of lightweight and geometrically complex aluminum alloy components due to its high design flexibility. Variations in LPBF process parameters can significantly influence the resulting microstructure, with layer thickness often adjusted to increase productivity and reduce manufacturing costs [1].

In [2], the effect of layer thickness on defect formation, microstructure, crystallographic texture, and mechanical properties of AlSi10Mg alloy fabricated by high-power laser powder bed fusion (HPLPBF) was investigated. By increasing the layer thickness from 0.05 to 0.25 mm , the authors reported a substantial increase in the build rate, accompanied by changes in densification behavior, grain morphology, and texture evolution. Thicker layers promoted a transition from columnar to more equiaxed grains and a reduction in texture intensity, while the tensile properties exhibited a nonmonotonic dependence on layer thickness, resulting from the combined effects of relative density and microstructural refinement.

Similar trends were observed in a previous study by the authors [3], which examined the influence of LPBF layer thickness on the microstructural evolution and mechanical behavior of AlSi7Mg aluminum alloy. In particular, increasing the layer thickness led to a transition in the morphology of Fe-rich intermetallic phases from acicular β-AlFeSi to globular π-AlFeMgSi. These microstructural changes resulted in higher strength and hardness but reduced ductility and toughness, highlighting
the key role of layer thickness in tailoring the balance between microstructure and mechanical performance in LPBF-processed aluminum alloys.

Despite advances in LPBF processing, machining remains an essential post-processing step for aluminum alloy parts, making it crucial to understand how LPBF parameters influence subsequent machining behavior. Among machining parameters, lubrication and cooling conditions have gained considerable attention due to their ability to control surface integrity, in addition to reducing friction and heat generation during cutting operations.

In [4], tool wear and surface integrity during milling of LPBF-produced AlSi 10 Mg alloy were investigated under dry, flood, minimum quantity lubrication (MQL), and cryogenic cooling conditions. In terms of surface roughness (Ra), CO2 cryogenic cooling outperformed dry and flood lubrication but resulted in higher Ra values than MQL. The improvement was primarily attributed to the reduction in cutting temperature; however, the associated temperature drop also promoted surface defect formation. The reduced performance of CO2 cooling was linked to increased workpiece brittleness at low temperatures, which facilitated chip fragmentation but adversely affected surface quality. Regarding subsurface microstructure, cryogenic machining produced more densely packed grains than the other cooling conditions, an effect attributed to rapid thermal cycling induced by the chilled gas, although limited mechanistic interpretation was provided.

High-speed machining tests conducted on AA7075-T6 aluminum alloy under dry and cryogenic conditions in [5] showed improved surface roughness under dry machining compared to cryogenic cooling. In addition, significant grain refinement was observed near the machined surface, defining an affected layer whose thickness decreased with depth from the machined surface. Under dry machining, this layer ranged from 97 to 142μ m and increased slightly with the feed rate, whereas cryogenic machining consistently resulted in a thinner affected layer. These findings were attributed to the reduced ductility of the alloy at low temperatures; under cryogenic cooling, the material exhibited a more brittle response, limiting its capacity for plastic deformation before fracture.

Overall, the literature indicates that the benefits of cryogenic cooling in machining aluminum alloys remain inconclusive. This uncertainty is even more pronounced for additively manufactured aluminum alloys, where, to the best of the authors' knowledge, only limited studies have been reported. In [3], improved machinability, evidenced by lower cutting forces, reduced force variability, smoother surface finish, and a thinner subsurface plastically deformed layer, was observed when machining LPBF AlSi7Mg samples produced with higher layer thickness. Similar results were found in [6]. In [7], the effect of hatch spacing in LPBF on the microstructure and machining response of AlSi7Mg aluminum alloy was investigated. Increasing hatch spacing promotes coarser grains, modifies melt pool geometry, and alters silicon precipitation behavior after heat treatment. While average cutting forces remain largely unaffected, larger hatch spacings reduce force variability and significantly improve surface finish.

Within this context, the present study investigates the effect of cryogenic machining on the surface integrity of LPBF-produced AlSi7Mg samples fabricated with different layer thicknesses. Cylindrical specimens were manufactured via LPBF and subsequently machined under fixed cutting conditions using either conventional flood cooling or cryogenic cooling. Surface integrity was evaluated in terms of surface roughness, surface defect morphology, and machining-induced microstructural alterations, while cutting forces were simultaneously recorded.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

LPBF and heat treatment of AlSi7Mg samples. Specimens were fabricated using AlSi7Mg powder on a SISMA MYSINT100 TM LPBF machine. The feedstock powder had a particle size distribution ranging from 20μ m to 63μ m, with a median particle size (D50) of 40.5μ m. Although the median particle size is slightly larger than the selected layer thicknesses, adequate powder spreading was achieved due to the broad particle size distribution [8]. Energy-dispersive X-ray spectroscopy (EDS) was employed to characterize the elemental composition of the powder, with the resulting chemical analysis reported in Table 1.

The LPBF system was equipped with a Ytterbium fiber laser with a 30μ m spot diameter and operated within a chamber filled with ultra-high-purity argon (>99.99%).

Different specimen geometries, namely cubic, tensile, and cylindrical, were fabricated according to the specific objectives of the work.

Cubic specimens with a volume of 1 cm3 were produced for microstructural characterization.
Four round tensile specimens, designed in accordance with ASTM E8/E8M-25 standards, were fabricated to evaluate tensile properties, with their principal axis aligned parallel to the building direction.

Finally, three cylindrical specimens, each 20 mm in diameter and 70 mm in length, were manufactured for each LPBF parameter set for machining trials, with their axes oriented parallel to the building direction to ensure representative process-orientation effects.

The processing window for LPBF of AlSi7Mg was identified in a previous study [7] based on the criterion of maximum densification. After determining the optimal set of process parameters, the layer thickness was varied within a range close to this optimal condition, while remaining within the limits specified by the machine manufacturer. In particular, the layer thickness was varied between 20 and 30μ m. The decision to vary the layer thickness was motivated by the fact that, among the process parameters investigated for Al-Si alloys, layer thickness has been less extensively studied in the literature compared to laser power and scanning speed.

The LPBF process parameters are reported in Table 2. It is worth noting that, hereafter, samples processed with a layer thickness of 20μ m will be referred to as L20, those with a layer thickness of 25μ m as L25, and those with a layer thickness of 30μ m as L30.

Fabrication was performed using the chessboard scanning pattern, which was chosen and set at 3 mm×3 mm, with layers rotated 67∘ from each other, according to [9].

All the samples were detached from the baseplate by using a high-precision micro-cutting machine Metkon Micracut 200 S.

After LPBF, the samples underwent a T6 heat treatment, consisting of solubilization at 535∘C for 3 hours followed by artificial aging at 150∘C for 6 hours in an argon atmosphere, to mitigate residual stresses induced by LPBF and promote precipitation hardening for enhanced material strength.

Table 1. Chemical composition of the AlSi7Mg powder ( wt% ).

Al | Si | Mg | Fe | Cu | Zn | Ti | Mn
Bal | 6.86 | 0.58 | 0.14 | 0.01 | 0.03 | 0.14 | 0.0004

Table 2. LPBF parameters.

Laser power (W) | 170
Scanning speed (mm/s) | 1056
Hatch spacing (μm) | 175
Layer thickness (μm) | 20/25/30

Mechanical characterization of the LPBF AlSi7Mg samples. A hydraulic MTS TM 322 dynamometer was used to carry out tensile tests at a constant strain rate of 1.2 min−1 at room temperature. The tensile axis of each specimen was along the sample length, which was parallel to the building direction.

Machining of the LPBF AISi7Mg samples. The heat-treated cylinders were longitudinally turned on a Mori Seiki NL1500 TM  lathe using PVD-TiAlN coated VCEX 1103 01L-F 1125 turning tools, provided by Sandvik Coromant TM. A fresh tool was used for every machining trial to prevent tool wear effects. A 9129AA Kistler™ three-component piezoelectric dynamometer was used to acquire the cutting forces (cutting force/Fc, radial force/Fr, and thrust force/Ft) at a sampling rate of 2500 Hz . During acquisition, a low-pass filter was used with cutoff frequencies of 300 Hz for Fc and Fr , and 600 Hz for Ft, as suggested by the dynamometer's producer guidelines. After acquisition, the force
signals were filtered with a 2 nd-order low-pass filter with a cutoff frequency set at 25 Hz to remove undesired noise.

A preliminary roughing step, involving the removal of a 0.5 mm depth of cut, was carried out before the finishing pass to reach the target depth. Afterwards, the finishing step was performed using the following cutting parameters: cutting speed Vc =200 m/min, feed f=0.03 mm/rev, depth of cut d=0.25 mm, longitudinal cutting length Lc=6 mm, which were selected in accordance with the tool manufacturer's guidelines. Conventional flood lubrication was employed using a water-based fluid with 5% semi-synthetic oil (Astro-Cut HD XBP, Monroe Fluid Technology), supplied to the cutting zone by spraying at 8 bar and a flow rate of 2.5 L/min. Cryogenic turning, on the other hand, involved liquid nitrogen ( LN2 ) as a coolant, sprayed towards the flank and rake faces of the tool via two 0.9 mm diameter copper nozzles. LN2 was stored at a constant pressure of 15 bar inside a dewar and conveyed to the nozzles through a vacuum-insulated tube. More details about the cryogenic cooling apparatus can be found in [10].

Microstructure and mechanical characterization of the machined LPBF AlSi7Mg samples. The surface topographies of the machined samples were acquired using a Sensofar Metrology™ SNeox 3D optical profiler. Subsequently, surface roughness parameters were calculated using the SensoVIEW TM  software in accordance with ISO 25178 [11]. The analysis included a form-removal procedure to eliminate the cylindrical geometry induced by turning, followed by band-pass filtering with an S-filter of 2.5μ m, an L-filter of 0.8 mm , and a sampling length of at least 4 mm . Three surface measurements were performed for each machined sample.

Three surface roughness parameters were considered: the arithmetic mean height ( Sa ), the reduced valley depth (Svk), and the reduced peak height (Spk). Sa represents the conventional surface roughness parameter; Svk quantifies the average depth of the valley regions, indicating the volume available for lubricant retention or debris trapping; Spk corresponds to the average height of the protruding peaks above the core surface level and is associated with the initial wear behavior of the surface.

The quality of the machined surfaces was further examined using a FEI Quanta TM 450 scanning electron microscope (SEM) to identify the presence of surface defects.

Finally, cross-sections of the machined samples were ground, polished, and etched using Weck's reagent to evaluate machining-induced microstructural alterations. High-magnification SEM images were acquired in the near-surface region to characterize the subsurface microstructure.

Additionally, electron backscatter diffraction (EBSD) analyses were performed on the machined samples using an Oxford Instruments TM  CMOS-Symmetry detector integrated into a Tescan SOLARIS TM  dual-beam field-emission scanning electron microscope (FE-SEM).

Results and Discussion

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

Mechanical properties of the LPBF AlSi7Mg samples. The true stress-strain curves reported in Fig. 1 for the samples fabricated with different layer thicknesses clearly demonstrate the influence of this printing parameter on the resulting mechanical performance. Increasing the layer thickness leads to higher mechanical strength, while the overall ductility, expressed in terms of both elongation and toughness, tends to decrease. The samples printed with the largest layer thickness exhibit an increase in ultimate tensile strength (UTS) of 40% compared to those manufactured with the smallest layer thickness. Conversely, the elongation at fracture decreases by 39% when comparing L30 to L20.

Fig. 1. Mechanical properties of the LPBF AlSi7Mg samples at varying layer thicknesses.

Surface integrity of the machined LPBF AISi7Mg samples. The cutting force data reported in Fig. 2a reveal distinct differences between flood and cryogenic machining conditions.

[picture]

Fig. 2. a) Cutting forces and b) surface roughness of the machined AlSi 7 Mg samples at varying layer thicknesses and cooling conditions.

Fig. 2. a) Cutting forces and b) surface roughness of the machined AlSi 7 Mg samples at varying layer thicknesses and cooling conditions.

Under cryogenic cooling, all three force components are consistently lower than those measured under flood lubrication. On average, reductions of 50%,43%, and 14% were observed for the L20, L25, and L30 samples, respectively.

This trend appears to contradict what is typically expected when liquid nitrogen is applied, as cryogenic cooling generally reduces the cutting temperature and may enhance strain hardening in the material, often resulting in increased cutting forces. A possible explanation lies in the high thermal conductivity of aluminum alloys, which promotes rapid heat dissipation from the cutting zone and reduces the effectiveness of temperature reduction under cryogenic cooling compared with other metals, where thermal effects play a more dominant role. Instead, the dominant effect of cryogenic cooling in the present case is the mitigation of tool-chip adhesion and the suppression of built-up edge formation, which are particularly critical in machining aluminum alloys [12].

With respect to the influence of layer thickness, lower cutting forces are generally associated with samples produced with higher layer thickness under flood cooling conditions. Specifically, under flood lubrication, a reduction of approximately 30% in cutting force is observed when moving from L20 to L30 samples. This trend can be correlated with the specific cutting energy: materials exhibiting higher ductility typically require higher cutting forces [3].

By contrast, under cryogenic conditions, the cutting forces appear largely insensitive to the LPBF processing parameters of the machined samples.

In addition, the variability of the measured forces depends on layer thickness. The force scatter decreases progressively from L20 to L30, with the latter exhibiting the most stable cutting behaviour. This reduced variability at higher layer thicknesses is likely attributable to decreased microstructural
anisotropy, whereas the greater anisotropy associated with lower layer thicknesses leads to more unpredictable material behaviour during machining [3].

The corresponding surface roughness parameters reported in Fig. 2b further support the benefits of cryogenic cooling. Cryogenically machined surfaces exhibit lower Sa, particularly for lower layer thickness, as well as reduced Spk and Svk values, indicating a decrease in average roughness and a diminished presence of surface peaks and valleys, respectively.

[picture]

Fig. 3. SEM images of the machined surfaces of AlSi7Mg samples at varying layer thicknesses and cooling conditions.

Fig. 3. SEM images of the machined surfaces of AlSi7Mg samples at varying layer thicknesses and cooling conditions.

A similar combined effect of layer thickness and cooling condition observed for the cutting forces is evident for the roughness parameters. Under flood conditions, increasing layer thickness leads to a pronounced reduction in Sa,Spk, and Svk of 77%,74% and 80%, respectively, when moving from L20 to L30. Conversely, under cryogenic conditions, the surface roughness parameters remain nearly unchanged with varying layer thickness, showing variations of only 10% and 14% from L20 to L30 for Sa and Spk , respectively.

In general, the surface roughness results appear to be well aligned with the cutting force trends.
The SEM images in Fig. 3 provide qualitative confirmation of the defect morphology observed on the machined surfaces of AlSi7Mg samples under varying layer thicknesses and cooling conditions.

Under flood lubrication, the surfaces exhibit a higher density of tearing, particularly in samples produced with lower layer thicknesses. At the highest layer thickness, the dominant defect type shifts from tearing to pits. These pits appear as localized depressions and irregular cavities, indicative of particle undermining associated with the eutectic regions. In contrast, the surfaces machined under cryogenic cooling consistently show the same defect type, namely pits, regardless of the layer thickness.

A clear relationship between defect morphology and cutting forces emerges when comparing Fig. 1 and Fig. 3: when cutting forces exceed a certain threshold, tearing is preferentially formed, whereas lower force levels promote the formation of pits. Furthermore, the different defect types correspond to different surface roughness values, as tearing tends to increase roughness, while pits reduce it.

The metallographically-prepared cross-sections shown in Fig. 4 show the subsurface microstructural characteristics resulting from machining at different layer thicknesses and cooling strategies. In general, a thin yet clearly distinguishable plastically deformed layer (PDL) is observed immediately beneath the machined surface.

Under flood cooling conditions, the PDL is more pronounced in samples produced with lower layer thicknesses, where the deformation depth is greater and noticeable microstructural refinement
is observed. The increased deformation correlates well with the higher cutting forces and the more frequent occurrence of tearing defects discussed previously.

[picture]

Fig. 4. Microstructure in the correspondence of the machined surfaces of AlSi 7 Mg samples at varying layer thicknesses and cooling conditions.

Fig. 4. Microstructure in the correspondence of the machined surfaces of AlSi 7 Mg samples at varying layer thicknesses and cooling conditions.

Conversely, in cryogenically machined samples, the subsurface deformation layer is slightly reduced and, in the case of L20, barely visible. This reduction in subsurface deformation is consistent with the lower cutting forces and the predominance of pit-type defects observed under cryogenic cooling.

[picture]

Fig. 5. EBSD analysis of the subsurface of AlSi7Mg L30 machined samples.

Fig. 5. EBSD analysis of the subsurface of AlSi7Mg L30 machined samples.

Fig. 5 presents the EBSD pole figures acquired in the correspondence of the subsurface of the L30 LPBF AlSi7Mg machined samples at varying cooling conditions. Both figures exhibit predominantly the same crystallographic planes, {101} and {111}, which correspond to the primary slip systems in FCC aluminum alloys.

The similarity in pole figures indicates that the deformation texture remains consistent between cryogenic and flood machining, supporting the hypothesis that cryogenic cooling did not induce a brittle deformation response despite the temperature reduction.

Summary

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

In this work, the influence of cryogenic machining on the surface integrity of LPBF AlSi7Mg aluminum alloy printed with different layer thicknesses was investigated.

The main conclusions can be summarized as follows:

Cryogenic machining significantly reduced cutting forces compared to conventional flood lubrication, with the most pronounced reductions observed in samples manufactured with lower layer thicknesses.

Surface roughness parameters (Sa, Spk, Svk) were consistently improved under cryogenic cooling, indicating enhanced surface quality and reduced surface asperities, particularly for lower layer thicknesses.

A strong correlation was observed between cutting forces and surface integrity: higher cutting forces promoted tearing defects, whereas lower force levels favored pit-type defects, which are associated with reduced surface roughness.

Increasing layer thickness improved machinability and surface integrity under both cooling conditions, leading to lower cutting force variability, reduced roughness, and fewer severe surface defects.

Cryogenic machining effectively reduces machining-induced subsurface plastic deformation, resulting in a thinner affected layer and a sharper transition between the machined surface and the bulk microstructure.

EBSD analysis revealed similar deformation textures under both cryogenic and flood machining, with dominant {101} and {111} crystallographic planes, indicating that cryogenic cooling did not induce a brittle deformation response in the FCC AlSi7Mg alloy.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

The work was financed by the European Union - NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033-17/06/2022, Spoke 11 - Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commission's official position. Neither the European Union nor the European Commission can be held responsible for them.

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The original version of this paper is available on https://www.scientific.net/DDF.450.31.pdf

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 41-48

doi: 10.4028/p-GVG0Wh

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-24

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-15

On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

François Ducobu 1,a*, Olivier Pantalé 2, b, Laurent Spitaels 1,c and Domenico Umbrello 3, d

1 Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, University of Mons, Mons, Belgium

2 Université de Toulouse, UTTOP, LGP, Tarbes, France

3 Department of Mechanical, Energy and Management Engineering, University of Calabria, Arcavacata, CS, Italy

a Francois.Ducobu@umons.ac.be, b Olivier.Pantale@uttop.fr,

c  Laurent.Spitaels@umons.ac.be, domenico.umbrello@unical.it

Keywords: Taylor impact test, finite element, CEL, adaptive mesh refinement, Johnson-Cook.

Abstract

This paper examines the use of adaptive mesh refinement in Coupled Eulerian-Lagrangian (CEL) finite element modeling of the Taylor impact test. Traditional Lagrangian models suffer from severe mesh distortion under large strains, while CEL avoids this issue but requires much longer computation times. Using Abaqus/Explicit 2025, a mesh convergence study was performed to identify an accurate reference mesh. Adaptive mesh refinement was then applied to refine the mesh dynamically based on equivalent plastic strain. Results show that CEL models achieve convergence, unlike Lagrangian models, and that adaptive mesh refinement reduces computation time by up to 67%, with minimal impact on accuracy. This approach provides an efficient and reliable solution for high-strain simulations.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

Identifying parameters of material constitutive models for modeling cutting operations is a challenging task due to the values of strain, strain rate and temperature seen by the workpiece material during that operation [1]. For a given empirical constitutive model, such as the Johnson-Cook (JC) model [2], the identification process can be performed in mainly two ways [3]: from dedicated material testing equipment results such as those used in this study, or directly from cutting experiments or simulations such as in Ducobu et al. [4] or in Klippel et al. [5].

The most used dedicated equipment for material behavior identification in these severe conditions are the Split Hopkinson Pressure Bars [6] and the Taylor impact test [7]. Inverse identification is then carried out by comparing the experimental geometry of the specimen after deformation with the geometry from the numerical modelling of the test. The parameters of the material constitutive behavior are adjusted in the model for the numerical and experimental geometries to match.

The Lagrangian formulation has long been the natural choice for modeling this type of test [8]. The mesh and the material being tied together, however, often leads to strong mesh deformations due to the large strains. This leads to a decrease in the quality of the results, and consequently of the identification procedure and of the value of the material parameters. The Coupled EulerianLagrangian (CEL) formulation avoids these issues because the mesh is no longer tied to the material. It has already been successfully applied in the modeling of cutting operations [9]. Although it suppresses the problem of elements distortion, the computation times are usually significantly higher than for a model using the Lagrangian formulation, mainly due to a higher number of elements.

This study introduces the adaptive mesh refinement technique in the CEL modeling with the main objective of reducing the computation time without degrading the quality of the results. The CEL formulation has already been adopted for the 3D coupled stress-heat transfer modelling of the Taylor impact [10]; the current implementation of the adaptive mesh refinement is limited to adiabatic analysis. The main contributions of this paper by comparison to Ducobu et al. [10] are the introduction
of the adaptive mesh refinement in the CEL formulation, the study of the influence of the mesh size on the quality of the results and the CPU computation time, as well as the reduction of the computation without degradation of the results.

The paper starts by comparing the Lagrangian and CEL models in adiabatic conditions and by determining the best mesh to be used through a mesh convergence study. The adaptive mesh refinement technique is then introduced and the results are compared to the previous CEL models.

Presentation of the finite element models

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

A Lagrangian model and a CEL model are developed in the frame of this study. They are 3D finite element models using Abaqus/Explicit 2025 with the same conditions as Ming and Pantalé [11] and Ducobu et al. [10]. A quarter of the actual configuration is modelled with adequate boundary conditions to reduce computation time. The specimen is a cylinder of 32.4 mm long and 3.2 mm radius. Initial conditions are a speed in the axial direction of 200 m/s and a specimen temperature of 293 K . The simulation is run for 80μ s. Contact conditions between the specimen and the rigid plate are assumed to be perfect without friction.

The Lagrangian model includes only the specimen, while the CEL model must include the volume where the specimen will deform. This results in a larger mesh for CEL. To reduce computation time as much as possible, the initial void where the specimen will deform has a conical shape and the volume surrounding the initial cylinder is also meshed, as shown in Fig. 1. The mesh of the specimen is the same for both models. It must be noted that the size of the model and the boundary conditions were selected to avoid any loss or gain of mass during the simulations.

[picture]

Fig. 1. Initial geometry and fine mesh of the Lagrangian (a) and CEL (b) models

Fig. 1. Initial geometry and fine mesh of the Lagrangian (a) and CEL (b) models

The behavior of the specimen is modeled with JC constitutive model [2]:

σJC=[A+B·εn]·[1+C·ln(ε˙ε˙0)]·[1−(T−TroomTmelt−Troom)m].(1)

Five constants need to be identified: A,B,n,C and m.ε˙0 is the reference strain rate. Troom  is the room or reference temperature and Tmelt  is the melting temperature. The specimen material is 42CrMo4-FP steel. Table 1 provides the material properties and JC parameters.

Table 1. Johnson-Cook parameters and material properties of 42CrMo4-FP steel [11]

A [MPa] | B [MPa] | C | n | m | ε̇₀ [s⁻¹] | Troom [K]
504 | 370 | 0.025 | 0.17 | 0.793 | 0.01 | 293
Tmelt [K] | E [GPa] | ν | ρ [kg/m³] | λ [W/mK] | cp [J/kgK] | α [K⁻¹]
1813 | 206.9 | 0.29 | 7830 | 34 | 460 | 1.23×10⁻⁶

The current implementation of adaptive mesh refinement in Abaqus/Explicit 2025 is limited to adiabatic analysis [12], preventing carrying out a coupled stress-heat transfer analysis. Adiabatic heating effects are, however, included due to inelastic deformation (with an inelastic heat fraction of 0.9). The element type used is 3D linear 8-node with reduced integration, meaning C3D8R elements for the Lagrangian model and EC3D8R elements for the CEL model. Computations were performed with Abaqus/Explicit 2025 on Windows 1164 bits and 32 GB of RAM, using 4 cores of an Intel i913900HX@1.70−5.40GHz processor and double precision.

Influence of Mesh Size on the Results

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

A mesh sensitivity analysis on the results of both Lagrangian and CEL models is performed to assess the influence of the mesh size on the results and determine the best mesh size to use for the simulation of the 3D Taylor impact test. The Lagrangian model is the reference as this formulation is well-known and widely adopted. Structured mesh is adopted (except in the conical region of the CEL model) with 4 different element sizes: coarse (mean element size of 0.8 mm ), medium (mean element size of 0.4 mm ), fine (mean element size of 0.2 mm ) and very fine (mean element size of 0.15 mm ).

The shape of the deformed specimen can be assessed in Fig. 2 and Fig. 3. It is similar to experimental results for the same material but different specimen dimensions and speed [8]. As already observed [10], small defects, similar to indentations are noted for coarse and (slightly) medium CEL meshes mainly due to the errors in tracking the shape of the specimen in the thin void layer around the specimen. This should be reduced with an increase of the size of the void region surrounding the undeformed part of the specimen. However, as they are far from the region of interest, they do not influence the results and this would lead to an increase in computation time without benefit for the quality of the results. A similar observation is performed on the upper corner of the specimen that is rounded (or chamfered) due to the interpolation carried out by Abaqus to determine where material is. Such a rounding is inherent to the implementation of the CEL formulation and it is also noticed when it is used for other applications [9].

Table 2 provides a quantitative comparison of the results:

Lf : final length and Rf : final radius of the specimen, measured in a lateral face of the specimen.

ε¯max  : maximal equivalent plastic strain, σmax  : maximal equivalent von Mises stress and Tmax  : maximal temperature.

Nelements  : number of elements of the model and t^CPU : computation time normalized by the computation time of the Lagrangian model with a coarse mesh.
Refining the mesh globally leads to a convergence of the dimensions of the specimen for both Lagrangian and CEL models. The general trends are a reduction of the final length and an increase of the final radius of the specimen when the mesh density increases. When comparing the results of the fine and of the very fine meshes, it turns out that the fine CEL mesh is the converged case.

[picture]

Fig. 2. Equivalent plastic strains for the Lagrangian coarse (a), medium (b), fine (c) and very fine (d) meshes

Fig. 2. Equivalent plastic strains for the Lagrangian coarse (a), medium (b), fine (c) and very fine (d) meshes

[picture]

Fig. 3. Equivalent plastic strains for the CEL coarse (a), medium (b), fine (c) and very fine (d) meshes, and adaptive mesh refinement with 1 level (e) and 2 levels (f) meshes (rounding and indentation aterfacts highlighted)

Fig. 3. Equivalent plastic strains for the CEL coarse (a), medium (b), fine (c) and very fine (d) meshes, and adaptive mesh refinement with 1 level (e) and 2 levels (f) meshes (rounding and indentation aterfacts highlighted)

The contours (Fig. 2 and Fig. 3) of the equivalent plastic strains, ε¯, and the values (Table 2) of the maximal equivalent plastic strain, ε¯max , clearly show the mesh dependence and localization of the deformation for the Lagrangian model. As highlighted in Fig. 4, convergence is not reached, and unrealistically high and increasing values of strain are observed with denser mesh. This clearly shows that elements in the impact region of the specimen are too much deformed (Fig. 5) and that such results cannot be used. Consequently, comparing the dimensions of the specimen for both formulations, it is not surprising that the final radius is larger for the Lagrangian formulation than for the CEL formulation.

Table 2. Results for the different meshes with the Lagrangian and CEL formulations (converged results in bold)

Model | Coarse | Medium | Fine | Very fine
Lagrangian models
Lf [mm] | 39.17 | 38.85 | 39.01 | 39.09
Rf [mm] | 6.74 | 7.03 | 6.93 | 6.93
εmax | 1.27 | 6.12 | 5.58 | 11.83
σmax [MPa] | 678 | 763 | 913 | 747
Tmax [K] | 599 | 1357 | 1305 | 1675
Nelements | 600 | 4860 | 38 880 | 91 800
t̂CPU | 1 | 2 | 108 | 491
CEL models
Lf [mm] | 39.08 | 39.07 | 38.92 | 39.01
Rf [mm] | 6.50 | 6.67 | 6.74 | 6.79
εmax | 0.99 | 1.20 | 1.26 | 1.27
σmax [MPa] | 531 | 614 | 634 | 633
Tmax [K] | 534 | 583 | 598 | 600
Nelements | 1080 | 8477 | 67 340 | 150 998
t̂CPU | 3 | 15 | 412 | 1334

The maximal equivalent von Mises stress, σmax , and the maximal temperature, Tmax , values globally follow the same trend as expected. The absence of mesh deformation for the CEL formulation prevents these localizations and allows to reach convergence. The results of the CEL model are therefore more accurate and reliable.

[picture]

Fig. 4. Illustration of the convergence of the maximal equivalent plastic strain for the CEL formulation and not for the Lagrangian formulation

Fig. 4. Illustration of the convergence of the maximal equivalent plastic strain for the CEL formulation and not for the Lagrangian formulation

As expected, refining the mesh increases the computation time whatever the formulation. The computation time is larger for the CEL formulation due to a higher number of elements for the same mesh density. The better accuracy and reliability of the CEL model have therefore a CPU cost.

Introduction and influence of adaptive mesh refinement for the CEL formulation

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

Presentation of adaptive mesh refinement in CEL. Now that the mesh size to use for the 3D simulation of the Taylor impact test is determined, adaptive mesh refinement is considered to reduce the computation time of the CEL formulation (i.e. its disadvantage by comparison to the Lagrangian
formulation) without degrading the quality of the results. The ultimate goal would be to obtain results as accurate as with the fine mesh for computation times as low as those with coarse meshes.

The adaptive mesh refinement allows to refine the mesh in the 3 directions of space by the same factor for each element satisfying a defined criterion [12]. One level of refinement consists of dividing 1 element into 8 elements (i.e. each side of the initial element is divided by 2 ). The number of levels is in theory not limited, but preliminary tests showed that a level higher than 2 was leading to the immediate termination of the computation. However, this limitation does not impact the current study as 2 levels is what is needed. Indeed, dividing the edges of the elements from the coarse mesh (0.8 mm) in 4 gives the same mesh size as the fine converged mesh (0.2 mm).

Adaptive mesh refinement is applied to the CEL model with coarse mesh with a value of 0.1 for the equivalent plastic strain, ε¯, as refinement criterion (this value has been set following a preliminary sensitivity study). It means that any element with a ε¯ value of 0.1 will be divided into 8 elements for 1 level of refinement and into 64 elements for 2 levels of refinement. The minimum element size with 1 level of mesh refinement corresponds to the elements size of the medium mesh, while it is to the fine mesh for 2 levels of mesh refinement.

[picture]

Fig. 5. End of the deformed specimen results showing the mesh and the equivalent plastic strains for the Lagrangian coarse (a), Lagrangian medium (b), Lagrangian fine (c), CEL coarse (d), CEL medium (e), CEL fine (f), and CEL adaptive mesh refinement with 1 level (g) and 2 levels (h) meshes

Fig. 5. End of the deformed specimen results showing the mesh and the equivalent plastic strains for the Lagrangian coarse (a), Lagrangian medium (b), Lagrangian fine (c), CEL coarse (d), CEL medium (e), CEL fine (f), and CEL adaptive mesh refinement with 1 level (g) and 2 levels (h) meshes

Results and discussions. Fig. 3 shows the deformed specimens at the end of the simulation, while Fig. 5 demonstrates the refinement of the mesh in the areas where ε¯ is larger than 0.1 . The results are qualitatively close to those without adaptive mesh refinement. The quantitative comparison using
values from Table 3 confirms that the dimensions of the deformed specimen are close with and without adaptive mesh refinement. Differences between the medium mesh and 1 level of refinement are close to 2%, while they are close to 1% between the fine mesh and 2 levels of refinement. Similarly, the maximal equivalent plastic strain, the maximal equivalent von Mises stress, and the maximal temperature are close.

The dramatic change is to be found in the computation time. Indeed, reductions of 27% and 67% are observed when using adaptive mesh refinement by comparison to the medium and fine meshes, respectively. The objective when introducing adaptive mesh refinement in the CEL models is therefore achieved: the computation time is significantly reduced without significantly decreasing the quality of the results.

Table 3. Results for the CEL formulation with and without adaptive mesh refinement (results without adaptive mesh refinement are the same as in Table 2, they are included here for an easier comparison)

Model | Lf [mm] | Rf [mm] | εmax | σmax [MPa] | Tmax [K] | Nelements | tCPU
CEL Coarse | 39.08 | 6.50 | 0.99 | 531 | 534 | 1080 | 3
CEL Medium | 39.07 | 6.67 | 1.20 | 614 | 583 | 8477 | 15
CEL Fine | 38.92 | 6.74 | 1.26 | 634 | 598 | 67 340 | 412
CEL 1 level | 38.23 | 6.53 | 1.22 | 590 | 588 | 9720 | 11
CEL 2 levels | 38.59 | 6.67 | 1.32 | 664 | 609 | 70 200 | 135

Conclusions

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

The Taylor impact test has been modeled in 3D with Abaqus/Explicit using both Lagrangian and CEL formulations. While severe mesh distortions and localizations have been highlighted for the Lagrangian model, the CEL formulation showed better performance thanks to the absence of mesh deformation. This allowed to reach convergence of dimensions, and strains, stresses and temperatures with the CEL formulation, contrary to the Lagrangian formulation. The longer computation times for the CEL models were successfully decreased with the introduction of adaptive mesh refinement. This positive impact on computation resources did not degrade significantly (difference of max. 2% ) the quality of the results. This study showed that high quality results can be obtained with the CEL formulation and for a small computation cost (reduction of CPU time up to 67%) if adaptive mesh refinement is used. Future works could expand CEL adaptive mesh refinement to other applications, and especially to cutting operations, provided it is implemented for stress-heat transfer coupled analysis.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

This research was funded by the European Union and the Walloon regional government (HybridAM research project).

References

The original version of this paper is available on https://www.scientific.net/DDF.450.41.pdf

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F. Ducobu, A. Demarbaix, O. Pantalé, Finite element modelling of the Taylor impact test in 3D with the Coupled Eulerian-Lagrangian method, in: 24th International Conference on Material Forming (ESAFORM 2021), Liège, Belgium, 2021. .

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SIMULIA, Abaqus Documentation, Dassault Systems, 2025.

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The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 49-59

doi: 10.4028/p-YtwNQ4

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-12

Revised:
2026-02-19

Accepted:
2026-02-22

Online:
2026-04-15

Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

Martin Beutner 1,a*, Matthias Hackert-Oschätzchen 1, b, Marco Eich 2,c, Bernhard Karpuschewski 2, d

¹Chair of Manufacturing Technology with Focus Machining, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

2 Leibniz-Institut für Werkstofforientierte Technologien - IWT, Badgasteiner Str. 3

28359 Bremen

a  martin.beutner@ovgu.de, b  matthias.hackert-oschaetzchen@ovgu.de, c  eich@iwt-bremen.de, d  karpuschewski@iwt-bremen.de (*corresponding author)

Keywords: gear hobbing, wear, cutting fluid

Abstract

Geared components increasingly require higher torque density, driving the use of highstrength steels and necessitating stable machining processes, particularly in small and medium-sized enterprises that rely on cutting fluids. This study evaluates the performance potential of various cutting fluids in gear hobbing using a fly-cutting analogy test setup, which enables controlled and reproducible analysis of wear mechanisms of a single hob tooth. Water-based and oil-based cutting fluids, different tool substrate materials (PM-HSS, MC90, and tungsten carbide), and workpiece steels of different strength levels were systematically investigated. The results show that PM-HSS is unsuitable for machining the highest-strength material. Dry machining improved tool life, whereas the application of cutting fluids led to increased tool wear.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

Previous work conducted within the FVA (ForschungsVereinigung Antriebstechnik or Research Association for Drive Technology in Germany) research project 744 I (IGF No. 18538 BG) demonstrated that the application of different coolant-lubricant strategies in gear hobbing leads to significant variations in process performance. The investigations examined two oil-based cutting fluids, an emulsion, and dry machining, using single-tooth fly cutting as an analogy process to gear hobbing. The cutting speed vc was varied across five levels while all remaining parameters were kept constant. Although no performance differences were observed between the two oil-based cutting fluids, dry machining exhibited substantially longer tool life at low cutting speeds. With increasing cutting speed, tool life converged toward the levels achieved with the oil-based fluids. These results were confirmed independently in Bremen and in Magdeburg, with no significant deviations between the two research institutions [1].

While these initial experiments were carried out on the case-hardening steel 20 MnCrB 5 with a tensile strength of Rm≈530 N/mm2, the industrial relevance lies in assessing whether these findings can be transferred to the machining of higher-strength steels (Rm≥1000 N/mm2), as the torque density of modern gear systems continues to increase. Of particular interest is the technological and economic potential of wet machining compared to dry machining. Over the past two decades, research has predominantly focused on the introduction [2,3] and further development of dry machining [4, 5, 6, 7]. However, the advantages of dry machining - such as reduced machining costs and increased productivity - are typically realized only in large-scale production [8]. In contrast, wet machining remains common in small- and medium-batch manufacturing [1], where its robustness under frequently changing gear geometries is considered beneficial.

Given this situation, no systematic studies currently exist that evaluate the potential of coolantlubricant applications in gear hobbing of higher-strength materials while considering modern tool and machine concepts. To determine the transferability of the findings from the previous project to these more demanding material classes, further research activities are required.

Experimental Methodology

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

For the experimental investigations, a representative spur gear geometry from the heavy commercial vehicle (HCV) segment was selected. The experiments were conducted using the singletooth fly cutting test (see Figure 1 b), a research-established analogy test for industrial gear hobbing, in which only a single cutting tooth is engaged in the cutting process. This approach allows a significant reduction in experimental time and cost, particularly for wear investigations, compared to full-scale gear hobbing operations [9,10]. Compared to industrial gear hobbing the process kinematic is adapted, but the resulting gear geometry is the same for both processes. Each parameter test was carried out once within this study, as preliminary three repeat experiments demonstrated stable and reproducible results regarding wear pattern and tool life, rendering further repetitions unnecessary. Due to the number of experimental combinations, the specific parameter combinations of tool substrate to the respective workpiece material are introduced in the results section.

The investigated spur gear geometry is shown in Figure 1 and is characterized by a module of 4 mm,26 teeth and a pressure angle of 20∘. The gear has an outside diameter of 114.6 mm and a face width of 60 mm , which are representative dimensions for gears used in heavy commercial vehicle applications. All experiments were carried out on a Liebherr LC 180 gear hobbing machine. The applied teeth were cut out by wire erosion from three different hobs having 18 gashes and an outer diameter of 100 mm , featuring AlCr-based coatings. The different tool substrate materials are powder metallurgical high-speed steel (PM-HSS), MC90 (an iron-based tool steel of the Fe−Co−Mo alloy system) and tungsten cemented carbide. In addition to the geometric and tool-related parameters, Figure 1 also illustrates the reference profile geometry, as well as the experimental setup on the machine tool showing the single-tooth engagement between tool and workpiece.

[picture]

Fig. 1. a) Parameters of the gearing and b) Test setup at the machine tool

Fig. 1. a) Parameters of the gearing and b) Test setup at the machine tool

At the beginning of the investigations, the initial condition of the fly cutting teeth (with coating) was characterized measuring the surface topography of the rake face. The surface roughness was quantified using the Rz parameter, as depicted in Figure 2. Among the investigated tool substrates, the PM-HSS exhibited the highest roughness with approximately Rz≈2.0μ m. In contrast, the surface roughness decreased to about Rz≈1.5μ m for the MC90 substrate and further to Rz≈0.5μ m for the tungsten-cemented carbide. These variations in initial surface characteristics can be attributed to the different manufacturing processes of the substrates. All recorded roughness values are within typical
industrial tolerance limits, thereby ensuring that the tools used in the experiments are representative for industrial machining conditions.

Additionally, the cutting-edge radius was examined. The cutting-edge radius at the tip of the tooth was found to be comparable for all three substrates, with in the range of rβ≈15μ m.

[picture]

Fig. 2. Initial state of different tool substrate teeth

Fig. 2. Initial state of different tool substrate teeth

Table 1 and Table 2 presents the chemical composition and mechanical classification of the investigated steels, as determined by Atomic Emission Spectroscopy (AES). As a representative of application-oriented engineering steels, a quenched and tempered 42 CrMoS 4 ( 1.7227 ) steel was selected and investigated in two different tempering conditions in order to represent two distinct strength levels. As a representative material for the automotive sector, the case-hardening steel 20 MnCrB 5 (1.7168) was chosen. The chemical compositions of both steels are shown in Table 1 and Table 2.

Table 2 Chemical composition of quenched and tempered steel using atomic emission spectroscopy

1.7227 - quenched and tempered steel with superscript number and superscript number |
C | Mn | Si | Cr | Ni | Mo | Cu | P | S | Al
0.43 | 0.79 | 0.288 | 1.15 | 0.05 | 0.189 | 0.04 | 0.018 | 0.022 | 0.035

Table 3 Chemical composition of the case-hardening steel using atomic emission spectroscopy

1.7168 - case-hardening steel with superscript number |
C | Mn | Si | Cr | Ni | Mo | Cu | P | S | Al
0.150 | 1.27 | 0.25 | 1.36 | 0.09 | 0.05 | 0.11 | 0.009 | 0.021 | 0.039

The steel grade 1.7227 exhibits a tensile strength ( Rm ) between Rm=883 N/mm2 and Rm=1130 N/mm2, depending on the tempering condition. The steel grade 1.7168 shows a significantly lower core tensile strength of approximately Rm=540 N/mm2, which is characteristic for case-hardening steels. The low carbon content ( 0.15wt.% ) enables surface carburizing.

Results

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Figure 3 shows the results of the interpenetration simulations performed to localize the specific load distribution along the cutting edge for the investigated gear hobbing process. The simulations were carried out using the commercial Software SpartaPro (WZL Aachen) which is used in research and industry [11,12]. Three different axial feeds ( fa=2.9,4.8, and 6.4 mm/rev ) were calculated, while
all remaining process parameters were kept constant. The load evaluation is plotted along the unfolded tool profile, enabling a direct assignment of local load maxima along the cutting edge. The diagram a) presents the maximum uncut chip thickness as a function of the unfolded tool profile. Independent of the applied axial feed, a peak is observed at the transition between the leading flank (LF) and the tip of the tooth. Increasing the axial feed results in a systematic increase of the maximum chip thickness, while the position of the peak remains unchanged. The diagram b) shows the accumulated cutting length, revealing a similar distribution with maximum values in the transition from tip to trailing flank (TF) region. The diagram c) depicts the number of cutting engagements, again showing pronounced maxima at the identical cutting edge location as the max. uncut chip thickness. The coincidence of peak values across all evaluated load indicators confirms this region as the most highly stressed section of the cutting edge under the investigated conditions.

The microscopic image in Figure 3 shows a microscopic view of the rake face of a single hob tooth after a preliminary cutting test. The extension of the dashed vertical lines to the microscopic picture marks the critical section of the cutting edge where the load parameters reach their maximum on a worn-out tooth. This region corresponds to the geometric simulation. Local edge chipping and material breakouts on the rake face are clearly visible predicted by the simulation, showing a qualitative correlation between the simulation-based load localization and experimentally observed wear initiation.

[picture]

Fig. 3. Different load parameters calculated by interpenetration simulation SPARTpro:

Fig. 3. Different load parameters calculated by interpenetration simulation SPARTpro:

Figure 4 a) and b) illustrates characteristic failure modes observed during preliminary cutting experiments using different coolant-lubricant strategies. A non-water-miscible metalworking fluid with a viscosity of 13 mm2/s at 40∘C and a water-miscible metalworking fluid with a concentration of 10% were applied. Independent of the coolant-lubricant variant and despite variations in cutting parameters, the PM-HSS tool substrate proved unsuitable for machining the high-strength 42CrMoS4 steel (Rm≈1130 N/mm2).

[picture]

Fig. 4. a) and b) tool failure of the substrate PM-HSS for hobbing QT steel with Rm≈1130 N/mm2, c) resulting workpiece

Fig. 4. a) and b) tool failure of the substrate PM-HSS for hobbing QT steel with R m ≈ 1130 N / m m 2 , c) resulting workpiece

Figure 4 a) shows a microscopic view of a single tooth after a short cutting time, revealing catastrophic tooth failure at the tip radius. Pronounced material breakouts and crack initiation at the cutting edge indicate excessive mechanical loading exceeding the fracture toughness of the PM-HSS substrate. Figure 4 b) depicts a failure at the tool clamping region, where the tooth fractured close to the tool body shortly after the start of the cutting process. The resulting gear is shown in Figure 4 c). This failure mode suggests high bending and impact loads acting on the tooth during interrupted cut. Due to the early occurrence of tool breakage, a further reduction of cutting parameters was not pursued, as this would have led to impractically long process times.

[picture]

Fig. 5. Rake face comparison of the of tungsten cemented carbide substrate cutting Q&T steel with Rm≈1130 N/mm2

Fig. 5. Rake face comparison of the of tungsten cemented carbide substrate cutting Q&T steel with R m ≈ 1130 N / m m 2

Overall, the preliminary experiments demonstrates that, under the investigated process conditions, PM-HSS tools are not capable of reliably machining high-strength quenched and tempered 42 CrMoS 4 steel, regardless of the applied coolant-lubricant strategy. The observed failure modes underline the necessity of alternative tool substrates for this application.

As shown in Figure 5, dry cutting exhibits a stable wear behavior during the initial cutting stage. After machining one gear, corresponding to a cutting length of Ltool =1.56 m/ tooth, almost no flank wear is observed, with the flank wear land width (VB) remaining below 30μ m along the entire cutting
edge. In contrast, when applying oil or emulsion, severe edge chipping and large-scale cutting edge breakouts occur already at the same cutting length, preventing any further machining under these conditions. Consequently, both coolant-lubricant strategies lead to premature catastrophic failure of the tungsten cemented carbide tool. Dry cutting could be continued up to a tool life of Ltool =10.7 m/ tooth, as depicted in Figure 5 (upper right). At this cutting length, a pronounced breakout occurs at the tip flank of the tooth, resulting in a sudden increase in VB. The defined wear criterion of VB=140μ m is exceeded, leading to the end of the experiment. This abrupt increase in flank wear is a well-known characteristic wear behavior of cemented carbide tools in gear hobbing.

To further analyze the underlying damage mechanisms, a SEM investigation was conducted on single-tooth used in dry cutting and cutting with oil, as shown in Figure 6. The SEM images of the dry-cutting condition part a) reveal locally confined substrate cracks in the vicinity of the cutting edge. However, these cracks do not exhibit the typical morphology of comb cracks [13,14], which usually develop perpendicular to the cutting edge in cemented carbide tools during gear hobbing. Moreover, no material breakout originates from these cracks, and they are therefore not considered to be tool-life determining under dry cutting conditions. In contrast, the SEM analysis of the cutting edge subjected to oil lubrication part b) shows pronounced material spalling and breakouts on the rake face, occurring perpendicular to the cutting edge. Interestingly, higher-magnification SEM images reveal that these breakouts are not associated with pre-existing substrate cracks. This observation suggests that the failure mechanism is not driven by classical comb crack propagation but is more likely related to thermally induced stresses, potentially caused by thermal shock effects due to the intermittent cooling of the cutting edge by the coolant-lubricant.

[picture]

Fig. 6. Detailed wear phenomena of tungsten cemented carbide substrate cutting Q&T steel with Rm≈1130 N/mm2 for a) dry cutting and b) cutting with oil

Fig. 6. Detailed wear phenomena of tungsten cemented carbide substrate cutting Q&T steel with R m ≈ 1130 N / m m 2 for a) dry cutting and b) cutting with oil

Figure 7 compares the wear behavior of MC90 tool substrates during fly cutting of quenched and tempered 42CrMoS4 steel with a reduced strength level ( Rm=883 N/mm2 ) under dry cutting, oil lubrication, and emulsion cooling. In comparison to Figure 5, differences between oil and emulsion were observed, while dry cutting still achieves the highest tool life combined with the lowest wear level at comparable cutting lengths. Under dry cutting conditions, a relatively uniform and moderate wear progression is visible, even at extended cutting lengths of up to Ltool =14.0 m/ tooth.

When using oil lubrication, pronounced cutting edge chipping occurs along almost the entire cutting edge at the tip of the tooth, even at significantly lower cutting lengths. The damage is characterized by irregular edge breakouts and increased surface discoloration, indicating enhanced oxidation effects compared to both dry cutting and emulsion cooling. In contrast, machining with emulsion results in fewer cutting edge breakouts than oil lubrication; however, a distinctly different and more pronounced crater wear develops compared to dry cutting. Moreover, the location of crater
wear differs from that observed under dry conditions, indicating a modified thermo-mechanical load distribution at the rake face due to the lubricant. Despite these differences, a similar wear pattern is observed for all cutting strategies at the transition from the tip radius to the trailing flank, indicating a geometrically driven wear concentration in this region.

[picture]

Fig. 7. Wear comparison of the of MC90 substrate cutting Q&T steel with Rm≈883 N/mm2

Fig. 7. Wear comparison of the of MC90 substrate cutting Q&T steel with R m ≈ 883 N / m m 2

Figure 8 compares the wear behavior obtained with PM-HSS to the previously presented results for MC90 at identical workpiece strength. Despite the reduced cutting speed, a pronounced increase in crater wear is observed when oil or emulsion are applied. For oil, crater formation occurs almost immediately and is already dominant at Ltool =1.56 m/ tooth , whereas for the emulsion a similar damage pattern becomes evident at Ltool =6.4 m/ tooth. This behavior clearly differs from dry cutting, where only minor, edge-proximal crater traces appear and only at a significantly later stage of Ltool =14.0 m/ tooth . The interaction between coolant and tool material responsible for this accelerated damage cannot yet be conclusively clarified. However, the change of the substrate from MC90 to PM-HSS evidently leads to fundamentally different wear patterns under lubricated conditions.

[picture]

Fig. 8. Wear comparison of the of PM-HSS substrate cutting Q&T steel with Rm≈883 N/mm2

Fig. 8. Wear comparison of the of PM-HSS substrate cutting Q&T steel with R m ≈ 883 N / m m 2

Figure 9 shows the wear behavior of PM-HSS cutting tools during gear hobbing of a casehardening steel with a tensile strength of Rm≈540 N/mm2, representing the lowest workpiece strength level investigated. Compared to the previously examined quenched and tempered steels, this material exhibits a distinctly different interaction with the tool substrate, resulting in a different altered wear response. Here, a similar wear behavior and comparable tool life are observed for dry cutting and oil lubrication. In both cases, tool wear is dominated by crater wear on the rake face, with similar cutting lengths of Ltool =10.9 m/ tooth being reached. Under oil lubrication, the crater appears slightly more washed-out and smoother compared to dry cutting, which can be attributed to the lubricating effect reducing frictional stresses at the chip-tool interface. Under dry cutting conditions, an additional crater lip breakage is observed at the transition from the tip cutting edge to the LF at Ltool =10.9 m/ tooth . This localized failure is not present when machining with oil, suggesting that the cooling effect of the lubricant stabilizes the crater lip and delays its detachment from the cutting edge.

A further increase in tool life is achieved when applying emulsion cooling. In this case, the achievable cutting length increases by approximately 44%, reaching Ltool =15.7 m/ tooth. The images indicate a delayed initiation and slower progression of crater wear, which is attributed to the higher heat capacity of the water-based emulsion, leading to more effective heat dissipation from the cutting zone. As a result, both crater formation and propagation are delayed, extending the tool life.

[picture]

Fig. 9. Wear comparison of the of PM-HSS substrate cutting case hardening steel with Rm≈540 N/mm2

Fig. 9. Wear comparison of the of PM-HSS substrate cutting case hardening steel with R m ≈ 540 N / m m 2

Figure 10 provides a comparative and consolidated overview of all obtained tool life results and the corresponding flank wear progressions for the investigated combinations of tool substrate, workpiece strength, and coolant-lubricant strategy. The figure thus integrates the individual observations from Figures 6-10 into a unified assessment of wear behavior and process stability.

The bar chart Figure 10 d) summarizes the achievable tool life for dry cutting, oil lubrication, and emulsion cooling. In agreement with the trends observed in the preceding figures, the overall comparison clearly demonstrates a superior tool life performance under dry cutting conditions, largely independent of tool substrate and workpiece tensile strength. This advantage persists even when varying the cutting parameters and tool materials.

An exception is observed only for the machining of the case-hardening steel ( Rm≈540 N/mm2 ) using PM-HSS, where emulsion cooling achieves a higher tool life than dry cutting. This behavior is consistent with earlier investigations reported in [1] and can be attributed to the enhanced cooling effect of the water-based emulsion, which delays the initiation and progression of crater wear.

The remaining diagrams in Figure 10 a), b), c) depict the flank wear evolution for the three coolant-lubricant strategies: dry cutting (a), oil lubrication (b), and emulsion cooling (c). A comparative analysis reveals that, despite differences in cutting parameters, tool substrates, and workpiece materials, the flank wear curves under dry cutting exhibit a significantly narrower scatter band than those obtained with oil or emulsion. This indicates a more stable and predictable wear progression (process stability) for dry cutting.

In contrast, machining with oil and emulsion frequently leads to catastrophic tool failures, such as complete tool breakage or excessive edge chipping, resulting in flank wear values far exceeding acceptable limits. In these cases, tool reconditioning or reliable continuation of the process is no longer feasible, rendering such process variants economically and technologically unsuitable. Only the PM-HSS tool machining the low-strength workpiece steel ( Rm≈540 N/mm2 ) exhibits a stable wear progression under coolant application.

Overall, Figure 10 confirms that, even when reducing workpiece tensile strength or changing the tool substrate, dry cutting provides the most favorable and robust wear behavior for gear hobbing of quenched and tempered steels with progressive cutting parameters. Whether the cooling action of oil and emulsion induces thermally driven damage mechanisms, such as substrate thermal shock,
particularly for high-strength Q&T steels, could not be conclusively clarified within the present study. While such behavior is well documented for cemented carbides, comparable effects for MC90 and PM-HSS substrates have not yet been conclusively reported and require further investigation.

[picture]

Fig. 10. a),b),c) Resulting flank wear using different coolant strategies d) Tool life comparison for different cutting conditions

Fig. 10. a),b),c) Resulting flank wear using different coolant strategies d) Tool life comparison for different cutting conditions

Conclusion

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

In this paper investigations on the influence of cooling and lubrication strategies (dry cutting, oil, and emulsion) on tool wear and tool life in gear hobbing using different tool substrates (MC90, PMHSS) and workpiece materials with varying tensile strength ( Rm≈540−1130 N/mm2 ) were shown.

Across all investigated tool-workpiece combinations, dry cutting shows the most robust and reproducible tool life behavior, particularly for high-strength steels. Compared to oil and emulsion, dry cutting exhibits narrower scatter in flank wear progression and a lower tendency for severe crater wear, edge chipping, or premature tool breakage.

An exception is observed for PM-HSS machining of the low-strength case-hardening steel (Rm≈540 N/mm2), where emulsion provides a tool life increase of up to 44%, attributed to its superior cooling capacity delaying crater wear initiation.

Overall, the results demonstrate that dry gear hobbing with these parameters is a technically and economically favorable strategy for high-strength steels, while the use of cooling lubricants may induce unstable wear mechanisms. The role of thermally induced damage under lubricated conditions requires further investigation.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

This project was funded by the VDW Research Institute.

References

The original version of this paper is available on https://www.scientific.net/DDF.450.49.pdf

Forschungsvereinigung Antriebstechnik e.V.: Leistungspotentiale des Kühlschmierstoffeinsatzes beim Wälzfräsen. FVA-Forschungsheft Nr. 1278, Frankfurt, 2018

Cselle, T.: Trockenbearbeitung beim Wälzfräsen. Tagungsband zum Seminar „Trockene Zahnradvorbereitung", Aachen, 15.-16.02.2002, ADITEC, 2002

Kleinjans, M.: Wälzfräsen am Limit. Tagungsband zum Seminar „Trockene Zahnradvorbereitung", Aachen, 15.-16.02.2002, ADITEC, 2002

Forschungsvereinigung Antriebstechnik e.V.: Bearbeitung extrem schwefelarmer Stähle durch Bohren und Wälzfräsen. FVA-Forschungsheft Nr. 948, Frankfurt, 2010

Karpuschewski, B.; Beutner, M.; Köchig, M.; Wengler, M.: Cemented carbide tools in high speed gear hobbing applications. CIRP Annals - Manufacturing Technology 66 (2017) S. 117120,

Klocke, F., Kauffmann, P., Schalaster, R., Stuckenberg, A.: Comparison of PM-HSS and Cemented Carbide Tools in High-Speed Gear Hobbing. Gear Technol. 09/10 (2009) S. 57-59

Sari, D.; Troß, N.; Löpenhaus, C.; Bergs, T.: Development of an application-oriented tool life equation for dry gear finish hobbing. Wear . 1563-1572,

Bausch, T.: Innovative Zahnradfertigung. 5. Auflage, Expert Verlag, Renningen 2015, ISBN 978-3-8169-3280-2

Kühn, F., Hendricks, S., Troß, N. et al. Experimental analysis on the influence of the tool micro geometry on the wear behavior in gear hobbing. Int J Adv Manuf Technol 126, 1279-1292 (2023).

Karpuschewski, B., Knoche, H.-J., Hipke, M., Beutner, M. High Performance Gear Hobbing with powder-metallurgical High-Speed-Steel, Procedia CIRP, Vol. 1, 2012, pp. 196-201, .

Weck, M.; Hurasky-Schönwerth, O.; Winter, W., M. Manufacturing simulation for the analysis of the gear hobbing process, In: International Conference on Gears, March 13-15, 2002 / VDIGesellschaft Entwicklung Konstruktion Vertrieb, pp. 145-159., 2002

Weck, M., Klocke, F., Winter, W., & Winkel, O. Analysis of Gear Hobbing Processes by Manufacturing Simulation Production Engineering. In Research and Development. WGP Berlin, 2003

Karpuschewski, B., Beutner M., Köchig M., Wengler M. Cemented carbide tools in high speed gear hobbing applications, CIRP Annals,Volume 66, Issue 1, 2017, Pages 117-120,

Schalaster, R., Optimierung des Fertigwälzfräsens von Verzahnungen. Ph.D. Thesis, RWTH Aachen University, Aachen, 2012.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 61-70

doi: 10.4028/p-LO2XLh

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-12

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-15

Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

Nils Paucke 1,a*, Martin Beutner 1,b, Andreas Lümkemann 2,c, Keith Thomas 2,d, Martin Učík 3,e, Hamid Bolvardi 2,f, Matthias Hackert-Oschätzchen 1, g

1 Chair of Manufacturing Technology with Focus Machining, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

2 PLATIT AG, Eichholzstrasse 9, 2545 Selzach, Switzerland

3 PLATIT a.s., Průmyslová 3020/3, 78701 Šumperk, Czech Republic

a  nils.paucke@ovgu.de, b  martin.beutner@ovgu.de, c  a.luemkemann@platit.com,

d k.thomas@platit.com, e m.ucik@platit.com, f h.bolvardi@platit.com, 9 matthias.hackert-oschaetzchen@ovgu.de

*corresponding author

Keywords: dry gear hobbing, wear, crystal structure, fly cutting test.

Abstract

Dry machining of gears demands advanced coating technologies to withstand high thermal and mechanical stresses. In this study, AlCrN coatings were deposited using the newly developed Focused Magnetron Sputtering (FMS) process and compared with conventional Cathodic Arc Evaporation (CAE)-AlCrN and boron-containing CAE-AlCrBN coatings. XRD analysis showed that FMS produced a fine-grained crystal structure with half the full width at half maximum (FWHM) of CAE-AlCrN. Stress-optimised deposition allowed a 60% higher coating thickness with improved adhesion. Analogy gear hobbing tests (fly cutting tests) demonstrated that FMS-AlCrN had 52% lower crater wear than CAE-AlCrN, while CAE-AlCrBN also improved crater-wear resistance due to boron-induced grain refinement. However, both fine-grained coatings exhibited increased flank wear compared to the coarse-grained CAE-AlCrN coating. The results show that FMS enables the production of dense, fine-grained coatings with superior adhesion and crater-wear resistance, highlighting its potential for dry gear hobbing. Further optimisation of hardness and microstructure is required to balance crater and flank wear behaviour.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

Gear hobbing is recognised as one of the most productive processes in soft machining of gears, and in recent years, there has been a clear trend towards dry gear hobbing. With the increasing demand for gears, particularly in the automotive industry, which accounts for over 41% of the gear hobbing machines [1], the trend towards dry machining becomes increasingly apparent.

Eliminating the use of cutting fluids offers economic and environmental benefits. From an economic perspective, coolant-related costs typically range from 2−8% of total machining costs and can sometimes even surpass the costs of the tool [2]. Therefore, eliminating the need for cutting fluids offers significant potential for machining cost savings. From an environmental perspective, dry machining reduces emissions and prevents metal dust from binding with coolants [2], resulting in improved working conditions for the machine operator.

However, this transition imposes higher demands on the gear hob, mainly due to increased heat generation and challenges in chip removal, which result in faster tool wear. Consequently, advances in tool coatings are necessary. While the chemical composition, the thickness, and the architecture of coatings are traditionally considered as critical factors affecting the coating performance, recent research [3] suggests that the inherent crystalline structure of the coatings also has an impact on the coating performance. For this reason, this paper focuses on the influence of the crystal structure of AlCrN-based coatings, examining how these microstructural characteristics impact coating properties and wear during dry gear hobbing.

State of the Art

The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

Coating technologies for gear hobs can primarily be divided into Physical Vapor Deposition (PVD) and Chemical Vapor Deposition (CVD) processes. However, PVD processes have largely superseded CVD technology for coating gear tools [4] due to several advantages, including more variety in terms of coating composition, as well as reduced processing temperatures, which enable the coating of gear hobs made of PM-HSS. Consequently, PVD processes will be further discussed.

PVD technology involves transforming the target material, which is to be deposited, into atomic particles in a vacuum environment or gaseous plasma under low-pressure conditions. These particles are then directed towards the substrate, the cutting tool to be coated, where they condense to form a coating with desired properties [5,6]. The two most common methods for applying PVD coatings are evaporation and sputtering deposition, with Cathodic Arc Evaporation (CAE) being the leading deposition technology within the machining sector [7].

CAE operates by vaporising and ionising the target material in a high vacuum utilising an electric arc. The arc moves across the evaporator source connected as a cathode [8]. The CAE process is characteristic of a high ionisation degree of the evaporated material and a fast growth of the highly dense coatings. However, there is a main drawback of this coating technology. The CAE process tends to produce unwanted metallic droplets that can reduce the coating's density, increase the surface roughness, and act as stress concentrators, potentially leading to cracks [7, 9].

These limitations could be overcome utilising the sputtering process. In this process, an inert gas like argon is ionised under a high voltage in low-pressure plasma, allowing the charged gas ions to collide with the target material and knock off atoms through impulse exchange [8]. When utilising a magnetron, the magnetic field lines penetrate the target perpendicularly. Here, the superposition of magnetic and electric field lines causes the electrons to be deflected onto elongated spiral paths in the plasma, which lead to enhance ionisation probability and deposition rate [5,8]. A further developed process of magnetron sputtering with direct current (DCMS) is the High Power Impulse Magnetron Sputtering (HiPIMS) process. In the HiPIMS process a high power is applied in unipolar pulses, which results in a high plasma density and improved ionisation fraction, allowing for better control over the film growth [10].

Although the sputtering process and its current developments like HiPIMS have advantages over CAE in terms of droplet formation, they present a number of challenges, particularly in relation to achieving sufficient plasma densities to produce high-quality coatings on a large substrate area at an industrially practicable growth rate [7]. To overcome these challenges, PLATIT has developed a novel coating deposition technology known as Focused Magnetron Sputtering (FMS). This process is characterised by a longitudinal movement of a reduced size magnetron inside a turbular target, as shown in Fig. 1 a-b. FMS enables a sixfold increase of peak sputter power densities compared to conventional magnetron sputtering, as illustrated in Fig. 1 c. The power densities reached in the FMS process are characteristic of HiPIMS, yet with higher duty cycles, due to effective cooling in the tubular targets. This allows higher deposition rates and greater productivity. [9, 11, 12].

Recent research by Klimashin et al. [9] explored superstoichiometric (Al, Cr ) Nx coatings, revealing that small changes in stoichiometry significantly impact the microstructure and mechanical properties. Specifically, (Al,Cr)N1.08 exhibited densely packed, elongated grains with a (111) growth orientation, leading to increased hardness and abrasion resistance. In contrast, (Al,Cr)N 1.11 displayed a finer-grained structure with a (220) orientation, enhancing fracture toughness and resistance to microcracking, which is beneficial for impact-intensive applications like gear hobbing.

[picture]

Fig. 1. (a) Platit π411 chamber with schematised annular blue plasma generated by movable focussed magnetic field, (b) Schematics of magnetron assembly for FMS and DCMS mode and (c) Power densities achieved in FMS and DCMS mode at 25 kW according to [9].

Fig. 1. (a) Platit π 411 chamber with schematised annular blue plasma generated by movable focussed magnetic field, (b) Schematics of magnetron assembly for FMS and DCMS mode and (c) Power densities achieved in FMS and DCMS mode at 25 kW according to [9].

Building on these insights, this paper aims to utilise the newly developed FMS technology to influence the crystal structure of AlCrN coatings. Based on that, this study will analyse how the changes in crystal structure affect coating properties and wear resistance during gear hobbing.

Methodology

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The methods for determining the coating properties involve a series of standardised tests to evaluate chemical composition, thickness, hardness, and adhesion of the coatings. The chemical composition of the coatings was determined by applying Energy Dispersive X-ray Spectroscopy (EDX). This technique involves exciting the atoms in the coating with an electron beam of specific energy, which results in the emission of X-rays characteristic of the elements present. These characteristic X-rays provide detailed information about the elemental composition of the coatings [13].

The coating thickness is assessed utilising the Calotte Grinding Test in accordance with DIN EN ISO 26423 [14]. In this test, a steel ball moistened with an abrasive fluid is utilised to grind a calotte into the coating. The grinding process forms two concentric rings on the coating surface. To calculate the coating thickness, the diagonals of these rings are measured. The ground divots were prepared using a 30 mm diameter steel ball, a 0.25μ m diameter diamond suspension and a BAQ kaloMAX grinding unit. Micrographs of the ground divots were collected utilising a Carl Zeiss Axio Scope Vario optical microscope. Measurement of divot dimensions and calculation of coating thickness was performed using Platit PQCS software.

Coating adhesion was evaluated by applying visual inspection of Rockwell Hardness test indent according to the widely used DIN 4856 norm in which HF1 represents excellent adhesion and HF6 represents total delamination of the coating [15]. A Rockwell indenter manufactured by ESI Prüftechnik GmbH was applied to perform the indentation experiments. The inspection of the indents was performed utilising the same optical microscope used for the thickness measurements. The mechanical properties of the coatings themselves were assessed by applying a Fischerscope HM2000 nanoindenter.

The crystal structure of the coating was influenced by the newly developed FMS technology. Evaluation of crystal structure was performed by sawing through approximately 90% of the tungsten carbide substrate from the uncoated side and striking the sample with a hammer to generate a fracture surface through the remaining substrate and the coating. This fracture surface was inspected utilising a Zeiss EVO 15 Scanning Electron Microscope (SEM) to gain an impression of the coating microstructure. This microscope was equipped with an EDS detector (Xplore 30) from Oxford Instruments to evaluate the elemental composition of each coating. Furthermore, X-ray Diffraction (XRD) analysis was conducted to investigate the crystalline structure.

Alongside analysing the coating properties and crystal structure, the wear of the FMS-AlCrN coating was compared to CAE-AlCrN and CAE-AlCrBN coatings utilising the fly cutting test. The inclusion of the CAE-AlCrBN coating was motivated by the literature suggesting that boron addition can effectively reduce grain size [16,17,18]. The applied fly cutting test is an analogy test of the real gear hobbing process. Here, the gear hobbing process is characterised by a high number of cutting edges, which results in a high tool life [19]. The high productivity and tool life of gear hobs are advantageous for industry due to its cost-effectiveness but disadvantageous for basic research, as several 100 to 1,000 workpieces have to be manufactured before significant wear occurs [20]. Based on this, the fly cutting test was developed as an analogy test for research purposes. In this wellestablished test, the tool is reduced to a single tooth, thereby reducing the time and cost of the investigation compared to tests with a full gear hob [19]. The fly cutting tooth assumes all generating positions of the full gear hob one after the other. Based on this, the mechanical cutting load of a shifted full gear hob is correctly reproduced, and the manufactured gear is geometrically identical to the real process [20]. The experimental setup utilised for the fly cutting test is shown in Fig. 2.

[picture]

Fig. 2. Experimental setup: Fly cutting test on Liebherr LC180 gear hobbing machine.

Fig. 2. Experimental setup: Fly cutting test on Liebherr LC180 gear hobbing machine.

The tests were conducted on a Liebherr LC180 gear hobbing machine. The tool substrate was made of PM-HSS (S390), and the workpiece material was made of 20 MnCr 5 ( 1.7168 ). The tests were conducted at a cutting speed of vc=220 m/min and a feed rate of fa=6.4 mm/rev ( hcu,max,Hoff =0.24mm). The flank and crater wear were measured with an optical microscope from Mitutoyo and with an optical coordinate measuring machine μCMM from Alicona. The tests were stopped when a maximum width of the flank wear land of 130μ m or a maximum crater wear of 90μ m was reached. In this study each coating performance test was conducted once, as initial tests that were performed multiple times have shown consistent and reproducible results.

Results

The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

All coatings were deposited utilising composite targets with a fixed Al:Cr ratio. The targets used for the FMS and CAE coatings were nominally identical, with EDX results consistently resulting in stoichiometric AlCrN with an Al:Cr ratio of around 1.6:1. For coatings containing boron, 10at.% boron was incorporated into the target. Through appropriate selection of deposition parameters, the boron content of the coating between 2 and 4at.% could be achieved. Precise quantification of the boron content applying EDX was not possible.

Table 1 summarises the measured coating properties, including thickness, hardness and adhesion for the investigated coatings. Due to the higher plasma density and energy input characteristic of the FMS process, a significantly thicker coating could be deposited compared to the conventional CAE process. In the present case, the FMS coating exhibits approximately 60% greater coating thickness than the CAE-AlCrN coating. Despite the increased thickness, the FMS coating demonstrates a better adhesion class (HF2) compared to the CAE-AlCrN coating (HF3). This improvement indicates that the higher energy densities of the FMS process enable stress-optimised deposition, i.e., lower intrinsic stresses within the coating. Consequently, thicker coatings can be produced without deterioration of adhesion.

Table 1. Overview of coating properties for FMS-AlCrN, CAE-AlCrN and CAE-AlCrBN coatings.

Coating properties | Unit | FMS-AlCrN | CAE-AlCrN | CAE-AlCrBN
Coating thickness | μm | 5.98 | 3.78 | 4.03
Hardness | GPa | 35.7 | 36.9 | 33.9
Adhesion class | - | HF 2 | HF 3 | HF 2

Comparing the XRD spectra for FMS-AlCrN and CAE-AlCrN in Fig. 3, shows that the predominant texture for the FMS-AlCrN is (200), as compared to the (111) texture observed in CAEAlCrN. XRD analysis revealed that the full width at half maximum (FWHM) of the diffraction peaks for FMS-AlCrN is also broader, suggesting a finer crystallite size. This observation is confirmed in the cross-sectional images of the coatings.

[picture]

Fig. 3. X-ray diffractograms (Bragg-Brentano geometry).

Fig. 3. X-ray diffractograms (Bragg-Brentano geometry).

Fig. 4 shows the fracture surface of the coatings after cleaving. The FMS-AlCrN coating exhibits a much finer crystalline structure while the CAE-AlCrN has a larger grain size and columnar growth pattern. As expected, the CAE-AlCrN also contains a significant number of macroparcticles and other growth defects typically associated with arc evaporation, while the FMS-AlCrN has fewer growth defects.

[picture]

Fig. 4. Fracture cross section for (1) FMS- AlCrN and (2) CAE-AlCrN.

Fig. 4. Fracture cross section for (1) FMS- AlCrN and (2) CAE-AlCrN.

The macroparticles are especially obvious in the plan-view micrographs seen in Fig. 5. Interestingly, the surface topology of the FMS-AlCrN reflects the underlying fine-grained microstructure with a number of small crystalline facets visible.

[picture]

Fig. 5. Surface morphology for (1) FMS-AlCrN and (2) CAE-AlCrN.

Fig. 5. Surface morphology for (1) FMS-AlCrN and (2) CAE-AlCrN.

Fig. 6 illustrates the crater wear on the rake face for the FMS-AlCrN, CAE-AlCrN, and CAEAlCrBN coatings after constant cutting lengths of 3.1 m/ tooth and 9.4 m/ tooth . After a cutting length of 3.1 m/ tooth, the measured crater wear depths are 8μ m for FMS-AlCrN, 14μ m for CAE-AlCrN and 11μ m for CAE-AlCrBN. For all three coatings, the wear is located close to the cutting edge, precisely at the transition between the leading flank and the tip. At this stage, the wear patterns are therefore comparable in shape and extent.

After a cutting length of 9.4 m/ tooth, the differences between the coatings become evident. The crater wear increases to 50μ m for FMS-AlCrN, 104μ m for CAE-AlCrN and 28μ m for CAEAlCrBN . For the CAE-AlCrN coating, the wear criterion is exceeded. The maximum crater wear is located on the leading flank, the region where wear initiation was first observed. On the opposite side of the rake face, at the transition from the trailing flank to the tip, a secondary crater has formed with
a maximum depth of 53μ m, which is roughly half the depth of the primary wear zone. The formation and merging of both craters were already detected after 4.7 m/ tooth.

In contrast, the FMS-AlCrN coating shows significantly improved wear resistance. The maximum crater depth after 9.4 m/ tooth is 50μ m, corresponding to a 52% reduction compared to the CAEAlCrN coating. The wear region remains similar in shape and location to that after 3.1 m/ tooth, and no second crater area has formed.

The CAE-AlCrBN coating containing boron exhibits a maximum crater depth of 28μ m after 9.4 m/ tooth, demonstrating a marked improvement over CAE-AlCrN. A second wear zone develops on the opposite side of the rake face. However, the two craters do not merge. The second crater appears after a cutting length of 7.8 m/ tooth and shows a depth of 24μ m, slightly lower than that of the primary wear area.

Overall, both the finer microstructure of the FMS-AlCrN coating and the addition of boron in the CAE technology result in a significant reduction of crater wear. The reduced crater formation and absence or delayed development of secondary wear regions indicate improved crater wear resistance for a finer grained microstructure under dry gear hobbing conditions.

[picture]

Fig. 6. Crater wear for FMS AlCrN, CAE-AlCrN and CAE-AlCrBN at a cutting length of 3.1 m/ tooth and 9.4 m/ tooth.

Fig. 6. Crater wear for FMS AlCrN, CAE-AlCrN and CAE-AlCrBN at a cutting length of 3.1 m / tooth and 9.4 m / tooth.

Fig. 7 shows the evolution of the maximum flank wear land (VB) on the leading flank as a function of the cutting length. The initial wear is the lowest for the CAE-AlCrBN coating with 16μ m, followed by CAE-AlCrN with 28μ m and FMS-AlCrN with 38μ m. Between cutting lengths of 4.7 m/ tooth and 7.8 m/ tooth, the increase of the maximum VB of both arc-deposited coatings is comparable, whereas the FMS-AlCrN coating consistently exhibits values that are approximately 25μ m higher. From a cutting length of 7.8 m/ tooth onwards, the wear on the leading flank rises more rapidly for the CAE-AlCrBN coating than for the CAE-AlCrN coating, resulting in nearly identical VB for CAE-AlCrBN (114μ m) and FMS-AlCrN (110μ m) after 9.4 m/ tooth. At a cutting length of 10.9 m/ tooth, the VB of FMS-AlCrN reaches 146μ m, thus exceeding the wear criterion. At the same cutting length, the CAE-AlCrBN coating shows a smaller VB of 119μ m. Notably, the maximum wear for CAE-AlCrBN occurs further away from the tip compared to the FMS-AlCrN coating. In contrast, the CAE-AlCrN coating exhibits a maximum VB of 81μ m, with the wear zone located close to the tip, which corresponds to the position of the crater wear observed previously.

[picture]

Fig. 7. Width of the flank wear land on leading flank for FMS-AlCrN, CAE-AlCrN and CAEAlCrBN coating.

Fig. 7. Width of the flank wear land on leading flank for FMS-AlCrN, CAE-AlCrN and CAEAlCrBN coating.

Based on the wear analysis on the leading flank, grain refinement achieved either through FMS technology or through the addition of boron has a potentially negative influence on flank-wear resistance. For both FMS-AlCrN and CAE-AlCrBN coatings, the wear criterion was reached primarily due to abrasive flank wear. Conversely, the coarser-grained CAE-AlCrN coating demonstrated lower flank wear and remained clearly below the critical wear threshold VBmax  at the end of the cutting test.

Discussion and Outlook

The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

With the newly developed Focused Magnetron Sputtering technology, it was possible to produce an AlCrN coating with a fine-grained crystal structure. XRD analysis revealed that the FWHM of the FMS-AlCrN coating was only about half of that of the conventional CAE-AlCrN coating, indicating a significant grain refinement. In addition, stress-optimised deposition by FMS enabled a considerably higher coating thickness while simultaneously improving adhesion compared to the CAE-AlCrN coating. Contrary to expectations, the finer microstructure did not result in an increase in coating hardness. To further investigate the influence of grain refinement, an additional CAE-AlCrBN coating was examined during the fly cutting tests, as literature reports suggest that the addition of boron can promote finer grains.

The wear experiments demonstrated that a finer crystalline structure improves crater-wear resistance. This effect can be explained by the longer crack propagation paths in fine-grained microstructures, which delay crack growth and material removal. In contrast, the coarse-grained CAE-AlCrN coating exhibited higher abrasion resistance against flank wear than both the FMS-AlCrN and the CAE-AlCrBN coatings. A possible explanation is that fine-grained structures may be more easily polished away near the cutting edge, enhancing abrasive wear. Another contributing factor could be the lower hardness measured for both FMS-AlCrN and CAE-AlCrBN coatings, which may have promoted flank wear.

In future studies, the influence of microstructure on flank wear should be systematically investigated by comparing coatings with similar hardness but different grain sizes. If the results reveal that hardness has the predominant effect, the next step should be the development of an FMS-deposited coating combining both fine-grained structure and increased hardness to simultaneously meet the requirements for abrasion resistance and crack resistance. If, however, the microstructure proves to be the dominant factor, an optimal "sweet spot" in grain size and coating architecture must be determined to balance crater and flank wear performance.

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The original version of this paper is available on https://www.scientific.net/DDF.450.61.pdf

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 71-81

doi: 10.4028/p-wko6TW

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-16

Revised:
2026-02-15

Accepted:
2026-02-22

Online:
2026-04-15

Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

Mikel Etxebeste 1,a*, Iñaki M. Arrieta 1,b, Gorka Ortiz-de-Zarate 1,c and Pedro J. Arrazola 1,d

1 Mondragon Unibertsitatea, Faculty of Engineering, Loramendi 4, Arrasate-Mondragón, 20500, Spain

a* metxebeste@mondragon.edu, b  imarrieta@mondragon.edu, c  gortizdezarate@mondragon.edu, d pjarrazola@mondragon.edu

*corresponding author

Keywords: machining, boring, tuned mass damper (TMD), FEM, chatter, modeling.

Abstract

Sectors such as energy, aerospace, and heavy machinery increasingly rely on the machining of large components, where boring bars can easily exceed 200 mm in diameter and reach length-todiameter ratios of up to 14 . In these operations, chatter remains the dominant limitation due to the inherently low dynamic stiffness of such long tools. While Tuned Mass Dampers (TMDs) are widely applied in small and medium-sized boring bars, but transferring this technology to large-scale tools introduces significant challenges, particularly in the selection and tuning of damper components and the difficulty of evaluating performance prior to manufacturing. Because producing large boring bars is costly, a structured and predictive design strategy is essential to avoid trial-and-error iterations. This work introduces a scaling methodology that adapts TMD-integrated boring bar designs to large dimensions, providing a systematic approach to predict dynamic behavior across different tool sizes. The methodology is demonstrated through a case study involving ∅200 mm boring bar with length of 14 times the diameter. Experimental validation with the manufactured prototype confirms that the proposed scaling strategy enables effective chatter suppression and offers a practical path for extending TMD technology to large-scale boring applications.

1. Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

Industrial sectors such as energy, aerospace, and heavy machinery increasingly demand larger and more complex components to improve performance and reliability. Internal turning of these parts often requires boring bars with long overhangs, where geometric accuracy and surface quality depend heavily on the tool's dynamic behavior [1]. As tool dimensions scale with component size, the inherent reduction in stiffness makes the boring bar the main limitation in machining performance, particularly due to its susceptibility to chatter [2]. Ensuring effective vibration suppression in large boring bars is therefore essential for maintaining productivity and process robustness.

Chatter remains a dominant constraint in deep boring operations [3]. It arises from the regenerative effect between tool passes, creating self-excited vibrations that degrade surface finish, reduce dimensional precision, and limit material removal rates [4,5]. Long and slender boring bars are especially vulnerable because increasing overhang reduces stiffness and natural frequencies, making the tool highly sensitive to cutting-force excitation [6]. For large boring bars, the significant mass further amplifies compliance, creating additional challenges for achieving both rigidity and manageable tool weight [7]. Stability Lobe Diagrams (SLDs) have been widely used to identify stable cutting regimes [6,8], yet modifying cutting conditions alone is rarely sufficient in large-scale internal turning, reinforcing the need for enhanced tool designs.

Numerous passive, semi-active, and active vibration suppression strategies have been explored, including Tuned Mass Dampers (TMDs) [9-12], magnetorheological systems [13-16], piezoelectric actuators [17-19], electromagnetic solutions [20-22], topology-optimized structures [23-25], and high-damping materials [26-28]. Among these, passive strategies are generally favored for industrial implementation due to their simplicity and reliability. TMDs are particularly prominent in boring applications [5,31]. They introduce a secondary mass-spring-damper subsystem tuned to the
dominant mode of the bar, and when properly adjusted, the TMD attenuates the resonance peak and increases dynamic stability [4,5]. Variants such as friction dampers [32], impact absorbers [33], Lanchester systems [34,35], and eddy-current dampers [36,37] have also been investigated, but TMDs remain the preferred solution for industrial use because of their robustness and straightforward integration [38]. Commercial TMD bars perform well for smaller diameters and overhangs up to L=10×∅ [38], but scaling this technology to very large boring bars introduces challenges that current literature only partially addresses.

When tool dimensions exceed conventional ranges, directly scaling TMD solutions from smaller bars becomes ineffective. At large scales, the dynamic response is dominated by mass distribution, internal architecture, and limitations in achievable tuning frequencies. Empirical prototyping is costly and often impractical, making predictive scaling strategies essential. Analytical models remain common in boring bar design [ 3,8 ], but they offer limited fidelity for representing complex internal layouts or supporting iterative optimization. Finite Element Method (FEM) approaches, by contrast, have proven effective for designing TMD-equipped tools and predicting chatter behavior [39,40]. Although FEM has been applied to small TMD boring bars [40], composite designs [41], and stability boundary estimation [42], the literature still lacks an integrated framework for large-scale tools. To date, no existing approach offers a unified and efficient methodology for the design of large boring bars equipped with TMDs.

This review highlights that despite extensive work on chatter suppression and TMD technologies, a consolidated scaling approach suitable for very large boring bars, particularly those exceeding 14×∅, has not been documented. The high manufacturing cost and demanding performance requirements of such tools make the absence of systematic design strategies especially problematic, as trial-and-error manufacturing cycles are unrealistic in industrial environments.

To address these gaps, this work presents a scaling methodology for large boring bars incorporating TMD. The approach provides a structured framework to extrapolate TMD concepts to larger dimensions and guide the design of scalable configurations. The methodology includes the analysis of boring bar and TMD scaling relations, TMD design and tuning for large-scale applications, and experimental validation. A case study involving ∅200 mm bar with 14×∅ overhang is used to demonstrate the approach, including prototype manufacturing and testing. Results show that the proposed scaling methodology offers an effective path for extending TMD technology to large boring bars, enabling stable machining performance across tool sizes.

2. Methodology

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

The proposed scaling methodology provides a structured framework for designing large boring bars equipped with TMDs. It integrates the analysis of dynamic behavior across varying bar diameters and lengths with TMD configuration, enabling standardized procedures for future tool development while minimizing modeling and validation efforts. Analytical models are employed to efficiently predict dynamic responses and extract modal parameters necessary for TMD tuning. The methodology also introduces a TMD design suitable for large boring bars and adaptable to different tool geometries. Overall workflow, consists of four main steps (Figure 1):
(i) Case study definition: Each boring bar is defined by its diameter ( ∅ ), overhang length ( L ), and key design characteristics relevant to scaling and TMD integration. This step establishes the baseline configuration for the subsequent analyses and design procedures.
(ii) Boring bar scaling analysis: Analytical formulations are applied to assess bar stiffness and natural frequencies across different diameters and lengths. The methodology focuses primarily on bars with lengths around L=14×∅ and diameters between 100 mm and 500 mm . Slenderness is evaluated to determine whether the geometry can be reliably scaled from previous analyses, while TMD requirements are concurrently considered to ensure proper integration.
(iii) TMD design and tuning: Based on the predicted modal parameters from the scaling analysis, the TMD is designed and tuned. Flexible couplings are characterized, and the TMD mass is calculated to maximize vibration suppression according to the selected tuning criteria. The
dynamic Frequency Response Function (FRF) of the boring bar, both with and without the TMD, are compared to verify effectiveness and confirm the design's ability to mitigate chatter.
(iv) Experimental validation: The final step involves experimental verification of the methodology and the designed boring bars. Experimental Modal Analysis (EMA) through tap-testing is conducted to characterize the dynamic behavior at various overhang lengths, confirming that the scaling methodology and TMD design achieve the intended vibration control.

Boring bar scaling methodology

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

(i) Case study definition: Large boring bar design characteristics

[picture]

Fig. 1. Scaling methodology for optimized large boring bars with TMD and its experimental validation.

Fig. 1. Scaling methodology for optimized large boring bars with TMD and its experimental validation.

2.1. Case Study Definition

The scaling methodology was developed and validated through a real case study involving a boring bar with a length-to-diameter ratio of L=14×∅, a diameter of ∅200 mm, and an overhang length of L=2800 mm. The analysis was conducted at this constant ratio to isolate tool diameter as the primary scaling variable, since diameter and length effects do not scale linearly in large boring bars. Scaling analysis and dynamic evaluation were performed for both tool configurations to assess feasibility and dynamic performance.

The study focused on the critical bending mode of the boring bars, as torsional and axial responses were found to have a negligible influence. Figure 2 shows representative designs of the case study bar, highlighting the main cylindrical bar, the end caps, the tool-holder with cutting insert, and the integrated TMD assembly.

The employed clamping system was a double-clamping configuration, which is commonly used for large boring bars. The first clamp is wider (a) and the second narrower (b), with an adjustable spacing (λ) between them. In industrial practice, the same clamping fixture can be reused for different bar diameters by employing sleeves, ensuring flexibility across tool sizes.

[picture]

Fig. 2. Case study boring bar definition.

Fig. 2. Case study boring bar definition.

Key parameters for the boring bars, including geometric dimensions, material properties, and clamping dimensions, are summarized in Table 1. These parameters provide the basis for scaling analysis, predicting dynamic behavior and guiding the TMD design and tuning process.

Table 1. Case study definition and material properties.

Parameter | Value
Boring bar:
    Ø (mm) | 200
    L (mm) 14×Ø | 2800
Clamping
    a (mm) | 620
    b (mm) | 100
    λ (mm) | 1200
Material properties:
    Density, ρ (kg/m³) | 7800
    Young's modulus, E (GPa) | 210
    Poisson's ratio, ν (-) | 0.28

2.2. Scaling Analysis

The scaling analysis of the defined boring bars was performed using analytical formulations to evaluate stiffness ( k ) and natural frequency ( ωn ) as functions of diameter ( ∅ ) and length ( L ). Both bars were modeled as Euler-Bernoulli cantilever beams with a constant circular cross-section (Equations (1)-(2)), neglecting additional structural features such as tool-holder or end caps. Material properties are reported in Table 1.

k=Fδ=3·E·IL3=3·E·π·∅4L3·64ωn=kmeff=E·∅2ρ·16·12π·(kiL)2

(1)(2)

where δ is the tool-tip deflection under an applied force F,E is the Young's modulus of the bar, I is the moment of inertia, ρ is the material density, meff  represents the fraction of the bar's mass actively participating in the vibration mode, and ki is a mode-dependent factor (for the first bending mode, ki=1.875 ).

A sensitivity analysis was conducted to evaluate how variations in diameter and length affect stiffness and natural frequency. This analysis provides a systematic framework to predict performance trends and assess the scalability of boring bars, both for tools sharing the same length-to-diameter ratio and for tools with different geometric proportions. The results offer practical guidance for extending TMD design and tuning strategies across a wide range of industrial boring bar configurations.

2.3. Scalable TMD Design

A specific TMD was developed for the large boring bars in the case study. The TMD is positioned near the tool tip and consists of a mass supported by flexible couplings, fully immersed in damping fluid. Its dynamic behavior is modeled as a secondary mass connected to the bar through a spring ( k2 ) and a damper ( c2 ) (Figure 3). The complete assembly includes an external sleeve and end caps sealed with O-rings. When the bar vibrates, the flexible couplings allow the TMD mass to oscillate out of phase, dissipating energy through polymer coupling elasticity and viscous damping in the fluid.

[picture]

Fig. 3. TMD design for large boring bars and dynamic model.

Fig. 3. TMD design for large boring bars and dynamic model.

The TMD is tuned to the primary bending mode of each bar. The tuning process considers: (i) modal parameters of the bar (stiffness k1, natural frequency ω1, and effective modal mass m1* ), (ii) stiffness of the flexible couplings ( k2 ), which sets the required TMD mass ( m2 ), and (iii) geometric and material constraints for the TMD. Optimal tuning is achieved when the TMD natural frequency ( ω2 ) matches the bar's target frequency ( ω1 ), producing a counteracting inertial force that reduces the tool-tip vibration peak and enhances dynamic stability. The effective TMD mass is determined by Equations (3). Once defined, the mass ratio ( μ ) can be achieved to assess the tuning by Equations (3). The corresponding damping coefficient ( c2 ) for TMD simulation and validation was obtained using Equation (5), where ( ζ ) is the damping ratio.

f=ω2ω1→ for f=1:m2=k2ω12μ=m2m1*c2=2ζk2m2

(3)(4)(5)

where μ is the mass ratio being around 20% for enhanced vibration attenuation. The TMD mass geometry (diameter ∅TMD, length LTMD ) and material are iteratively selected to satisfy the required mass and fit within the available housing.

Dynamic performance is validated using FRFs computed with a substructure approach, which couples the bar's receptance with the TMD dynamics based on the model in Figure 3a. The design is confirmed when the initial vibration peak of the boring bar is effectively attenuated by the TMD. This approach also allows iterative sensitivity analyses of the TMD configuration and component selection, providing a reliable prediction of vibration reduction at the primary bending mode.

2.4. Experimental Validation Procedure

The proposed methodology was validated through prototype manufacturing and experimental testing. The bar was mounted in a Geminis L CNC lathe (maximum diameter 2 m and length of 10 m) using the described clamping configuration. EMA was conducted using tap-testing: the bar was excited with an impact hammer (Brüel & Kjaer Type 8206), and dynamic responses were recorded with an accelerometer (Brüel & Kjaer Type 4525-B) (Figure 4).

The TMD-integrated boring bar was tested at multiple effective lengths ( L′ ), starting from the target overhang of 14×∅(2800 mm) and sequentially reduced in 500 mm increments, resulting in four test configurations (L′=2800,2300,1800,1300). Measured FRFs were analyzed to assess TMD effectiveness across different lengths and to validate the predictive models by comparing experimental and simulated responses.

[picture]

Fig. 4. EMA of ∅200 boring bar.

Fig. 4. EMA of ∅ 200 boring bar.

3. Results and Discussion

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

The results obtained from the scaling methodology, TMD design and tuning, and the experimental validation were analyzed and discussed.

3.1. Scaling Analysis

The scaling analysis was first carried out using analytical expressions to estimate the stiffness and natural frequency of the boring bars as functions of ∅ and L. The resulting trends, shown in Figure 5, highlight a clearly non-linear relationship between geometry and dynamic behavior. This confirms that the performance of large boring bars cannot be reliably extrapolated with linear scaling rules, emphasizing the relevance of the proposed methodology.

[picture]

Fig. 5. Scalation analysis of boring bars in function of Ø and L: a ) stiffness, and b ) natural frequency.

Fig. 5. Scalation analysis of boring bars in function of Ø and L: a ) stiffness, and b ) natural frequency.

The results reveal that both stiffness and the first bending frequency drop sharply when the diameter decreases or the length increases. Beyond a certain slenderness level, however, the response begins to flatten, indicating that once the bar becomes extremely slender, further geometric changes have a comparatively small influence on dynamics. In contrast, within the moderate-slenderness region, the steepest portion of the curves, even small geometric adjustments generate significant improvements in stiffness, offering a favorable window for internal design optimization for example.

The sensitivity study reinforces that diameter is the dominant parameter influencing dynamic behavior, producing far greater improvements than comparable adjustments in length. As a result, moderate increases in diameter consistently deliver a more substantial gain in stiffness and frequency than reducing the overhang.

Based on these findings, the 14×∅ scaling study was completed for the two target diameters bar (Ø200). It should be noted that if different length-to-diameter ratios are required, the scaling factors derived may not remain applicable.

3.2. TMD Design Tuning

The TMD was first tuned for case study boring bar following the stablished methodology. The process began with the selection and experimental characterization of the flexible couplings to determine their effective stiffness k2. The couplings were tested under bending in the same direction as in the TMD assembly: the upper side was fixed, the lower side was connected to the TMD cap mounted on a dynamometer, and controlled displacements were applied using a machine-tool. The resulting reaction forces allowed quantification of the coupling stiffness, yielding k2=0.2 N/μm.

With the coupling stiffness defined, the TMD mass (m2) was determined using the tuning criterion that matches the absorber frequency to the natural frequency of the boring bar. Based on the modal parameters in Figure 6(ω1=22 Hz), the boring bar required a 10 kg mass. The resulting mass ratios was approximately of 10%, valid for optimal dynamic attenuation. Achieving higher mass ratios would require stiffer couplings to allow larger masses without detuning, which were not available in this study. The simulated FRF with and without the TMD are shown in Figure 6. The TMD successfully reduces the original resonance peak, demonstrating effective energy extraction from the primary bending mode. The real part of the FRFs (Figure 6) further confirms these trends. The critical mode maximum peak and amplitude reduction closely match predictions.

[picture]

Fig. 6. Boring bar FRF with TMD vs noTMD for a) Magnitude of the FRF, and b) Real part of the FRF.

Fig. 6. Boring bar FRF with TMD vs noTMD for a) Magnitude of the FRF, and b) Real part of the FRF.

3.3. Experimental Validation

The prototype of the boring bar of ∅200 mm equipped with the TMD was tested experimentally at several overhang lengths ( L′ ), ranging from the target configuration of 2800 mm(14×∅) down to 1300 mm , in order to evaluate the dynamic behavior over a representative operating range. A comparison between the predicted and experimental FRFs at the largest overhang configuration shows that the experimentally measured natural frequency (Figure 7) was slightly lower than the predicted value (Figure 6) and exhibited higher damping, which is attributed primarily to the additional mass and higher attenuation introduced by the damping fluid and other TMD assembly components not explicitly represented in the analytical model. The frequency deviation remained
below 10%, and the reduced response magnitude indicates clear increase in actual damping, thereby confirming that the predictive models provide adequate accuracy for design and tuning purposes.

The experimental tests conducted across different overhang lengths revealed the expected increase in stiffness and natural frequency as the bar length decreased (Figure 7). As a result, shorter overhangs inherently exhibit significantly higher dynamic stiffness and reduced vibration amplitudes, even without considering the contribution of the TMD. For this reason, it is essential to distinguish between two effects observed in Figure 7; first, the structural stiffening effect caused by shortening the overhang, which shifts the natural frequency to higher values and inherently reduces the FRF magnitude; and second, the vibration attenuation effect produced by the TMD, which is most effective when the excitation frequency coincides with the tuned natural frequency at 14×∅ configuration.

The reduced FRF magnitude at shorter overhang lengths is primarily due to increased structural stiffness rather than improved TMD performance. Since the absorber was tuned to the natural frequency of the 14×∅ configuration, its effectiveness decreases as the bar stiffens and the natural frequency shifts, leading to detuning and reduced damping capability. Although shorter overhangs would typically be machined using different boring bars in industrial settings, these results provide valuable insight into how bar length affects dynamic behavior and TMD performance in large tools. Specifically, the results demonstrate that optimal TMD performance in large-scale boring applications requires tuning to the dominant bending mode corresponding to the intended overhang length.

[picture]

Fig. 7. Experimental FRF results across the different lengths (L') with the boring bar of ∅200.

Fig. 7. Experimental FRF results across the different lengths (L') with the boring bar of ∅ 200 .

4. Conclusion

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

A scaling methodology for the development of large boring bars with integrated TMDs has been presented and validated. The approach was applied and experimentally validated on a boring bar with a diameter of ∅200 mm and a length-to-diameter ratio of 14×∅. The main findings are:

The scaling analysis confirms that the external diameter is the dominant parameter governing the dynamic behavior of large boring bars. For tools sharing the same diameter-to-length ratio, geometry can be reliably scaled using the identified factors. However, TMD tuning does not follow a linear scaling law: it depends strongly on the stiffness of the flexible couplings, requiring diameter-specific tuning to maintain optimal vibration attenuation.

The proposed methodology provides a structured framework for optimizing boring bar design and integrating TMDs suitable for large tool geometries. The resulting TMD configuration demonstrates clear advantages when scaled to large boring bars, offering substantial improvements in dynamic performance while maintaining an efficient and practical design approach.

Experimental validation confirms the effectiveness of the methodology. The ∅200 mm prototype exhibited clear vibration attenuation at the target overhang, demonstrating that the TMD was correctly tuned for the intended operating length. Additional tests performed at shorter overhangs confirmed the expected variation in dynamic behavior and provided insight into the usable operating range of the boring bar.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/DDF.450.71.pdf

The authors hereby thank the financial support from the TAILORSURF (PID2022-139655OBI00), ORLEGI (KK-2024/00005), and MAND (ZL-2023/00856) projects, as well as the Basque Government for the predoctoral grant (PRE_2023_1_0134) and the research group support: Grupo de Excelencia A (IT1443-22).

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 83-91

doi: 10.4028/p-p7iF0w

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-26

Accepted:
2026-02-28

Online:
2026-04-15

Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

TAMURA Shoichi 1,a* and MATSUMURA Takashi 1, b

1 Tokyo Denki University, 5 Senjyu Asahi-cho, Adachi-ku, Tokyo, 120-8551, JAPAN

Keywords: Cutting Force, Temperature Distribution, Thermoplastic

Abstract

Carbon fiber reinforced thermoplastic (CFRTP), such as carbon fiber reinforced polyetheretherketone (CF/PEEK), are applied in aerospace structures because of their high specific strength and recyclability. In this study, cutting tests were conducted to investigate cutting force in drilling of CF/PEEK composites. The lower cutting force was measured at the higher spindle speed. Temperature distributions on the exit side of the hole were compared between two spindle speeds, the temperature at high spindle speed indicates a higher value. The different conditions on machined hole walls were observed between the two spindle speeds. Then, an energy based force model was applied to analyze the thrust and torque during drilling, in which the cutting edge was discretized, and the chip flow was determined to minimize cutting energy. Based on the predicted shear and friction works, a finite difference thermal analysis was performed to evaluate temperature distributions in the tool, chip, and workpiece. The analysis indicated that higher spindle speed leads to an increase in cutting temperature. The results suggest that temperature-dependent behavior of the thermoplastic matrix may influence the shear stress on the shear plane and thereby contribute to the reduction in cutting force at the higher spindle speed.

Introduction

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Carbon fiber reinforced plastic (CFRP) has been widely applied to aerospace structures because of their high specific strength and stiffness. Thermoset CFRP (CFRTS), particularly carbon fiber reinforced epoxy composite, has been the dominant material. In recent years, however, carbon fiber reinforced thermoplastic (CFRTP) has been increasingly used as alternative material to CFRPS due to their advantages in recyclability, repairability, and shortened manufacturing cycle times. A comprehensive overview of CFRTP applications in aerospace structures has been provided by Meng et al [1]. Among various CFRTP, high performance materials such as CF/PEEK have been used in aircraft structures due to their high toughness [2]. Although CFRTP components can be fabricated using near-net-shape forming processes, secondary machining operations remain indispensable for mechanical fastening. Drilling is a critical process to ensure dimensional accuracy for bolted and riveted joints [3].

Drilling of CFRP is inherently associated with challenges such as rapid tool wear and delamination, which deteriorate hole quality. Extensive experimental studies on thermoset CFRP, particularly CF/epoxy composites, have clarified the strong relationship between thrust force and drilling-induced damage, including delamination [4]. Drilling temperature has been investigated using infrared thermography and thermocouples. Fu et al. [5] reported detailed temperature characteristics in drilling of unidirectional and multidirectional CFRP, emphasizing the importance of thermal effects.

In contrast, CFRTP shows different machining behavior due to the deformation characteristics of thermoplastic matrices. The thermoplastic matrix has a higher capacity for plastic deformation during cutting, resulting in continuous chip formation [2][6], which is more commonly observed than in CFRTS.

Cutting temperature is an important factor influencing the cutting process, including cutting forces, tool wear, and surface finish and integrity, because cutting energy is transferred into heat through shear deformation and friction in CFRTP machining. Experimental studies have shown that drilling
temperatures in CF/PEKK tend to be higher than those in CF/epoxy under comparable cutting conditions [3]. Previous experimental studies have indicated that when the machining temperature approaches or exceeds the glass transition temperature of the polymer matrix, significant deterioration in surface quality and integrity can occur in CFRTP machining [7][8]. He et al. [9] reported that cryogenic pre-cooling during drilling of CF/PEEK significantly reduced the drilling temperature below the glass transition temperature, resulting in reduced exit damage and improved hole wall integrity, although the cutting force did not decrease at low temperature due to the inherently high stiffness and deformation resistance of the material.

Numerical approaches with thermo-mechanical coupling have been applied to CFRTP drilling. Ge et al. [10] combined temperature-controlled orthogonal cutting experiments with finite element simulations to investigate the effects of prescribed workpiece temperature and fiber orientation on the cutting behavior of CF/PEKK. Liu et al. [11] developed a multi-scale thermo-mechanical coupling model for woven CF/PEEK composites, considering anisotropic heat conduction to predict temperature distributions during drilling. In their approach, the heat source was defined primarily based on frictional heating on the flank face using the measured thrust force.

In drilling processes, edge geometry, including the rake angle, and cutting speed change significantly along the cutting edge from the chisel to the outer lip; therefore, cutting forces and heat generation depend on the position along the cutting edge. For CFRTP, temperature analysis based on cutting mechanics using shear plane model [12] is suitable for investigating local thermal behavior along the cutting edge, because matrix shear deformation plays an important role in chip formation and heat generation is closely linked to cutting forces.

In this study, the cutting force and the temperature behavior during drilling of CF/PEEK composite, requiring careful temperature control, are investigated experimentally and numerically to clarify their trends. Cutting forces and temperatures of workpiece are measured under representative cutting conditions. To interpret the cutting force and temperature behavior, the cutting force and the cutting temperature are evaluated using an energy based force model coupled with a finite difference thermal analysis. This study suggests a link between thermo-mechanical behavior of the thermoplastic matrix and the reduction in shear stress predicted with an energy based force model.

Cutting Process in Drilling of CF/PEEK Composite

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

The experiments were designed to compare two representative spindle speeds in order to clarify the influence of thermo-mechanical behavior on the drilling process, rather than to establish a comprehensive parametric trend. By comparing two spindle speeds, the study aims to highlight the change in cutting force and the associated temperature behavior. The cutting tests were conducted in a 3 -axis machining center (FANUC, ROBODRILL α-D21MiB5), as shown in Fig. 1. An 8 mm diameter carbide drill was used for the tests, as shown in Fig. 2 and table 1. The drill was clamped to the spindle with a rotating piezoelectric dynamometer (Kistler, 9170B) to measure thrust and torque during the drilling. The workpiece material was a CF/PEEK laminate with a thickness of 4.2 mm . The stacking sequence was [+45/90/−45/0] ss. The workpiece was clamped on the machine table. The through hole drilling was conducted under dry condition at spindle speeds of 1194 and 5968 min−1 with a feed rate of 0.025 mm/ flute . The corresponding cutting speeds at the end of lip were 30 and 150 m/min, respectively. Prior to force measurement, four preliminary holes were drilled using a new tool to stabilize the cutting edge condition and minimize the influence of rapid initial wear. All measurements were then conducted using the same drill under stabilized conditions.

To experimentally examine the temperature difference between the two spindle speeds, infrared thermography was performed from the exit side of the plate immediately before the chisel exits the workpiece. The hole exit was observed with an infrared camera (NEC San-ei, TH9100B) inclined about 44∘ from vertical, and geometric correction was applied to the thermal images so that the hole appeared circular in the corrected images. To ensure reliable temperature measurement, the backside surface was coated with thermographic paint, and the emissivity was set to 0.94 in the infrared camera.

Fig. 3 shows the cutting force measurement in drilling of CF/PEEK composite at the two spindle speeds. The cutting force reflects the change in cutting area as the cutter feed, as follows:

[picture]

Fig. 1. Cutting test in drilling

Fig. 1. Cutting test in drilling

[picture]

Fig. 2. Drill employed

Fig. 2. Drill employed

Table 1. Tool parameters

Diameter [mm] | 8
Point angle [°] | 140
Helix angle [°] | 30
Web thickness [mm] | 1.7
Web inclination [°] | 140
Thinning manner | X type
Material | Carbide
Coating material | TiAlN

[picture]

Fig. 3. Cutting force measurements: (a) 1194 min−1 and (b) 5968 min−1.

Fig. 3. Cutting force measurements: (a) 1194 m i n − 1 and (b) 5968 m i n − 1 .

[picture]

Fig. 4. Temperature distribution measurements on exit side: (a) 1194 min−1 and (b) 5968 min−1.

Fig. 4. Temperature distribution measurements on exit side: (a) 1194 m i n − 1 and (b) 5968 m i n − 1 .

From O to A , the thrust increases quickly when the chisel edge and the thinning edge engage with the workpiece because of negative or small rake angle. Meanwhile, the torque remains small due to the small cutting radius.

From A to B, the thrust and torque gradually increase as the cutting area of the lip expands, where the rake angle is relatively large.

From B to C, the cutting force remains nearly constant in steady cutting process.

From C to D, The cutting force decreases as the cutting edge exits the backside of workpiece. At the beginning of steady cutting process, (after point B in Fig. 3) thrust and torque were lower at a spindle speed of 5968 min−1 than at 1194 min−1. In particular, the torque decreases from approximately 29.6 Ncm to 23.2 Ncm corresponding to a reduction of approximately 28%, whereas the thrust decreases from 178.7 N to 169.4 N , corresponding to a reduction of about 5%. The larger reduction in torque compared with thrust indicates that, under the present two cutting conditions, the tangential force component associated with chip formation is more sensitive to spindle speed than the thrust component.

Fig. 4 shows the temperature distribution measured on the backside of the plate at spindle speeds of 1194 and 5968 min−1, immediately before the chisel edge exits the workpiece. A high temperature

[picture]

Fig. 5. Machined surface characteristics: (a) surface morphology at 1194 min −1, (b) surface morphology at 5968 min−1, (c) cross-sectional profile at 1194 min−1, and (d) cross-sectional profile at 5968 min−1

Fig. 5. Machined surface characteristics: (a) surface morphology at 1194 min − 1 , (b) surface morphology at 5968 m i n − 1 , (c) cross-sectional profile at 1194 m i n − 1 , and (d) cross-sectional profile at 5968 m i n − 1

region was observed within the circular machining area under both conditions. The temperature near the center of the hole was slightly lower than that near the outer region of the machining area. The maximum temperatures were approximately 274∘C and 315∘C at 1194 and 5968 min−1, respectively.

Notably, the temperature at the higher spindle speed approached the melting temperature of PEEK approximately 343∘C. The higher temperature at the higher spindle speed suggests that temperaturedependent material behavior may contribute to the reduction in cutting force. The infrared measurement provides qualitative validation of the temperature increase at the higher spindle speed.

Fig. 5 shows the machined surface inside the hole wall observed around the mid-thickness of the plate. The surface observation was conducted at mid-thickness to avoid the influence of damage in penetration and exit processes and to focus on the steady cutting. At a lower spindle speed of 1194 min−1, the matrix resin remains around the fibers in the 0∘ orientation, and significant separation is not observed along the interlaminar interface. In contrast, at the higher spindle speed of 5968 min−1, exposed fibers are observed in the 0∘ fiber orientation region. Resin exudation along the interlaminar interfaces and small adhesion are also confirmed. The averaged surface profiles are shown in Fig. 5 (c) and (d), which are obtained from 50 cross-sectional profiles in the dashed region in Fig. 5(a) and (b). At lower spindle speed, although the surface exhibits relatively rough, the profile remains nearly level across different fiber orientations. In contrast, at the higher spindle speed, the profile of 0∘ region is located slightly below the surrounding surface. This suggests that part of the matrix resin was removed around the fibers.

These observations are consistent with the elevated temperature discussed in the previous section, which approached the melting temperature of PEEK. Although this discussion is qualitative, it suggests that thermal softening may have influenced the surface condition at the higher spindle speed.

[picture]

Fig. 6. Chip flow model in drilling

Fig. 6. Chip flow model in drilling

[picture]

Fig. 7. Cutting force simulations: (a) 1194 min−1 and (b) 5968 min−1.

Fig. 7. Cutting force simulations: (a) 1194 m i n − 1 and (b) 5968 m i n − 1 .

Cutting Force and Temperature Analysis in CF/PEEK Drilling

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

Cutting Force Model and Validation

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

Experimentally, a reduction in cutting force accompanied by an increase in cutting temperature was observed at the higher spindle speed. To interpret this tendency from a mechanical perspective, an analytical model is applied to predict the cutting force with the chip flow direction in drilling. Being presented in Reference [13], the force model in drilling is briefly described here. The cutting edges are divided into small discrete segments to consider the change in the tool geometry. The chip flow models are made on the chisel and the lip independently. The chip flow in each segment is interpreted as a piling up of the orthogonal cuttings in the planes containing the cutting velocities V and the chip flow velocities Vc, as shown in Fig. 6. The orthogonal cutting model is made by Eq. (1), which are acquired in the orthogonal cutting tests:

ϕ=exp(C00V+C01t1+C02α+C03)τs=exp(C10V+C11t1+C12α+C13)β=exp(C20V+C21t1+C22α+C23)Lc=t1exp(C30V+C31t1+C32α+C33)}(1)

where ϕ,τs and β are the shear angle, the shear stress on the shear plane and the friction angle. V,t1 and α are the cutting velocity, the uncut chip thickness and the rake angle, respectively.

When the chip flow angle is assumed, the orthogonal cutting models in the chip flow are made by Eq. (1). In the penetration and the exit processes of the edges, the inclination of the workpiece surface with respect to the cutting direction is also considered in the orthogonal cutting model. The cutting
energy is consumed into the shear energy in the shear plane and the friction energy on the rake face. Then, the cutting energy in the chip flow model is estimated using Eq. (1). Because the cutting energy depends on the chip flow direction, the chip flow angle is determined to minimize the cutting energy. Consequently, the cutting force is predicted in the determined chip flow model. The cutting force was simulated using following orthogonal cutting data for cutting of CF/PEEK composite and TiAlN coated carbide tool in this study:

ϕ=exp(−0.0646V+1.23×104t1+0.298α−1.51)τs=exp(−0.0292V−806t1+0.433α+18.9)β=exp(−0.0416V−5.53×103t1+0.474α+0.102)Lc=t1exp(0.0314V−3.13×104t1−0.391α+2.86)}(2)

Fig. 7 compares the simulated and the measured cutting forces in drilling of CF/PEEK composite at the same cutting conditions of Fig. 3. The simulated torque in steady process decreases from 43.9 to 40.8 Ncm with increasing spindle speed, corresponding to a reduction of approximately 7%. Although the reduction is smaller than that observed experimentally (approximately 28% ), the simulation results are consistent with the experimentally observed decrease in cutting force at the higher spindle speed.

The analytical estimation indicates a moderate decrease from 191 to 180 MPa in the shear stress on the shear plane at the end of lip with increasing spindle speed from 1194 to 5968 min−1. The reduction in shear stress at the higher spindle speed may be associated with the temperature rise in the cutting zone and the temperature-dependent behavior of the thermoplastic matrix.

Finite Difference Model for Cutting Temperature Analysis

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When the material is removed at a velocity v with a heat generation S, the temperature rise θ in the time t is given by the following equation, which associates the change in the temperature with the thermal conduction, the thermal convection and the heat generation:

ρc∂θ∂t=div(kgradθ)−div(ρc𝐯θ)+S(3)

where ρ,c and k are the density, the specific heat and the thermal conductivity, respectively. Although the drilling process includes the edge penetration and exit processes, the transient cutting processes are observed in a few rotations of the tool. Therefore, the steady process is discussed here. Eq. (3) in two-dimensional analysis is expressed as the following equation with substituting ∂θ/∂t=0 :

∂(ρcvxθ)∂x+∂(ρcvyθ)∂y=∂∂x(k∂θ∂x)+∂∂y(k∂θ∂y)+S(4)

where vx and vy are the component of the flow velocity in X and Y direction. Eq. (4) is discretized in all of the finite volume. Up-wind scheme is applied to discretization of the thermal convection. Then, the temperatures of all volumes are determined to satisfy those discretization equations in relaxation method (SOR) [14]. In the present study, the temperature analysis is performed based on an isotropic thermal conductivity model for simplicity of the two-dimensional formulation. Although CF/PEEK exhibits anisotropic thermal conductivity, an equivalent isotropic value is adopted to evaluate the temperature variation at both spindle speeds, rather than to predict exact local peak temperatures.

The mechanical works, which are consumed into the plastic work on the shear plane and the friction work on the rake face, are converted into the heat generation based on the stress distributions. The shear stresses are given as a uniform distribution on the shear plane. The friction stresses are given as a triangular or a trapezoidal distribution on the rake face. The trapezoidal distribution is

[picture]

Fig. 8. Temperature analysis model: (a) discrete elements and (b) stress distribution

Fig. 8. Temperature analysis model: (a) discrete elements and (b) stress distribution

Table 2. Thermal properties

Parameter | Workpiece CF/PEEK composite | Tool Carbide
Density [×10³kg/m³] | 1.598 | 14.8
Specific heat [J/kg/K] | 1885 | 210
Thermal conductivity [W/m/K] | 0.766 | 88.0

[picture]

Fig.9. Temperature distributions at end of lip: (a) 1194 min−1 and (b) 5968 min−1, (c) shear plane, and (d) rake face

Fig.9. Temperature distributions at end of lip: (a) 1194 m i n − 1 and (b) 5968 m i n − 1 , (c) shear plane, and (d) rake face

applied when the stress at the tool tip is larger than the shear stress on shear plane. In the analysis, only the heat conduction is considered in the tool. The heat convection with the heat conductions occurs in the workpiece and the chip. In dry cutting, the adiabatic boundaries may be applied to the machined surface and the backsides of the workpiece and the chip because the thermal conduction rate in the material is much faster than the heat transfer to atmosphere.

Cutting Temperature in drilling of CF/PEEK composite

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

Fig. 8 shows an example of the analysis model in an orthogonal cutting plane at the end of lip. Table 2 lists the thermal properties of the CF/PEEK composite and the tool. The thermal properties of the CF/PEEK composite were experimentally measured for the present material. The properties of the carbide tool were taken as standard values commonly reported for K10 grade cemented carbide. Although the thermal conduction of CF/PEEK composite depends on fiber orientation and thickness direction, an equivalent isotropic thermal conductivity of the workpiece is assumed by averaging the conductivities in the fiber-parallel, fiber-perpendicular, and thickness directions. It should be noted that the present analysis assumes steady cutting and does not consider thermal accumulation due to repeated engagement of the drill edge. In actual drilling, heat may accumulate inside the hole as the cutting edges repeatedly pass along the previously machined surface, which can lead to higher local temperatures than those predicted by the present model.

Fig. 9 compares the temperature distributions in drilling at spindle speeds of 1194 and 5968 min−1. At the higher spindle speed, elevated temperatures are observed near the cutting edge and in the chip region, as shown in Fig. 9(b). Fig. 9(c) shows the temperature distributions on the shear plane. At 5968 min−1, the maximum and average temperatures reach 226∘C and 176∘C, whereas at 1194 min−1 they are 134∘C and 118∘C, respectively. The results indicate a significantly higher temperature in the shear zone at the higher spindle speed. Fig. 9(d) shows the temperature distribution on the rake face along the distance from the cutting edge. The maximum temperatures appear at the cutting edge and reach 114∘C at 1194 min−1 and 226∘C at 5968 min−1.
Although the present thermal model may underestimate the absolute temperature because heat accumulation is not considered, the predicted temperature difference between the two spindle speeds is larger in the cutting zone than that observed on the backside surface. This suggests that the temperature contrast between the two spindle speeds may be larger near the actual cutting edge than that measured on the exit-side of the plate.

Conclusions

The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

This study investigated cutting force and temperature behavior during drilling of CF/PEEK composites and provides a thermo-mechanical interpretation of the cutting force reduction at the higher spindle speed.

The cutting force decreased at the higher spindle speed. The temperature on the backside of plate immediately before the chisel exits the workpiece approached the melting temperature of the matrix at the higher spindle speed. In the drilled hole, deterioration of machined surface was observed due to matrix depletion and exudation around fibers and interlaminar interfaces.

An energy based cutting force model was applied to analyze thrust and torque during drilling. The analysis indicated that the shear stress on the shear plane decreases at the higher spindle speed.

The cutting temperature was evaluated at the end of the lip by a finite difference thermal model. The temperature predicted in the cutting zone was significantly higher at the higher spindle speed.

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The original version of this paper is available on https://www.scientific.net/DDF.450.83.pdf

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 93-102

doi: 10.4028/p-2lx7wD

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-15

Static Finite Element Modeling of Milling in Flexible Composite Plates

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

Matthias Nutte 1,a*, Edouard Rivière-Lorphèvre 1, b, Valentin Dambly 1,2,c, Pedro-José Arrazola 3, d, Ismail Lazoglu 4,e, Aurélie Granjon 5,f and François Ducobu 1, g

¹Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, UMONS, Mons 7000, Belgium

2 IDEKO, Dynamics & Control departement, Arriaga kalea 2, E-20870 Elgoibar, Spain

3 Mechanical and Manufacturing Department, Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, Arrasate-Mondragón 20500, Spain

4 Manufacturing and Automation Research Center, Koc University, Istanbul 34450, Turkey

5 Sobelcomp SPRL. Rue de l'économie 13, 4431 Loncin, Belgium

a  Matthias.nutte@umons.ac.be,

Keywords: Machining, Milling, Trimming operation, Flexible parts, Composite materials

Abstract

This study presents a static finite element analysis of the milling of a flexible unidirectional glass fiber-reinforced polymer (UD-GFRP) plate. The workpiece is modeled as a clamped-free cantilever, with cutting forces evaluated independently of structural deflections and applied along the machined edge. SC8R continuum shell elements are employed to accurately represent throughthickness loading and bending behavior. A mesh sensitivity analysis is conducted to determine a suitable discretization, leading to a 64×56×8 element mesh. For the investigated configuration ( ψ=0∘,fz=0.04 mm/ tooth ), the out-of-plane displacement reaches approximately 120μ m near the free end of the plate, whereas in-plane displacements reach up to −75μ m. These in-plane displacements are greater than or equal to the nominal feed per tooth, indicating a highly significant influence on chip formation. This work provides a basis for understanding the structural response of flexible composite plates during trimming and emphasizes the need for coupled force-deformation formulations.

Introduction.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

Fiber-reinforced polymers (FRPs) are increasingly employed in industrial applications due to their high strength-to-weight ratio, good fatigue resistance, shock absorption capacity, and strong design flexibility [1]. The forming processes of FRP components frequently lead to thin-walled geometries, making their structural flexibility a critical factor during machining operations such as trimming. These finishing operations are often required to achieve the final dimensional tolerances of molded composite parts and are still commonly performed manually. To improve process control and reduce reliance on trial-and-error approaches, it is therefore essential to accurately model the trimming process.

On the one hand, the numerical simulation of flexible metal parts has been explored in numerous studies [1,2,3,4]. On the other hand, research has addressed the numerical simulation of FRP machining through comprehensive reviews [5] and cutting force modeling [6, 7]. Furthermore, specific phenomena well-documented in metal machining, such as chatter, have also been investigated experimentally in the context of composite materials [8].

Despite these advancements, to the authors' knowledge, a gap remains in the literature regarding the numerical simulation of trimming operations for flexible composite parts. While some studies have examined thin-walled composite elements experimentally [9], recent research has begun to address this by introducing numerical simulation frameworks for thinning flexible composite components[10].

The objective of this numerical study is to conduct a static analysis of the trimming process applied to a unidirectional glass fiber-reinforced polymer (UD-GFRP) composite part. The aim is to obtain a continuous displacement field of the plate, which represents the form error induced by the trimming operation. Under a simplified configuration, the workpiece is modeled as a clamped-free plate with a cantilever. Within this static framework, plate deformations are neglected in the computation of the cutting forces, which are assumed to be independent of the structural response. The plate displacement resulting from average cutting forces is then evaluated. This study is exclusively based on numerical simulations.

The static numerical methodology is first introduced through the coupling of Abaqus with a Python-based scripting framework. A mesh convergence study is subsequently carried out to assess the numerical accuracy of the model, and the simulation results are finally analyzed and discussed.

Method.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

The static analysis of the trimming operation, illustrated in Fig. 1, is based on several fundamental modeling assumptions. To simplify the numerical solution and separate the prediction of cutting force from the structural response, plate deformation is neglected in the instantaneous calculation of chip thickness. The cutting tool is assumed to behave as a perfectly rigid body, whose axis remains strictly perpendicular to the initial neutral plane of the plate throughout the operation. It is also assumed that the tool operates in a pure rolling trimming configuration, which means that only the rolling teeth are engaged in the cutting operation, without any plowing effects. The structure of the machine tool is modeled as perfectly rigid, and no parasitic displacement of the tool holder or spindle is considered. The workpiece is assumed to be perfectly clamped in a vise.

[picture]

Fig. 1. Schematic representation of the configuration

Fig. 1. Schematic representation of the configuration

The geometric parameters defining the numerical model are shown in Fig. 1. The sample is characterized by a machined length L and a cantilever length Wc, measured from the clamped area to the cutting edge. The axial cutting depth corresponds to the thickness of the sample t, while the radial cutting depth is denoted by ae. The boundary conditions are defined by considering the shaded area inside the vise jaws as fully clamped, with all degrees of freedom constrained. The average cutting forces are applied along a vertical line corresponding to the instantaneous position of the tool x. It should be noted that these loads are applied to the unmachined edge of the plate. This choice was made to simplify the case study. The shaded volume in Fig. 1 represents the theoretical material removed during the cutting process.

The numerical implementation of the model in Abaqus is illustrated in Fig. 2. The modeled geometry corresponds to the nominal specimen dimensions, with the specimen width defined as Wc; the actual material removal is not explicitly modeled. The plate is discretized using SC8R continuum shell elements, which are eight-node hexahedral elements with three translational degrees of freedom
per node [11]. This element formulation combines the computational efficiency of shell elements with a fully three-dimensional geometric description. Unlike classical shell elements such as S4R, which discretize only the mid-surface and represent thickness through section properties [11], continuum shell elements explicitly mesh the thickness using nodes distributed along the throughthickness direction. As a result, external loads can be applied directly at discrete z-locations rather than being converted into equivalent membrane forces and bending moments acting on a reference surface. This distinction is essential for the present study. During trimming, cutting forces are transmitted to the workpiece along the contact edge between the tool and the material, where the load is intrinsically distributed across the entire thickness of the plate. The SC8R formulations allow cutting forces to be applied independently to nodes located at different positions across the thickness at the point of contact, enabling a physically consistent representation of load transfer without relying on equivalent mid-surface resultants. This capability is not available in conventional shell formulations, limiting their suitability for modeling localized loads on edges in trimming operations. The mesh density is controlled by the parameters Nx,Ny and Nz, representing the number of elements along the longitudinal, transverse, and thickness directions, respectively. To reproduce the physical clamping conditions, the face located at y=0 is subjected to a fully fixed boundary condition. The loading case is defined by the cutting forces, which are converted into equivalent nodal forces and applied to the nodes distributed along the vertical edge at the tool position x, as specified in Fig. 2.

[picture]

Fig. 2. Finite element model showing the applied loads and boundary conditions

Fig. 2. Finite element model showing the applied loads and boundary conditions

As summarized in Fig. 3, the workflow begins by specifying the geometry, mesh, and boundary conditions. The setup process is fully automated through a Python script that generates the finite element model in Abaqus, allowing for total control over all modeling parameters. The resulting deformed shape is then calculated using the Abaqus Standard solver.

[picture]

Fig. 3. Diagram of the static analysis workflow

Fig. 3. Diagram of the static analysis workflow

The material behavior is modeled as a unidirectional fiber-reinforced polymer laminate. The elastic response is defined by the longitudinal Young's modulus E1 in the fiber direction, the transverse Young's modulus E2 in the plane perpendicular to the fibers, the shear modulus in the plane G12, the out-of-plane shear moduli G13 and G23, and the principal Poisson's ratio v12, which relates the
transverse strain in direction 2 to the longitudinal strain in direction 1. Although the formulation of the laminate allows for arbitrary stacking sequences and ply orientations, the present study focuses on a configuration in which all plies share the same fiber direction, resulting in transversely orthotropic behavior of the material throughout its thickness.

The cutting forces applied to the structure are calculated outside the finite element solver using a dedicated mechanical cutting model. This force model is based on experimentally identified specific cutting pressures and provides the full three-dimensional cutting force components. The cutting forces introduced into the finite element simulations correspond to the mean total force components (Fx,Fy,Fz) averaged over one complete revolution of the tool. Applying average forces allows the continuous value of the plate deformation to be obtained. [4]. The application of instantaneous cutting forces would require solving the dynamic response of the plate and accounting for tool-plate interaction, including the ploughing effect. This approach allows both in-plane and out-of-plane loading effects to be captured within the structural analysis. In accordance with the static framework adopted in this study, the deformations of the plate are not fed back into the cutting force calculation. Since Abaqus does not allow the direct application of continuous loads along edges, the total cutting force components (Fx,Fy,Fz) are converted into equivalent nodal forces and distributed among the nodes located along the plate thickness at the position x. Each node is therefore subjected to equivalent nodal force components ( fx,fy,fz ). This load transfer procedure is handled automatically by the external scripting framework. Because all unidirectional plies are aligned in the same fiber direction, identical specific cutting pressures are applied through the thickness, resulting in a uniform distribution of the equivalent nodal forces. For heterogeneous laminates or laminates with varying ply orientations, a non-uniform force distribution through the thickness could be implemented using the SC8R continuum shell formulation, which would not be feasible with conventional shell elements.

Once loading conditions are established, the Abaqus Standard solver performs a static finite element analysis to compute the displacement field. This allows for the evaluation of the threedimensional deformation of the specimen. This analysis is repeated for all tool positions x along the cutting edge, with simulations performed only at the nodal locations of the mesh, which correspond to the discrete positions at which the cutting forces are applied. This procedure enables the reconstruction of the three-dimensional deflection of the plate throughout the trimming operation.

Cutting Force Calculation.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

The cutting forces along these directions can be expressed using Eq. 1, proposed by Altintas and Lee [12]. This formulation distinguishes two types of coefficients: cutting force coefficients, which represent the specific resistance of the material to machining [13], and edge force coefficients, which account for the influence of the cutting-edge radius re as well as tool-workpiece interaction effects.

The cutting force coefficients Ktc,Krc, and Kac are multiplied by the axial depth of cut ap and the uncut chip thickness h. The axial depth of cut corresponds to the depth of material engaged during a single tool pass, whereas the chip thickness represents the thickness of material removed by the cutting edge. The edge force coefficients Kte,Kre, and Kae are multiplied only by the axial depth of cut ap.

(FtFrFa)=(KtcKrcKac)·aph+(KteKreKae)·ap(1)

The cutting tool can be discretized into elementary slices along its axis of rotation. Accordingly, the total cutting forces can be decomposed into infinitesimal forces acting per tooth and per axial slice. These infinitesimal forces are expressed in Eq. 2.

The Boolean parameter g, defined in Eq. 3, takes the value 1 when the tooth is engaged with the material, i.e., when the angular position φj,k, representing the angular location of tooth j in slice k relative to the direction normal to the feed direction (also referred to as the local immersion angle), lies between the entry angle φst and the exit angle φex. Otherwise, g is equal to 0 .

(dFt,jdFr,jdFa,j)=g(φj)[(KtcKrcKac)·dzhk,j+(KteKreKae)·dz]g(φj)={1 si φst≤φj,k≤φex0 otherwise 

(2)(3)

The uncut chip thickness generated by tooth j in slice k, denoted hk,j, is defined by Eq. 4. It is expressed as the product of the feed per tooth fz and the sine of the local immersion angle φj,k. The local immersion angle is determined using Eq. 5.

hk,j=fz·sinφj,kφjk=φ10+(j−1)·φp+kdztanδD/2

(4)(5)

The immersion angle is expressed as the sum of the reference rotation angle φ10, corresponding to the first contact between the first tooth and the workpiece, and the pitch angle φp. The pitch angle is defined as φp=2π/Z, where Z denotes the total number of teeth. The term φp(j−1) accounts for the phase shift between successive teeth. An additional term is included to account for the phase shift induced by the helix angle δ.

Once the infinitesimal tangential, radial, and axial forces are computed, they are transformed into the Cartesian coordinate system (x,y,z). In this system, the x-direction corresponds to the feed direction, the y-direction is perpendicular to the feed direction, and the z-direction is vertical. This transformation is performed using the homogeneous transformation matrix given in Eq. 6, which is valid for tools with cylindrical geometry.

(dFxj,kdFyj,kdFzj,k)=[−cosφj,k−sinφj,k0sinφj,k−cosφj,k0001]·{dFtj,kdFrj,kdFaj,k}(6)

The specific cutting pressures are defined as second-order Fourier series ( M=1 ) depending on the instantaneous fiber cutting angle θ, as expressed in Eq. 7 [7].

Kpq(θ)=∑i=0M(Cipqcos(2iθ)+Sipqsin(2iθ)) avec p=t,r,a;q=c,e(7)

The values of the constants, experimentally identified for the considered material and cutting tool, are reported in Table 1 [7].

Table 1. Cutting coefficients constant

Coefficient | Ktc | Krc | Kac | Kte | Kre | Kae
C₀ | 255.7 | 353.4 | 48 | 6.2 | 1.6 | 0.1
C₁ | 7.6 | -16.4 | -30.7 | -1.2 | 7 | 0
S₁ | 16.4 | 7.6 | 6.6 | 11.1 | 2.1 | 0

Numerical setup.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

In the framework of this study, the cutting conditions, tool characteristics, and material properties used as input data for the numerical model are summarized as follows. The investigated material is a unidirectional glass fiber-reinforced polymer (UD-GFRP), whose orthotropic elastic behavior is fully characterized by six independent constants. The longitudinal Young's modulus E1=39,000MPa reflects the stiffness in the fiber direction, while the transverse modulus E2=8,600MPa characterizes the matrix-dominated response perpendicular to the fibers. The in-plane shear modulus G12=3,800 MPa governs the shear behavior within the laminate plane, and the out-of-plane shear modulus G13 is assumed equal to G12. The transverse shear modulus G23=1,900MPa is estimated as half of G12 following the approach proposed in [14]. The principal Poisson's ratio v12=0.28 relates the transverse strain to the longitudinal strain under uniaxial loading in the fiber direction. The laminate is composed
of 14 unidirectional plies, all oriented in the same direction. These material properties were provided by Sobelcomp. The plate geometry is defined by a length L of 80 mm , a cantilever length Wc of 75 mm , and a thickness t of 7.2 mm . The fiber orientation is uniform throughout the laminate and corresponds to an angle ψ=0∘ for all plies. The fibers are oriented parallel to the feed direction. This configuration was selected to minimize the occurrence of defects in the surface plies [15]. Machining is performed using a 6 mm diameter ( Dc ), four-flute carbide milling cutter featuring a 10∘ helix angle, specifically designed for machining thermoset or thermoplastic G/C-FRP materials (Reference Seco Tools: 880060R020Z4.0-DURA). The cutting parameters, including the axial ap and radial ae depths of cut ( ap=t=7.2 mm; ae =Dc/2=3 mm ), the cutting speed ( vc=125 m/min ), and the feed per tooth ( fz=0.04 mm/th ), are selected in accordance with the recommendations of the tool manufacturer, Seco Tools, to ensure representative and industrially relevant machining conditions. These parameters constitute the complete set of input data used both for the calculation of the cutting forces and for the subsequent static finite element simulations.

Results.

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Mesh convergence.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

SC8R continuum shell elements are specifically designed to handle high aspect ratios between inplane dimensions and thickness [11]. This characteristic makes them well-suited for modeling thin plate-like structures where element dimensions in the plane significantly exceed the throughthickness dimension. However, accurate resolution of bending behavior requires multiple elements stacked through the thickness. Since each SC8R element contains only a single integration point, using only one element through the thickness would lead to nonphysical results, particularly in bending-dominated configurations.

The plate considered in this study has dimensions of 80 mm×75 mm×7.2 mm. To ensure consistent element quality across mesh refinement levels and to provide adequate through-thickness resolution, a proportional mesh refinement strategy is adopted. The mesh is refined uniformly in all spatial directions while maintaining constant aspect ratios between refinement levels, to limit the influence of element aspect ratio on the numerical results and isolate the effect of mesh density. For the convergence study, four refinement levels are considered, with a fixed refinement ratio of 2 between successive levels. The corresponding mesh configurations are summarized in Table 2. All mesh configurations maintain moderate through-thickness aspect ratios ( dx/dz=1.39,dy/dz=1.49 ) and in-plane aspect ratios close to unity ( dx/dy=0.93 ). The use of at least two elements through the thickness in the coarsest mesh ensures that bending gradients are captured, while finer meshes provide progressively improved resolution and numerical accuracy.

Table 2. Mesh configurations for convergence study

Level | Nx | Ny | Nz | # Elements | dx [mm] | dy [mm] | dz [mm]
1 – Coarse | 16 | 14 | 2 | 448 | 5 | 5.36 | 3.60
2 – Medium | 32 | 28 | 4 | 3584 | 2.5 | 2.68 | 1.80
3 – Fine | 64 | 56 | 8 | 28672 | 1.25 | 1.34 | 0.90
4 – Very fine | 128 | 112 | 16 | 229376 | 0.625 | 0.67 | 0.45

The convergence analysis is performed by monitoring the normalized displacements U1*,U2*,U3*, and the total displacement Utot *, evaluated at a reference node located at the center of the plate, on the middle surface, corresponding to x=0 mm. The displacement values are normalized with respect to those obtained using the coarse mesh. The results, presented in Fig. 4, show that increasing the mesh density has a significant influence on the in-plane displacement components U1* and U2*, while U3* and Utot * exhibit similar convergence trends. It is important to note that the orders of magnitude of U1 and U2 differ from those of U3 and Utot , which explains why the strong evolution of U1* and U2* has a limited impact on the total displacement. Beyond a certain refinement level, further increases in mesh density have a negligible effect on Utot , indicating that a very fine mesh is not required for accurate prediction of global deflection. Nevertheless, a sufficiently refined mesh remains necessary to
accurately capture the in-plane displacement components. It is also observed that the out-of-plane displacement U3 is overestimated when using the coarsest mesh, which employs only two elements through the thickness. This confirms that more than two through-thickness elements are required to properly represent bending behavior.

[picture]

Fig. 4. Mesh convergence of normalized displacements at the reference node

Fig. 4. Mesh convergence of normalized displacements at the reference node

The relative computation time associated with each mesh configuration is shown in Table 3. All simulations were performed on a desktop workstation equipped with an Intel Core i5-6400 processor ( 4 cores, 2.70 GHz ) and 16 GB of RAM, running Windows 10 Pro 64-bit. Adding more elements in the longitudinal direction has a double impact on the calculation time. Not only does it increase the cost of each single simulation, but it also increases the total number of simulations required, since an analysis is performed at every nodal point along the tool path. From a computational standpoint, the very fine mesh is therefore not viable, as it leads to computation times approximately 200 times longer than those of the coarse mesh (approximately 15 seconds). Considering both numerical accuracy and computational efficiency, the fine mesh configuration, defined by Nx=64,Ny=56, and Nz=8, is selected for the remainder of the study. This choice provides an appropriate compromise between solution accuracy and computational cost.

Table 3: Computational cost of mesh configurations for convergence study

Level | U1 [μm] | U2 [μm] | U3 [μm] | |U| [μm] | Relative unit time | Relative total time
1 – Coarse | -34.5 | -17.7 | 140.3 | 145.6 | 1.0 | 1.0
2 – Medium | -40.0 | -8.7 | 125.9 | 132.4 | 1.2 | 2.3
3 – Fine | -45.4 | -12.4 | 124.2 | 132.8 | 2.7 | 10.2
4 – Very fine | -50.9 | -15.1 | 124.1 | 134.9 | 26.3 | 199.7

Results.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

Figure 5.a presents the averaged cutting forces calculated for the investigated configuration. Since the current model does not account for plate deformation in the computation of the chip thickness, the cutting forces remain constant over the entire range of plate positions along the x-direction. The in-plane force components are dominant. The longitudinal force Fx is the largest component, with a magnitude of approximately -141 N , while the transverse force Fy reaches about 44 N . The out-ofplane force component Fz is positive and significantly smaller, with a value of approximately 12 N . In this configuration, FZ represents only about 8.5% of the magnitude of the dominant longitudinal force.

Figure 5.b presents the displacement components U1,U2, and U3, corresponding to the x−,y-, and z-directions, respectively, for the investigated configuration ( fz=0.04 mm ). The plate exhibits a pronounced out-of-plane displacement U3 at the free ends, which is consistent with the behavior of a
cantilever subjected to edge-applied loads. This out-of-plane component reaches a maximum magnitude of approximately 120μ m near the plate extremities ( x=0 and x=80 mm ) and gradually decreases toward the center, attaining a minimum value of about 70μ m at mid-span. The in-plane displacements, although smaller than U3, remain significant. The longitudinal displacement U1 ranges from approximately −30μ m at mid-span to −75μ m near the extremities. The transverse displacement U2 varies between approximately −20μ m and 30μ m along the trimming path. While U3 remains the largest individual displacement component, the total displacement magnitude is substantially influenced by the longitudinal deformation, which is of the same order of magnitude.

[picture]

Fig. 5. a) Average cutting force; b) Displacement U1,U2, and U3 at the loaded edge

Fig. 5. a) Average cutting force; b) Displacement U 1 , U 2 , and U 3 at the loaded edge

Despite the out-of-plane cutting force Fz being the smallest component (approximately 12 N ), the resulting displacement U3 remains the dominant structural response. This behavior is characteristic of thin cantilever plates, for which the bending stiffness is significantly lower than the in-plane membrane stiffness. In addition, with fibers oriented at 0∘, the bending stiffness associated with out-of-plane deformation is governed primarily by the transverse Young's modulus E2, which is significantly lower than the longitudinal modulus E1.

The out-of-plane displacement, reaching approximately 120μ m at the plate extremities, induces local plate rotations that may alter the effective tool-workpiece engagement geometry. The in-plane displacements U1 and U2 have a more direct influence on chip formation. In particular, U1, which reaches up to 75μ m, exceeds the nominal feed per tooth ( fz=40μ m ) near the plate ends, potentially leading to a substantial modification of the instantaneous chip thickness. Meanwhile, U2 directly affects the radial depth of cut. These observations underline the necessity of coupled formulations that explicitly account for the strong interaction between structural deformation and cutting mechanics.

Conclusion.

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

A static finite element methodology has been developed to predict milling-induced deflections in a flexible UD-GFRP plate. SC8R continuum shell elements provide an appropriate framework for modeling edge-applied cutting loads with through-thickness distribution, enabling physically consistent load transfer without relying on equivalent mid-surface resultants. A preliminary mesh convergence study indicates that a 64×56×8 element configuration offers a reasonable balance between numerical accuracy and computational cost, with at least four elements through the thickness necessary to capture bending behavior. For the studied configuration ( ψ=0∘,fz=0.04 mm/ th ), the out-of-plane displacement U3 reaches 120μ m near the plate extremities where structural stiffness is reduced, while in-plane displacements remain one order of magnitude smaller. Although smaller in magnitude, the in-plane displacements have direct implications for cutting mechanics: the longitudinal displacement U1 ranges from -30 to −75μ m, making it comparable to or even larger than the nominal feed per tooth. This directly affects the chip thickness, while the transverse displacement U2 (up to 30μ m ) modifies the radial depth of cut. The out-of-plane displacement induces local plate rotation that may affect the effective tool-workpiece engagement geometry. The current approach
neglects the feedback of plate deformation on cutting forces, assumes perfectly rigid clamping, and does not account for material removal or dynamic effects. Future work will address semi-static simulations incorporating the influence of plate deformation on chip thickness, multi-directional laminates with non-uniform force distributions through the thickness, and experimental validation. A dynamic simulation should also be considered. This would allow instantaneous forces to be applied to the plate instead of average forces, as is currently done. This study provides a foundation for predicting and controlling the structural response of flexible composite components during machining operations, but further experimental investigations are necessary to validate the numerical results.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

The authors would like to thank Région Wallonne for supporting this research as part of the MachFlexComp M-ERA.NET 2022 research project under grant 2210138.

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The original version of this paper is available on https://www.scientific.net/DDF.450.93.pdf

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 103-110

doi: 10.4028/p-I0dGes

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-03-04

Accepted:
2026-03-04

Online:
2026-04-15

Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

The original version of this paper is available on https://www.scientific.net/DDF.450.103.pdf

Agata Sposato 1,a*, Maria Rosaria Saffioti 1, b, Chiara Morano 1,c, Francesco Modica 2, d, Giovanna Rotella 3,e, Irene Fassi 4,f, Vito Basile 2, g

1 Department of Mechanical, Energy and Management Engineering, University of Calabria, Via Ponte Pietro Bucci, Cubo 45C, 87036, Rende, CS, Italy

2 CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council, Via P. Lembo 38F, Bari 70124 Italy

3 Department of Engineering, University LUM Giuseppe Degennaro, Casamassima, BA 70010, Italy

4 CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council, Via A. Corti 12, Milano 20133 Italy

a* agata.sposato@unical.it, b mariarosaria.saffioti@unical.it, c chiara.morano@unical.it, d francesco.modica@stiima.cnr.it, e rotella@lum.it, f irene.fassi@stiima.cnr.it, g  vito.basile@stiima.cnr.it

Keywords: microinjection molding, laser surface texturing, sustainable manufacturing.

Abstract

Laser surface texturing (LST) is increasingly adopted to functionalize the surface in injection molding, enabling the control of interfacial and tribological phenomena without altering bulk material properties. While most studies have focused on mold cavities, the functionalization of ejection system components remains largely unexplored, despite its critical role in part release and process stability. This work presents a preliminary investigation of laser surface texturing for cylindrical ejector pins to promote lubricant retention at the pin-mold interface. A parametric study was first carried out on a flat to define a process window compliant with the maximum allowable groove depth constraint (20μ m). Based on this campaign, a stable ablation regime was identified and transferred to cylindrical ejector pins, where textures were fabricated along axial length. Different micro-texture geometries and spatial distributions were designed to generate controlled microreservoirs for lubricant retention. The textured surfaces were characterized in terms of groove depth, morphology and uniformity, confirming the feasibility of producing shallow and well-defined features within industrial constraints. The preliminary results demonstrate the technical feasibility of laser texturing on cylindrical ejector pins and its potential to modify the pin-mold interface. However, the comparative effectiveness of the different texture geometries in promoting lubricant retention will be further evaluated under extended service conditions. The study, therefore, establishes the basis for the functional optimization of textured ejection systems in injection molding applications.

Introduction

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Injection molding is one of the most commonly used techniques for the production of a wide range of polymeric parts, especially those that demand high productivity, dimensional precision, and surface quality. Of the various steps in this processing technique, the critical phase is the ejection of the parts from the mold itself because of the risk of high adhesion or friction forces between the mold surfaces and the processed polymeric parts. Such forces may induce dimensional inaccuracies, deformed parts, longer processing times, and a dramatic increase in mold lifetime. As a consequence, there has been substantial interest in optimizing the functional properties of mold surfaces and ejection systems to reduce frictional and adhesive forces, as well as the wettability of the polymeric parts. This has led to the development of a range of technologies capable of modifying the textural features of polymeric parts without altering their inherent properties. Laser Surface Texturing (LST) is a modern technology that can modify the textural properties of processed parts by producing specific surface features. In the beginning, direct laser surface texturing was effectively used in the production of hydrophobic metallic surfaces. According to the research study by Gregorčič et al., laser-induced nanosecond
texturing of stainless steel surfaces at high fluence can alter the material's hydrophobic properties by producing specific textural features [1]. This is just the beginning of the development of techniques capable of modifying the properties associated with the applied forces through the production of specific surface features. In addition, numerous studies have reinforced the effect of laser-induced textural properties in the production of metallic surfaces. Based on the study by Yang et al. [2], the surface texturing of 0Cr17Ni7Al stainless steel produced by laser processing was systematically investigated by correlating groove texture parameters with the resulting tribological behavior. The authors demonstrated that controlled laser-induced groove characteristics significantly influence the frictional properties of the metallic surface. This work represents a fundamental study highlighting how precise laser surface texturing can be effectively employed to tailor specific surface texture features and, consequently, the tribological performance of stainless steel.

In the context of injection molding, laser surface texturing has gained increasing attention as a means to functionalize mold cavities and inserts. A comprehensive overview of texturing technologies for plastics injection molding was provided by Masato et al., who reviewed conventional and advanced surface modification techniques, emphasizing the advantages of laser-based approaches in terms of design freedom, reproducibility, and industrial scalability [3, 4]. The review underlined how surface textures can influence polymer flow, part demolding, optical appearance, and surface functionality of molded components. Several experimental works have focused on the generation of laser-induced periodic surface structures (LIPSS) on mold steels and their replication onto polymer parts. Orazi et al. investigated the generation and characterization of LIPSS on plastic injection molds, demonstrating the feasibility of producing well-defined periodic structures and their durability under molding conditions [5]. Building on this, Piccolo et al. explored the replication of variable-pitch LIPSS onto plastic parts, showing that laser-textured molds can be used to functionalize polymer surfaces with controlled micro- and nano-scale patterns during the molding process [6]. These results were further extended by Gao et al., who analyzed the wetting characteristics of hierarchical laser-ablated textures replicated by micro injection molding, highlighting the strong link between mold texture geometry and the wettability of molded parts [7]. While most studies have focused on mold cavity surfaces, fewer works have addressed the role of surface texturing on ejection-related components. However, the ejection phase is strongly influenced by friction and adhesion mechanisms at the polymer-steel interface. Sorgato et al. demonstrated that machined cavity textures can significantly affect ejection force in micro injection molding, showing that surface topography plays a decisive role in part release behavior [8]. These findings suggest that extending surface functionalization strategies from cavities to ejection systems could offer additional benefits in terms of process stability and part quality. Recent investigations have also explored laser surface texturing in hybrid manufacturing contexts involving polymers and metals. Kim et al. studied the combined effects of laser surface texturing and injection molding parameters in direct joining between advanced high-strength steel and polyamide, confirming that laser-induced microstructures can enhance interfacial performance and mechanical interlocking [9-11]. Although focused on joining applications, this work further highlights the versatility of laser texturing in tailoring polymer-metal interactions. Within this research framework, the functionalization of cylindrical ejectors represents a relatively unexplored but highly promising direction. Unlike flat cavity surfaces, ejector pins and sleeves operate under localized contact conditions and are directly responsible for transmitting ejection forces to the molded part. The preliminary study forming the basis of the present work demonstrated the feasibility of applying controlled laser ablation to cylindrical ejectors, generating shallow vertical grooves with depths below 20μ m while preserving surface integrity. Such textures are expected to promote air evacuation, reduce real contact area, and mitigate adhesion during ejection.

The present contribution builds upon this preliminary investigation by extending the range of surface textures applied to cylindrical ejectors, exploring different geometries and spatial distributions and systematically assessing their influence on part release behavior in injection molding. By combining insights from laser surface engineering, tribology and polymer processing,
this work aims to advance the understanding of how laser-textured ejector surfaces can be used as an effective tool to improve ejection performance and process robustness.

Materials and Methods

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A preliminary experimental campaign was carried out on flat specimens in order to define the process window ensuring compliance with the maximum allowed groove depth. The tests were performed using an air-cooled ytterbium fiber laser system (Lasit CompactMark G8). The aim was to find the combination of laser power (P) and scanning speed (v) that is able to produce shallow and flat grooves below the 20μ m depth threshold imposed by the specification. Laser ablation tests have been carried out at varying process parameters, choosing three different scanning speeds and power levels: 1,2 , 10 mm/sec and 40, 60, 80 W, respectively. The formed grooves were evaluated using a non-contact white-light confocal profilometer (Rtec/UP-24). Among the tested conditions, the parameter combination of a scanning speed of 1 mm/s and 80 W of laser power was selected, as it provided a groove depth compliant with the imposed constraint while ensuring stable morphology.
Building on the outcomes of the preliminary phase, the following experimental campaign focused on extending the range of surface textures applied to cylindrical ejector pins (diameter 3 mm ), made by nitrided steel 1.2344 (HRC 58-60), along a 15 mm axial length of the cylindrical ejector pins, according to the setup shown in Figure 1. The ejector pins used for the final application tests were surface-nitrided to increase surface hardness and wear resistance, as is typically required in industrial injection molding systems.

[picture]

Fig. 1. Laser process experimental set up on cylindrical ejector pins.

Fig. 1. Laser process experimental set up on cylindrical ejector pins.

Different texture geometries, with a spatial distribution of 250μ m, are being designed in order to investigate their influence on part release behavior as shown in Figure 2 (vertical grooves, horizontal grooves and grid pattern). The different texture geometries were designed to create distinct lubricant retention mechanisms, which will be comparatively evaluated at the application level in terms of ejection force and surface interaction. Injection molding trials are being planned and progressively implemented to evaluate the functional performance of the laser-textured ejectors. The assessment is focused on qualitative and quantitative indicators of demolding behavior, including part release stability, occurrence of surface defects, and repeatability of the ejection process. The results obtained in this phase will provide the basis for correlating surface texture characteristics with ejection performance. Overall, the activities carried out to date confirm the technical feasibility of laser surface texturing of cylindrical ejector pins and support its potential as an effective surface functionalization strategy. The ongoing developments aim to consolidate these findings and to provide design guidelines for the application of laser-textured ejectors in industrial injection molding systems.

[picture]

Fig. 2. Different surface textures applied to cylindrical ejector pins.

Fig. 2. Different surface textures applied to cylindrical ejector pins.

Results

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Figure 3 shows representative grooves obtained during the preliminary campaign on flat samples. The highlighted configuration corresponds to the selected set of parameters ( 1 mm/sec,80 W ), which ensured the constraint on the maximum allowed depth (20μ m). When the power levels are low with a high scanning speed, the ablation depth is negligible (only a few microns), which confirms that there is a lack of energy input. By increasing the levels of power while reducing the scanning speed, deeper and more defined grooves have been obtained. However, even with the hardest conditions, the depth did not exceed 20μ m. This range is considered optimal with regard to the constraint, as it is effective in surface modifications with a significant safety margin below the maximum allowable threshold. The selected parameter combination, therefore, ensured a controlled and stable ablation regime, suitable for subsequent transfer to the cylindrical ejector pins.

[picture]

Fig. 3. Groove generated on flat steel.

Fig. 3. Groove generated on flat steel.

Figure 4 shows the groove morphology obtained on the cylindrical ejector pin using the selected laser parameters. Compared to flat samples, the surface response is influenced by both the hardened nitrided layer and the cylindrical geometry, resulting in slight variations in melt dynamics and groove edge definition. Under these conditions, a slightly greater groove depth was observed with respect to the flat specimens. However, the measured depth remains within the predefined allowed range and
therefore complies with the functional constraint imposed for the application. The 3D reconstruction and the profile confirm that process stability was preserved during the transfer from planar to cylindrical geometry, maintaining a controlled ablation regime and a consistent groove morphology.

[picture]

Fig. 4. Groove generated on cylindrical ejector surface (a) profile, (b) 3D view.

Fig. 4. Groove generated on cylindrical ejector surface (a) profile, (b) 3D view.

As can be seen from Figure 5, the 3D morphological comparison of the considered texture configurations has a more consistent look for the vertical grooves, where the sidewalls are also smoother, compared to the horizontal textures, where the valleys are deeper and the edges are more defined, and the grid texture, where the directionality of the textures creates a more complex surface, enhancing the depth in the intersection of the grooves.

These morphological differences are expected to influence lubricant retention mechanisms and interfacial behaviour during demolding, aspects that will be addressed in subsequent functional testing.

[picture]

Fig. 5. 3D surface topography of laser-textured cylindrical ejector pins: (a) vertical grooves, (b) horizontal grooves, (c) grid configuration. Measurements performed by optical profilometry; color scale represents height variation in μm.

Fig. 5. 3D surface topography of laser-textured cylindrical ejector pins: (a) vertical grooves, (b) horizontal grooves, (c) grid configuration. Measurements performed by optical profilometry; color scale represents height variation in μ m .

Table 1 summarizes the quantitative depth measurements of the different textured configurations obtained on cylindrical ejector pins. The vertical groove configuration had the lowest mean depth of 7.63μ m and the smallest standard deviation of 1.68μ m, indicating a highly stable ablation process. For horizontally oriented grooves, a higher mean depth of 13.74μ m was observed, indicating greater interaction between the laser energy and the cylindrical surface when the scanning direction is perpendicular to the pin axis. The grid configuration showed intermediate depth values (10.44μ m), with higher dispersion particularly in the horizontal component. This behaviour can be attributed to
pulse overlap and local thermal accumulation effects occurring at groove intersections. Overall, all textures remained within the predefined functional depth range, confirming the feasibility of controlled surface functionalization on cylindrical geometries.

Table 1. Quantitative depth characterization of laser-textured ejector pins (optical profilometry measurements).

Texture | Mean depth [μm] | Max depth [μm] | Std. Dev.
Vertical | 7.63 | 13.90 | 1.68
Horizontal | 13.74 | 17.22 | 3.05
Grid vertical | 10.34 | 15.31 | 2.54
Grid horizontal | 10.63 | 16.97 | 4.12
Grid mean | 10.44 | 16.97 | 3.07

Figure 6 presents the integration of the quantitative and morphological analysis of the investigated textures. In the bar chart, the depth trends, as previously reported in Table 1, are confirmed, showing that the grooves of the horizontal configuration are significantly deeper compared to the vertical configuration, while the grid texture has intermediate values. Error bars indicate the standard deviation, confirming good repeatability of the laser process, particularly for the vertical orientation. The 3D topography maps of the horizontal and grid textures further illustrate the morphological differences induced by the scanning strategy. The horizontal configuration generates deeper, more pronounced valleys, whereas the grid pattern produces localized depth amplification at groove intersections due to pulse-overlap effects. The longitudinal surface profile measured on the vertically textured sample shows a regular and periodic groove distribution, with limited depth fluctuation, supporting the stability of the selected laser parameters on cylindrical geometries. These morphological characteristics are expected to influence lubricant retention mechanisms and contact behaviour during ejection, which will be systematically investigated in future functional tests.

[picture]

Fig. 6. Integrated quantitative and morphological characterization of the textured ejector pins. (a) Average groove depth for vertical, horizontal and grid configurations, (b) and (c) 3D optical profilometry of horizontal and grid textures and the red arrows represents the measurement direction, (d) longitudinal surface profile of the vertical texture.

Fig. 6. Integrated quantitative and morphological characterization of the textured ejector pins. (a) Average groove depth for vertical, horizontal and grid configurations, (b) and (c) 3D optical profilometry of horizontal and grid textures and the red arrows represents the measurement direction, (d) longitudinal surface profile of the vertical texture.

Conclusion

The original version of this paper is available on https://www.scientific.net/DDF.450.103.pdf

This preliminary study demonstrated the feasibility of applying controlled laser surface texturing on cylindrical ejector pins for injection molding applications. The experimental results confirmed that the selected laser parameters enable stable and repeatable micro-scale groove generation on cylindrical surfaces while maintaining the depth within the predefined functional range.

Quantitative measurements highlighted a clear influence of scanning orientation on groove morphology. Vertical textures exhibited the highest process stability and lowest depth variability, whereas horizontal grooves produced significantly deeper valleys, likely due to the interaction between scanning direction and cylindrical geometry. The grid configuration resulted in intermediate average depths but showed localized amplification effects at groove intersections, attributable to pulse overlap and thermal accumulation phenomena.

The combined statistical and topographical analyses confirm that laser texturing can be effectively tailored to modulate surface morphology without compromising geometric integrity. Although the present work focused on morphological and metrological characterization, the observed differences in groove depth and spatial distribution are expected to influence lubricant retention and interfacial behavior during part ejection. Future work will therefore involve functional testing under real injection molding conditions to assess the impact of each texture configuration on lubricant stability, adhesion reduction, ejection force reduction and repeatability.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/DDF.450.103.pdf

The authors sincerely thank @STAR and MaTeRiA laboratories, funded by "Progetto STAR 2 PIR01-0000800", for providing the equipment employed for experimental analysis. This research was supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on "Next generation of sustainable and highly efficient molding processes" Italian Projects of Significant National Interest (PRIN) PNRR 2022 grant n. P2022ZE23N.

References

The original version of this paper is available on https://www.scientific.net/DDF.450.103.pdf

P. Gregorčič, B. Šetina-Batič, M. Hočevar, Controlling the stainless steel surface wettability by nanosecond direct laser texturing at high fluences, Applied Physics A, 2017. https://doi.org/10.1007/s00339-017-1392-5.

L. Yang, W. Ma, F. Gao, S. Xi, Effect of Different Laser Groove Texture Collation Frequency on Tribological Properties of 0Cr17Ni7Al Stainless Steel, Materials, 15(13), 4419, 2022. .

E. R. Moldovan et al., Morphological Analysis of Laser Surface Texturing Effect on AISI 430 Stainless Steel, Materials, 15(13), 4580, 2022. .

D. Masato, L. Piccolo, G. Lucchetta, M. Sorgato, Texturing Technologies for Plastics Injection Molding: A Review, Micromachines, 13(8), 1211, 2022. .

L. Orazi, M. Sorgato, L. Piccolo, D. Masato, G. Lucchetta, Generation and Characterization of Laser Induced Periodic Surface Structures on Plastic Injection Molds, International Journal of Advanced Manufacturing Technology, 2020. .

L. Piccolo, M. Sorgato, A. Batal, S. Dimov, G. Lucchetta, D. Masato, Functionalization of Plastic Parts by Replication of Variable Pitch Laser-Induced Periodic Surface Structures, Micromachines, 11(4), 429, 2020. .

P. Gao et al., Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding, Micromachines, 14(4), 863, 2023. .

M. Sorgato, D. Masato, G. Lucchetta, Effects of machined cavity texture on ejection force in micro injection molding, Precision Engineering, 2017. .

Y. Kim et al., Investigation of Laser Surface Texturing and Injection Molding Parameters in Advanced High-Strength Steel-Polyamide Direct Joining, Steel Research International, 2024. . 202400686.

Caruso, S., Umbrello, D. "Numerical and experimental validation of gas metal arc welding on AISI 441 ferritic stainless steel through mechanical and microstructural analysis". International Journal of Advanced Manufacturing Technology, 120, 7433-7444, (2022). .

Nie, G., Zhang, K., Outeiro, J., Caruso, S., Umbrello, D., Ding, H., Zhang, X., "Plastic strain threshold determination for white layer formation in hard turning of AISI 52100 steel using micro-grid technique and finite element simulations". Journal of Manufacturing Science and Engineering, Transaction of the ASME, 142(3), 034501 (2019). .

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Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 111-118

doi: 10.4028/p-xY2PXd

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-03-04

Accepted:
2026-03-04

Online:
2026-04-15

Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

The original version of this paper is available on https://www.scientific.net/DDF.450.111.pdf

Agata Sposato 1,a*, Maria Rosaria Saffioti 1, b, Zheng Liu 2,c, Enrico Simonetto 2, d, Giovanna Rotella 3,e, Domenico Umbrello 1,f

¹Department of Mechanical, Energy and Management Engineering, University of Calabria, Via Ponte Pietro Bucci, Cubo 45C, 87036, Rende, CS, Italy

2 Department of Industrial Engineering, University of Padova, via Venezia 1, 35131, Padova, PD, Italy

3 Department of Engineering, University LUM Giuseppe Degennaro, Casamassima, 70010, BA, Italy

a  agata.sposato@unical.it, b  mariarosaria.saffioti@unical.it, c  zheng.liu@unipd.it, denrico.simonetto.1@unipd.it, erotella@lum.it, fomenico.umbrello@unical.it 

Keywords: Fiber Metal Laminates (FMLs), Laser Texturing, Drilling, Cutting Forces.

Abstract

Drilling of Fiber Metal Laminates (FMLs) is a very important manufacturing operation, as the metal-composite layered structure tends to undergo delamination, burrs, and dimensional errors. Although to date research work has mainly focused on cutting conditions, drilling tools, and lubrication methods for improved performance, the role of surface engineering on metal sheets prior to laminate fabrication has not received significant consideration. In this study, the effect of the variation of feed rate values during the drilling operation of FMLs with laser-textured titanium sheets is examined. Specifically, prior to FML consolidation, a unidirectional pattern with spacing characteristics of 200μ m was created on the titanium sheets; then, drilling tests using twist drill bits were carried out at a fixed cutting speed while varying the feed rate on two levels. The results show that a higher feed rate value induces both greater thrust force - and therefore improved adhesion resistance at the interface between the various materials of the multilayer - and a deterioration in the surface quality of the hole, increasing the occurrence of fiber pull-out and metal transfer phenomena.

Introduction

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Fiber Metal Laminates (FMLs) are multilayered structures alternating aluminum or titanium alloys laminates and fiber-reinforced polymer composites, featuring well-designed hybrid properties from both components. FMLs are extensively employed as aerospace structural components because of their enhanced fatigue life, versatility, crack tolerance, and high strength-to-weight ratio relative to conventional metallic alloys and fiber-reinforced composites. Nevertheless, inherent heterogeneity, anisotropy, complexity, as well as difficulties in precise control of machining processing make FMLs difficult to handle. Given the applications for which FMLs are intended in the aerospace sector, drilling is one of the main machining operations they undergo. Thus, considerable work has been carried out to improve their machinability.

Damage induced by drilling has long been regarded as a critical issue related to composite materials. In this context, initial investigations focused on the mechanisms of surface integrity deterioration due to phenomena as delamination, fiber pull-out, matrix failure, and thermal damage, which may lead to a serious reduction of the sample mechanical performance and fatigue life [1]. In carbon fiber reinforced polymers (CFRP) laminates, it was found that thrust force predominantly influences delamination initiation, with a strong impact deriving from cutting speed, feed rate, and drill geometry [2]. In more recent studies, it was shown that optimizing drill force parameters through higher cutting speeds can lead to a reduction of thrust force and delamination damages as well, while displaying improved accuracy of the resulting hole geometry and surface quality [3].

When drilling hybrid stacks or FMLs, handling difficulties become considerably more complex due to the abrupt change in material properties at the interfaces. Metallic layers typically promote continuous chip formation and burr generation, while composite layers exhibit brittle fracture and powder-like chip morphology. Shyha et al. [4] provided one of the first comprehensive assessments of hole quality in metallic-composite stacks, demonstrating that interface-related damage, burr formation at metallic exits, and dimensional inaccuracies are strongly influenced by stack configuration and tool progression sequence.

Specific attention to FML drilling has increased over the past decade. Giasin et al. [5] developed a three-dimensional finite element model to predict cutting forces during drilling of FMLs and correlated numerical predictions with experimental observations of hole quality. Their results showed good agreement between predicted thrust forces and experimentally observed damage mechanisms, highlighting the importance of modelling approaches for understanding multi-material drilling behaviour. In a complementary experimental study, Giasin and Ayvar-Soberanis [6] investigated the effects of minimum quantity lubrication (MQL) and cryogenic cooling on GLARE TM  laminates, demonstrating that lubrication strategy significantly affects burr height, surface roughness, and delamination severity, especially at metallic-composite interfaces. The influence of cooling and lubrication strategies has also been explored for composite-titanium stacks. Xu et al. [7] conducted a comparative study on dry and MQL drilling of CFRP/Ti6Al4V stacks using different tool coatings, reporting that MQL can reduce tool wear and improve hole quality, although its effectiveness strongly depends on coating type and cutting parameters.

From a materials perspective, the machining behavior of titanium alloys commonly used in aerospace stacks has been extensively studied. Investigations on Ti6Al4V drilling have shown that high thrust forces, elevated temperatures, and segmented chip formation are inherent challenges that directly influence burr formation and dimensional accuracy [8, 9].

Recent works have broadened the focus of FMLs to the exploration of new metallic material systems. In this category, the work of Lizzul et al. [10] analyzed the drilling of magnesium FMLs through hot pressing: the authors observed the importance of surface treatments on metallic layers to the level of interfacial bonding. Although the importance surface modification to the overall performance of laminates has generally been affirmed, the direct relationship of the texturing to the resulting integrity of drilled holes has yet to be made. Indeed, contributions made by the works of Dogan et al. [11] culminated in the compilation of the entire literature relevant to the drilling process of FML stacks through conventional means. Nonetheless, the authors affirmed the challenges experienced despite the research progress made, mainly due to the material variability exposed to the process. Significantly, the challenges have come mainly from the layer surfaces [12].

Though considerable work has been carried out to improve FMLs machinability, very little attention has been paid to the potential role of engineered surface topographies at the metal-composite interface. In fact, a modification in surface roughness necessarily results in different local contact conditions and consequently hot pressing consolidation results, thus influencing key aspects such as forces transmission, drilling-induced damage and hole quality. The present work aims to correlate the application of surface modification techniques - with specific focus on laser texturing - and both the strength of the adhesive bond at the interface between sheet metal and composite layer and the quality of internal hole surface during drilling tests. This is achieved by assessing the evolution of the cutting forces developed during machining as the process parameters vary, and by analyzing the occurrence of phenomena like fiber pull-out, matrix smearing and interface debonding.

Materials and Methods

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The samples investigated are FMLs composed of two external Ti6Al4V titanium alloy sheets and a central glass fiber-reinforced thermoplastic composite layer. Each titanium sheet had dimensions of 40 mm×160 mm and a thickness of 1 mm , while the composite core consisted of a 1 mm -thick glass fiber-reinforced polyamide 6 prepreg PA6-GFRP (Tepex ® 102-RG600 (2)/47% Type B). The characteristics of the prepreg assembly are reported in Table 1, while the mechanical properties of the both the metal and composite layers are listed in Table 2.

Table 1. PA6-GFRP composition characteristics.

Layup | Characteristics
Matrix | Polyamide 6 [-NH(CH2)5CO-]n Melting point: 220°C

Reinforcement
E-Glass roving
Weaving style: Twill 2/2
Area weight (dry fabric): 600 g/m³
Yarn count: 1200 tex
Weight rate: 50%
Fiber content: 47%

Laminate density | 1.8 g/cm³

Table 2. Mechanical properties of Ti6Al4V and PA6-GFRP.

Ti6Al4V |  | PA6-GFRP |
Elastic modulus | 110 GPa | Tensile modulus | 18 GPa
Shear modulus | 41.35 GPa | Flexural modulus | 16 GPa
Poisson's ratio | 0.33 | Strain at break | 2.3%
Yield strength 0.2% | 1090 MPa | Flexural strength | 300 MPa
Ultimate tensile strength | 1120 MPa | Ultimate tensile strength | 380 MPa

Prior to laminate consolidation, the titanium sheets were cleaned in an acetone ultrasonic bath and then textured using an air-cooled ytterbium fiber laser system (Lasit Compact Mark G8 - Fig. 1a). Based on preliminary studies [13], a laser power of 40 W and a speed of 50 mm/sec were selected as process parameters. The applied texture consisted of unidirectional features aligned parallel to the minor dimension of the Ti6Al4V sheets and spaced 200μ m apart. After texturization, each component was hot-pressed to form the FML specimens (Fig. 1b), producing titanium-compositetitanium sandwich structures with controlled interfacial bonding.

[picture]

Fig. 1. a) Laser texturing experimental setup; b) hot pressing schematic representation based on [14].

Fig. 1. a) Laser texturing experimental setup; b) hot pressing schematic representation based on [14].

Drilling experiments were carried out on a CNC Mazak Nexus 410 vertical machining center; Fig. 2a illustrates the experimental set-up and the clamping configuration. The specimens were placed inside a support that allowed stable positioning during the test, so as to avoid excessive vibrations and possible displacements of the specimen itself. This support was in turn secured with screws to a 3-components piezoelectric dynamometer (Kistler© 9257), which was connected to a charge
amplifier (KistlerC LabAmp 5167A) and used to acquire signals related to the forces generated on the sample during drilling. The thrust force Fz was selected the main variable of interest for the purposes of the analysis; it was measured at a sampling rate of 2.5 kHz . Data acquisition and elaboration were carried out via the DynoWare TM software.

All tests were performed using a solid carbide drill (Sandvik Coromant CoroDrill® 863.1-0635-026A1-OS H10F) with a nominal diameter of 6.35 mm and a 135∘ point angle, designed to optimize GFRP drilling. Fig. 2b shows the geometry of the drilling tool; images were acquired via Leica Stereo Microscope MS5. Due to the intrinsic complexity of the multi-material system under investigation, given the tool manufacturer's recommendations to identify a stable processing window to avoid premature tool failure or excessive damage, and based on information gathered from literature, a cutting speed of 15 m/min was fixed as driving process parameter, while two feed rate values of 0.06 mm/rev and 0.10 mm/rev were varied during the experimental campaign. Given the localized nature of the drilling operations, a minimum quantity lubrication (MQL) strategy was chosen as most suitable. For each combination of process parameters, 5 repetitions were performed in order to allow for adequate statistical analysis of the results, for a total of 10 drilling operations. To prevent any effect of tool wear, each FML was drilled with a fresh drill bit.

[picture]

Fig. 2. a) Drilling experimental setup; b) drill bit geometry.

Fig. 2. a) Drilling experimental setup; b) drill bit geometry.

Drilled FMLs samples were sectioned using a Struers Labotom- 5 precision metallographic saw. The internal surface of the holes was analyzed using both a Leica Stereo Microscope MS5 and an optical microscope (Leica DFC320). The surface integrity evaluation took into account aspects such as surface finish, smearing, fiber pull-out, material residues and delamination.

Results and Discussion

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Fig. 3 shows the representative trends in the evolution of the thrust force generated during the drilling process for both combinations of process parameters used - the data was first cleaned of noise resulting from signal acquisition. In both cases, three clearly defined regions can be identified.

When the tool comes into contact with the external titanium surface of the FML, a rapid increase in thrust force occurs, as it reaches its maximum value, thus indicating complete penetration into the layer. This peak reflects the high mechanical strength and low thermal conductivity of the material, which result in considerable friction and, indeed, high thrust force.

Then, a sudden decrease in force is recorded: this can be attributed to the fact that the titanium sheet has been perforated and therefore the tool comes into contact with the PA6-GFRP layer. This material not only has a significantly lower strength than titanium, but also exhibits brittle deformation and fracture behavior due to the presence of glass fibers. It is interesting to note that the Ti6Al4V/PA6-GFRP and PA6-GFRP/Ti6Al4V interface regions can be identified by slight fluctuations in the measured force values. These variations are primarily due to the transition of the drill bit from the metal material to a completely different one, as well as to aspects such as the
presence of fibers, their orientation within the matrix and the alternating cutting action of fiber and matrix.

Finally, when the tool completes its engagement into the polymer layer to encounter the second titanium layer, a further increase in the thrust force occurs. Here, the peak force reached when the titanium layer is completely drilled through is lower than the previous one: this decrease is to be ascribed to both the tool, since the drilling of the two previous layers caused an increase in temperature and the possible onset of wear, and to the sample, as its overall flexural strength is reduced from the first layer to the last one. As the force value decays to zero, the FML has been completely drilled through.

[picture]

Fig. 3. Thrust force evolution for different feed rate values: a) f=0.06 mm/rev; b) f=0.10 mm/rev.

Fig. 3. Thrust force evolution for different feed rate values: a) f = 0.06 m m / r e v ; b) f = 0.10 m m / r e v .

To understand the effect of feed rate variation on thrust force, Fig. 4a shows the two typical trends analyzed above superimposed on the same graph; Fig. 4b shows the average thrust force values at the peaks identified for the two Ti6Al4V layers and the valley corresponding to the composite layer. In particular, given the variability of the thrust force in the PA6-GFRP layer between the two interfaces, the average force measured at the interfaces was taken as a reference value to describe the behavior of the material. It can be highlighted that, as the feed value increases, there is a 30% rise in thrust force: this effect is a direct consequence of the higher material strength, which in turn is attributable to an increase in strain rate. This effect is particularly significant for the PA6-GFRP layer, as it can be considered related to the mechanical strength of the FMLs interfaces, thus implying an increase in resistance to delamination when drilling at higher feed rates.

[picture]

Fig. 4. a) Comparison of thrust force evolution for both feed rate values; b) maximum Ti6Al4V and mean PA6-GFRP thrust force values.

Fig. 4. a) Comparison of thrust force evolution for both feed rate values; b) maximum Ti6Al4V and mean PA6-GFRP thrust force values.

In terms of hole quality assessment, Fig. 5 shows the hole cross sections as the feed rate varies. Specifically, images captured using a stereo microscope (Fig. 5a,b) and an optical microscope (Fig. 5c,d) are shown.

When using a feed rate f=0.06 mm/rev, the surfaces in the Ti6Al4V layers show regular and relatively uniform circumferential streaks, indicating a stable cutting process and low roughness; only limited localized plastic adhesion phenomena are observed, compatible with moderate built-up edge (BUE) formation, yet without obvious signs of tearing or smearing. Near the tool exit zone, grooves are probably caused by chips getting stuck in the hole before being ejected, so that the tool rotation left such pattern on the metal surface. The PA6-GFRP layer has a fairly homogeneous surface: the glass fibers emerge regularly and are sharply cut, while only near the interface with the metal layer a reduced fiber pull-out and a slight presence of smeared metal material can be noticed (Fig. 5c). On the other hand, the absence of cavities or voids suggests a limited damage and the absence of debonding.

As the feed rate increases, there is an overall deterioration in surface quality. In fact, in the Ti6Al4V layers, the streaks are more pronounced and less uniform, consistent with an increase in cutting forces, chip thickness and BUE instability. In the composite layer, the surface is more irregular, with less clear fibers cut, an increase in extraction or fraying phenomena, and the presence of more consistent plasticized metal residues, located not only at the interface, but also in the central portion of the layer (Fig. 5d). This phenomenon is likely to be favored by the increase in temperature and contact pressures that develop at a higher feed rate. However, no debonding or delamination phenomena are observed in this case either.

Overall, an increase in feed rate leads to increased roughness and irregularity in the metal layers and a higher level of damage to the composite layer. These findings indicate that, under the conditions examined, the lower feed rate ensures better surface integrity of the hole in the multilayer laminate, reducing both damage to the GFRP and metal transfer phenomena.

[picture]

Fig. 5. Hole cross sections of drilled samples at varying feed rate observed via: stereo microscope (a, b); optical microscope (c, d).

Fig. 5. Hole cross sections of drilled samples at varying feed rate observed via: stereo microscope (a, b); optical microscope (c, d).

Conclusions

The original version of this paper is available on https://www.scientific.net/DDF.450.111.pdf

This study focused on assessing the strength of the adhesive bond and the hole quality in the drilling process of FMLs structures typically used in the aerospace industry. The main objective was to investigate the influence of surface modification treatments, specifically laser texturing, on these aspects of significant engineering importance, since drilling-induced defects substantially affect the structural integrity and fatigue performance of FMLs.

To this end, Ti6Al4V sheets, which constitute the metallic part of FMLs, were subjected to laser texturing to create a one-dimensional micro-pattern; laser-textured titanium sheets were then consolidated with glass fiber-reinforced PA6 prepreg (PA6-GFRP) through hot pressing to produce aerospace-grade FMLs.

The experimental drilling test campaign allowed to establish how, at a given cutting speed, an increase in feed rate leads to an increase in thrust force, identified here as the main engineering variable since it provides a measure of the adhesive bond strength at the interface between the titanium sheets and the composite core. On the other hand, the evaluation of the surface quality of the inner walls of the holes showed that an increase in feed rate corresponds to an increase in roughness, a greater amount of fiber pull-out and more widespread plasticization of the titanium, resulting in it spreading in different areas of the polymer matrix layer. However, no debonding or delamination phenomena occurred for either feed rate value.

Accordingly, the next steps will aim to further expand this research by introducing new textures to be compared to the one investigated here, analyzing a wider range of feed rate values - in order to identify the delamination limit condition - and introducing a variation in cutting speed as characteristic process parameter. For a more in-depth analysis of the surface quality of the holes made, SEM can be used as reference technique.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/DDF.450.111.pdf

Authors sincerely thank @STAR and MaTeRiA laboratories, funded by "Progetto STAR 2 PIR010000800", for providing equipment employed for experimental analysis.

This research was supported by Ministry University Research within the PRIN 2022 (project ADVANCE2022W9SHCJ) and the PNRR (project FAIR Spoke 9 AI- PE00000013) programs funded by the NextGenerationEU.

References

The original version of this paper is available on https://www.scientific.net/DDF.450.111.pdf

E. Brinksmeier, S. Fangmann, R. Rentsch, Drilling of composites and resulting surface integrity, CIRP Ann. - Manuf. Technol. 60 (2011) 57-60. https://doi.org/10.1016/j.cirp.2011.03.081

V.N. Gaitonde, S.R. Karnik, J. Campos Rubio, A. Esteves Correia, A.M. Abrao, J.P. Davim, Analysis of parametric influence on delamination in high-speed drilling of carbon fiber reinforced plastic composites, J. Mater. Process. Technol. 196 (2008) 207-216.

V.P. Krishnaraj, A. Prabukarthi, A. Ramanathan, N. Elanghovan, M.S. Kumar, R. Zitoune, J.P. Davim, Optimization of machining parameters at high-speed drilling of carbon fiber reinforced plastic (CFRP) laminates, Compos. Part B 43 (2012) 1210-1218.

I.S. Shyha, S.L. Soo, D.K. Aspinwall, S. Bradley, R. Perry, P. Harden, S. Dawson, Hole quality assessment following drilling of metallic-composite stacks, Int. J. Mach. Tools Manuf. 51 (2011) 569-578.

K. Giasin, S. Ayvar-Soberanis, A. Vafadar, 3D finite element modelling of cutting forces in drilling fibre metal laminates and experimental hole quality analysis, Appl. Compos. Mater. 24 (2017) 501-525.

K. Giasin, S. Ayvar-Soberanis, The effects of minimum quantity lubrication and cryogenic liquid nitrogen cooling on drilled hole quality in GLARE fibre metal laminates, Mater. Des. 90 (2016) 1007-1018.

J. Xu, M. Ji, J.P. Davim, M. Chen, M. El Mansori, V. Krishnaraj, Comparative study of minimum quantity lubrication and dry drilling of CFRP/titanium stacks using TiAlN and diamond-coated drills, Compos. Struct. 234 (2020) 111727.

S.V.S. Kumar, S.S. Prasad, K.S. Reddy, S.P. Babu, G.N. Naidu, K. Srinivasa, Investigation of thrust forces, torque and chip microstructure during drilling of Ti-6Al-4V titanium alloy, Appl. Mech. Mater. 707 (2015) 431-436.

G. Parodo, F. Rubino, L. Sorrentino, S. Turchetta, Temperature analysis in fiber metal laminates drilling: Experimental and numerical results, Polym. Compos. 43 (2022) 7600-7615.

L.B. Lizzul, S. Rizzo, G. Montalto, S. Agnello, C. Borsellino, Drillability of magnesium-based fiber metal laminates obtained via hot metal pressing with different metal surface treatments, Key Eng. Mater. (2024).

M.A.Y. Doğan, G. Şeker, L. Gökçe, M. Yılmaz, A. Yıldız, A review on drilling of FML stacks with conventional and unconventional processing methods under different conditions, Compos. Struct. 287 (2022) 115280. .

M. Caggiano, M. R. Saffioti, G. Rotella, Fiber metal laminates: the role of the metal surface and sustainability aspects. J. Compos. Sci. (2025), 9, 35. .

G. Rotella, C. Morano, M. R. Saffioti, D. Umbrello, Surface functionalization of titanium screws for orthopaedic implant applications, CIRP Annals 1, (2024), 453-456. .

Z. Liu, E. Simonetto, A. Ghiotti, S. Bruschi, Compaction behaviour of magnesium alloy-based fibre metal laminates at varying forming parameters, Materials Research Proceedings 28 (2023) 259-266. .

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 119-126

doi: 10.4028/p-Ni0KLa

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-21

Revised:
2026-02-23

Accepted:
2026-02-24

Online:
2026-04-15

Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

Dimitrios Apollon Gkartzonikas 1,a, Nikolaos Karkalos 1, b, Angelos Markopoulos 1,c and Panorios Benardos 1, d * 

1 National Technical University of Athens, School of Mechanical Engineering, Manufacturing

Technology Laboratory, Heroon Polytechniou 9, GR15772, Athens, Greece

a  dimitrisgartzonikas@gmail.com, b  nkark@mail.ntua.gr, c  amark@mail.ntua.gr, d  pbenard@mail.ntua.gr

(*corresponding author)

Keywords: Machine learning, machine vision, tool wear classification, flank wear

Abstract

This study proposes an automated framework for online cutting tool wear classification in CNC turning using low-cost optical equipment and Convolutional Neural Networks (CNNs). Longitudinal turning experiments were performed on CK45 medium carbon steel using a HAAS TL1 lathe under dry machining conditions. Tool wear evolution was monitored via a lathe-mounted digital microscope, with images classified into three distinct stages: Low (Vb<160μ m), Medium (160≤Vb≤200μ m), and Critical (Vb>200μ m). A shallow CNN architecture, consisting of three convolutional blocks and a Softmax output layer, was developed to balance model complexity with computational efficiency for potential edge deployment. To enhance robustness against positional changes, data augmentation techniques including random translations and rotations were applied. The results demonstrate good performance, with the model achieving 94.7% accuracy and a weighted F1score of 95.4% on the testing subset. While the model showed exceptional performance in identifying Low and Medium wear, data scarcity in the Critical wear class remained a limiting factor for recall. Overall, the study confirms that shallow CNNs can accurately capture spatial hierarchies for imagebased wear assessment.

Introduction

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

In the era of Industry 4.0, the development of automated monitoring frameworks reduces reliance on manual tasks and enables data-driven decision making [1]. In this context, cutting tool wear monitoring still remains a topic that gathers a lot of interest due to the fact that continuous, accurate tool wear classification can directly improve productivity, quality and cost. Productivity is improved since adaptive control strategies can be applied, where feeds and speeds can be optimized [2]. Additionally, the number of unplanned downtimes for replacing worn out or broken tools can be reduced [3]. Quality is improved by achieving surface finish and dimensional accuracy values with less variability [4]. Cost is reduced as tool life can be more accurately estimated and therefore predictive maintenance strategies can be applied [5]. Finally, deploying automated monitoring frameworks at the process level enables 100% inspection strategies. This eliminates the sampling risk associated with Type I and Type II errors, thereby reducing uncertainty in quality assurance.

Typically, automated cutting tool wear classification is based on indirect methods where various signal types such as cutting forces, vibrations, acoustic emissions, motor loads etc., provide corresponding data modalities that are used by machine learning (ML) models that try to infer the actual wear state of the cutting tool [6]. However, when it comes to transferring these research approaches from the lab to real shop-floors, four main challenges appear. Firstly, instrumenting the machine tools requires significant cost for sensors, data acquisition and storage setups [7]. Secondly, human expertise is also a requirement since the collected signals need to be preprocessed and suitable features need to be extracted to develop the ML predictive models. Thirdly, a lot of relevant data need to be collected so that the ML models can achieve satisfactory performance [8]. Finally, particularly
for the case of vision systems, environmental variability in terms of inconsistent lighting, coolant spray and equipment vibrations plays a significant role for their practical deployment [9].

This paper aims to address these gaps by proposing a low-cost, image-based approach for the online classification of the cutting tool's flank wear focusing on turning processes. More specifically, the proposed approach involves the use of convolutional neural networks (CNNs) in order to associate image features with each of the three typical cutting tool wear stages (initial, steady-state and accelerated wear) [10]. Using images as inputs to the CNN addresses two of the previously identified challenges. Images are easier and less costly to acquire compared to other signal types. The CNN's ability to automatically extract and evaluate spatial features from raw pixel data makes them exceptional for more complex visual pattern recognition and reduces data preprocessing efforts. Finally, the dataset size limitation can be partially addressed by adopting data augmentation techniques.

In order to obtain the required experimental dataset, dry machining experiments were performed with constant cutting parameters. After a pre-determined number of passes were completed, an image of the cutting tool was captured using a low-cost digital microscope that was mounted on the lathe and then the cutting tool was removed to measure its actual flank wear using a stereoscopic microscope. The following sections provide the details regarding both the experimental as well as the CNN model development procedures and the achieved results.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

Materials. The selected workpiece material for performing the experimental investigation was CK45 medium carbon steel due to its very good machinability and wide range of applications across various industrial sectors such as automotive, heavy machinery and construction. Table 1 presents the typical chemical composition of the selected material.

Table 1. CK45 steel chemical composition.

C (wt%) | Si (wt%) | Mn (wt%) | P (wt%) | S (wt%)
0.42 – 0.50 | ≤ 0.40 | 0.50 – 0.80 | ≤ 0.035 | ≤ 0.03

Turning experiments. Longitudinal turning experiments were performed using two CK45 cylindrical workpieces ( 76 mm diameter, 610 mm length) on a HAAS TL1 CNC lathe (Fig. 1) using a TNMG160408-UG YG3020 carbide insert that was mounted on a MTJNR 2525 M16 tool holder. The cutting parameters were selected to accelerate flank wear development, while remaining within the supplier's recommended ranges. Thus, cutting speed ( Vc ), feed ( f ) and depth of cut ( ap ) were set at 280 m/min,0.35 mm/rev and 0.8 mm , respectively. The turning experiments were performed under dry machining conditions, i.e. no cutting fluid was used.

[picture]

Fig. 1. HAAS TL1 CNC lathe.

Fig. 1. HAAS TL1 CNC lathe.

Wear evaluation. The cutting tool wear was evaluated in terms of flank wear width using the Vb parameter and according to the ISO 3685 standard. A Leica MZ6 stereo-microscope was used along
with the ImageJ image analysis software to capture and analyze images of the cutting insert, resulting in the precise measurement of the maximum flank wear width (Fig .2). In order to ensure consistent monitoring of the actual wear evolution, the cutting tool was removed from the lathe and inspected in the stereo-microscope each time that a cutting length of 1000 mm , corresponding to two cutting passes, had been completed. Similarly, after each cutting interval and before the tool was removed, images of the flank face were captured at a 640×480 pixel resolution with the help of a low-cost digital microscope, which was directly mounted on the lathe (Fig. 2). The cutting tool wear evolution is shown in Fig. 3 as a function of machining time. Three distinct stages can be observed, corresponding to initial, steady-state and accelerated wear patterns, respectively.

[picture]

Fig. 2. Labelled image of cutting tool captured by Leica MZ6 (left) and low-cost digital microscope mounted on the CNC lathe (right).

Fig. 2. Labelled image of cutting tool captured by Leica MZ6 (left) and low-cost digital microscope mounted on the CNC lathe (right).

[picture]

Fig. 3. Cutting tool wear evolution.

Fig. 3. Cutting tool wear evolution.

It has to be emphasized that the cutting tool's removal from the lathe and subsequent wear measurement is only necessary so that the ground truth regarding wear evolution is established. This is a necessary step in any approach that involves the development of deep learning models and is performed as part of the model's training phase. After the model has been trained, it is expected to be able to classify the tool wear using only the images captured directly from the low-cost microscope and therefore no tool removal from the lathe is required. This corresponds to the model's inference phase.

Data preprocessing. Based on Fig.3, the wear measurements were divided in distinct classes: a) Low Wear (Vb<160μ m), b) Medium Wear (160≤Vb≤200μ m) and c) Critical Wear (Vb>200μm ), thereby formulating the investigated problem as a three-class classification task. Therefore, following this procedure, a dataset was created that consisted of 93 images, captured by the lathe-
mounted microscope, and associated flank wear width measurements, each belonging in the above classes. In order to accommodate CNN model development, additional preprocessing techniques were applied. Data augmentation was performed to increase dataset size and variability by applying random translations ( ±5 pixels) and rotations (±15∘) to the captured images. These geometrical transformations aim to increase model robustness against positional and orientation changes. Furthermore, the dataset was randomly split into training, validation and testing subsets following a 80/10/10 ratio of total available data. The validation subset was used for early stopping, while the testing subset was used to evaluate the model's generalization performance when presented with unknown data.

Convolutional Neural Networks. CNNs are a specialized class of deep learning models, which is designed to process data with a known grid-like topology. Unlike traditional multilayer Artificial Neural Networks (ANNs) that flatten input data into a single vector, thereby discarding crucial structural information, CNNs preserve spatial and temporal correlations through the application of learnable kernels (filters) [11]. A typical CNN is constructed by stacking different layers that transform the input into an output class score through a differentiable function. Three main layer types are involved; convolutional, pooling and fully connected. The convolutional layer consists of learnable kernels that convolve across the dimensions of the input (typically width and height of an image). This process produces 2D activation maps (feature maps) that represent the response of that filter at every spatial position, effectively automatically extracting hierarchical spatial features such as edges, textures etc. The pooling layer is inserted between successive convolutional layers so as to perform non-linear downsampling. Its primary function is to progressively reduce the spatial size of the representation, essentially reducing the number of parameters. Max pooling, which outputs the maximum value within a defined rectangular window, is the most common approach. The fully connected layers mirror the structure of a feedforward neural network. The final fully connected layer typically uses a Softmax activation function to produce probability distributions over the target classes. For the investigated problem, the input to the CNN is the image captured by the low-cost microscope mounted on the CNC lathe and the output is the level of the cutting tool wear as it is described by one of the three defined classes. The expectation is that through training the model will be able to associate image features with the wear evolution mechanisms.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

Shallow CNN model. The architecture of the developed CNN model consisted of three Convolutional Blocks, each containing a Convolutional Layer, a Batch Normalization and a ReLU activation function. It also included two Max-Pooling Layers ( 2×2 ) after its first two blocks for dimensional reduction, a dropout layer (rate =0.5 ) before the final layers that helps to prevent overfitting, a fully connected layer and a Softmax layer for the final classification into the three wear classes (Fig. 4). The model's hyperparameters corresponding to the convolutional layers are presented in Table 2. The choice of a shallow architecture was made as a compromise between model complexity and computational efficiency, which are important considering the limited dataset size.

[picture]

Fig. 4. CNN model architecture.
Table 2. CNN model's convolutional layer hyperparameters.

Fig. 4. CNN model architecture. Table 2. CNN model's convolutional layer hyperparameters.

Convolutional

Layer

Type

Kernel

Size

Stride
Padding
Filters

1 | Conv2D | mathematical formula | 1 | Same | 16
2 | Conv2D | mathematical formula | 1 | Same | 32
3 | Conv2D | mathematical formula | 1 | Same | 64

The network was trained using the Stochastic Gradient Descent with Momentum (SGDM) optimizer with an initial learning rate of 0.001 , a mini-batch size of 32 , over 25 epochs. The training performance, in terms of achieved accuracy and cross-entropy loss over each iteration, is shown in Fig. 5. As can be observed both training and validation curves exhibit smooth convergence behavior, finally reaching 100% and 94.7% accuracy, respectively. The complete results across training, validation and testing subsets are visualized through the respective confusion matrices in Fig. 6a-c.

[picture]

Fig. 5. Shallow CNN model training history.

Fig. 5. Shallow CNN model training history.

[picture]

Fig. 6a. Confusion matrix for training subset.

Fig. 6. a. Confusion matrix for training subset.

Fig. 6b. Confusion matrix for validation subset.

[picture]

Fig. 6c. Confusion matrix for testing subset.

Fig. 6. c. Confusion matrix for testing subset.

Additional performance metrics such as precision, recall and F1-score are also calculated in order to fully evaluate the model's predictive performance. Table 3 presents the relevant values for all the above metrics, focusing on the testing subset in order to assess the model's generalization capability. Out of the 19 cases of the testing subset, only one was misclassified by the shallow CNN model (a case of Critical Wear was misclassified as Medium Wear), resulting in overall accuracy equal to 94.74%.

Table 3. CNN's performance metrics for the testing subset.

Class
Precision
Recall
F1-score
Accuracy

Weighted

F1-score

Low Wear | 1.000 | 1.000 | 1.000 | 0.947 | 0.954
Medium Wear | 0.857 | 1.000 | 0.923
Critical Wear | 1.000 | 0.500 | 0.667 |

The model demonstrates exceptional performance in identifying Low Wear, with perfect precision and recall. For Medium Wear, the model achieves perfect recall, meaning it successfully captured all true medium wear instances; however, its precision is slightly lower ( 0.857 ) because it misclassified one "Critical Wear" instance as "Medium". For Critical Wear, while the precision is perfect, i.e. the model never falsely identifies a lower wear state as critical, the recall is only 0.500 . This is again due to the fact that out of the two "Critical Wear" instances, one was misclassified as "Medium Wear". Since there are only two instances of the "Critical Wear" class, it is not straightforward to draw meaningful conclusions regarding the model's reduced performance regarding this class.

In order to deal with this issue, the weighted average F1-score metric, which accounts for class imbalance by weighting each class score by its number of instances, was also calculated and found equal to 0.954 . The weighted F1-score reflects how the model would perform on a random sample from this specific, imbalanced population, indicating that for nearly 95% of the cases encountered in this specific environment, the model will be correct and reliable. In any case, to avoid any errors related to the Critical Wear class further experimental investigation and/or additional data augmentation methods should be investigated to account for the class imbalance problem.

Conclusions

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

This work presented an automated framework for online cutting tool wear monitoring utilizing lowcost optical equipment and CNNs. The achieved results show that shallow CNN architectures are highly effective at capturing the spatial hierarchies necessary for accurate image-based tool wear assessment. The main conclusions can be summarized as follows:

The proposed shallow CNN achieved an accuracy of 94.7% and a weighted F1-score of 95.4% in the testing subset.

The class imbalance associated with the Critical Wear class is the limiting factor for even better model performance, especially regarding recall.

Computationally efficient models provide a viable solution suitable for edge deployments in real manufacturing environments.
To improve this work several future research directions can be identified. The main priority will be to address data scarcity of the critical wear class. Several alternatives will be considered ranging from additional experimental investigations with pre-worn tools, to adopting class-weighted loss functions and even examining synthetic data generation through Generative Adversarial Networks (GANs). Transfer learning approaches through publicly available datasets will also be adopted to enhance model generalization. Furthermore, formulating the wear evolution as a regression rather than a classification problem will allow the direct prediction of the flank wear width and therefore offer a more refined and useful monitoring framework. Finally, additional validation by considering different cutting parameters, materials and tool geometries will be performed.

References

The original version of this paper is available on https://www.scientific.net/DDF.450.119.pdf

B.T. Saatci, M. Ulas, T. Gurgenc, Hybrid AI Systems for Tool Wear Monitoring in Manufacturing: A Systematic Review, Appl. Sci. 16(1) (2026) 208. https://doi.org/10.3390/app16010208

Y. Jiang, J. Huang, K. Liu, D. Zhao, H. Liu, Y. Wang, Intelligent machining technology for online monitoring and control of tool condition: A review, Front. Mech. Eng. 20 (2025) 50.

S. Li, M. Li, Y. Gao, Deep Learning Tool Wear State Identification Method Based on Cutting Force Signal, Sensors. 25(3) (2025) 662.

K. Wang, A. Wang, L. Wu, G. Xie, Machine Tool Wear Prediction Technology Based on MultiSensor Information Fusion, Sensors. 24(8) (2024) 2652.

A.D. Patange, R. Jegadeeshwaran, N.C. Dhobale, Milling cutter condition monitoring using machine learning approach, IOP Conf. Ser.: Mater. Sci. Eng. 624 (2019) 012030.

P. Unal, B.U. Deveci, A.M. Ozbayoglu, A Review: Sensors Used in Tool Wear Monitoring and Prediction, In: Awan, I., Younas, M., Poniszewska-Marańda, A. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2022. LNCS 13475 (2022). Springer, Cham.

K. Jemielniak, Contemporary challenges in tool condition monitoring, Journal of Machine Engineering, 19(1) (2019) 48-61.

F. Ramzan, D. Reforgiato Recupero, A Literature Review on Enhancing Predictive Maintenance in Smart Manufacturing Industries: Fostering Human-Technology Collaboration and Overcoming Data Scarcity Limitations with Advanced AI Models, Oper. Res. Forum 6 (2025) 181.

A.S. Kumar, A. Agarwal, V.G. Jansari, K.A. Desai, C. Chattopadhyay, L. Mears, Realizing onmachine tool wear monitoring through integration of vision-based system with CNC milling machine, J. Manuf. Syst. 78 (2025) 283-293.

B.J. Sujay, S. A. Singh, A. Agarwal, K.A. Desai, L. Mears, Identifying tool wear stages in turning process through machined surface image analysis using convolutional neural network, Manuf. Lett. 44 (2025) 678-686.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/machining-cutting-and-severe-plastic-deformation-processes/978-3-0364-1988-6

Defect and Diffusion Forum, ISSN: 1662-9507, Vol. 450, pp 127-137

doi: 10.4028/p-TB3Cbv

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-28

Revised:
2026-02-24

Accepted:
2026-02-25

Online:
2026-04-15

Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

The original version of this paper is available on https://www.scientific.net/DDF.450.127.pdf

ORTIZ-DE-ZARATE Gorka 1,a,a, RODRÍGUEZ Iñigo 1,b, ORUNA Ainara 1,c, OTALORA Harry 1, d, ETXEBESTE Mikel 1,e, and ARRAZOLA Pedro J. 1,f

1 Mondragon Unibertsitatea, Faculty of Engineering, Loramendi 4, Arrasate-Mondragón, 20500, Spain

a  gortizdezarate@mondragon.edu, b  irodriguez@mondragon.edu, c  ainara.oruna@alumni.mondragon.edu, dhyotalora@mondragon.edu, emetxebeste@mondragon.edu, f  pjarrazola@mondragon.edu (*corresponding author) 

Keywords: Recycling; Oil-free machining; Sub-zero cooling; Titanium

Abstract

Titanium is a Critical Raw Material for the European aerospace sector, yet its manufacturing is characterized by high buy-to-fly ratios and significant waste in the form of chips. Solid-state recycling (SSR) presents a low-energy alternative to remelting for chip revalorisation. However, its viability is strictly limited by chip oxidation. This study investigates the influence of milling parameters (cutting speed and radial depth of cut), and coolants (emulsion, LCO2 and dry), on cutting forces and chip quality (morphology and oxidation) to define a process window for generating low oxidation chips, enabling further SSR routes. By correlating cutting forces with chip analysis, the results reveal that emulsion cooling yields chips with the lowest oxidation, despite potential oil contamination of the feedstock. While LCO2 effectively reduces oxidation at lower material removal rates, high thermal loads overwhelm its cooling capacity, resulting in oxidation comparable to dry cutting. These findings establish the machining parameters necessary to produce high-quality, recyclable feedstock, bridging the gap between subtractive manufacturing and circular material flows.

Introduction

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Titanium holds great importance for the European Union's (EU) industrial sectors, primarily due to its high strength-to-weight ratio, superior corrosion resistance, and biocompatibility properties. However, the EU's position within the global titanium value chain presents significant strategic vulnerabilities. As a net importer of titanium products with an import-to-export ratio of 2:1, the EU relies heavily on external suppliers [1]. Critically, the region lacks both domestic reserves of titanium ore and sponge production infrastructure, creating a dependency on supply chains dominated by geopolitically sensitive regions. Due to these vulnerabilities, the European Commission designated titanium metal as a Critical Raw Material in 2020 [2].

The aerospace sector dominates European consumption, accounting for 67% of all titanium products, with Ti6Al4V being the primary alloy [3]. Future demand pressures will intensify this challenge; according to global market forecasts, approximately 43,420 new aircraft are anticipated over the next two decades to accommodate a 3.6% annual growth in passenger traffic, as seen in Figure 1 [4]. This trajectory suggests a dramatic increase in titanium demand precisely when supply security remains uncertain.

[picture]

Fig. 1. Forecast of air traffic and aircraft delivery by Airbus [4].

Fig. 1. Forecast of air traffic and aircraft delivery by Airbus [4].

Current manufacturing practices further undermine resource efficiency. Aeronautical machining commonly exhibits buy-to-fly ratios of 9:1 or higher, meaning nearly 90% of the material becomes chips [5]. Although these chips retain the thermomechanical properties of the virgin alloy, they are typically exported or recycled via conventional remelting [1]. Despite lower energy demand compared to primary production, remelting remains energy-intensive and entails material losses through oxidation and dross formation.

Solid-state recycling (SSR) offers a viable alternative by consolidating chips without melting, thereby reducing energy consumption and promoting circularity within the EU [6]. However, industrial deployment is constrained by process stability and, critically, chip quality-particularly morphology and oxidation state. In Ti6Al4V SSR, chip morphology, surface cleanliness, and interstitial content (O, N, C) govern consolidation success [7] and compliance with aeronautical specifications [8].

The machining of Ti6Al4V presents inherent challenges due to its high-temperature strength and low thermal conductivity. Tool-chip interface temperatures often exceed 800−1000∘C, promoting surface oxidation [9]. These oxides embrittle chips [10], reducing ductility and compromising SSR feedstock quality. Therefore, minimizing oxygen uptake during machining is essential for SSR viability [11].

Recent research has established that oxygen is not merely a passive environmental factor but an active process variable. Controlling it can alter chip formation mechanisms and tribological conditions [12]. Machining under oxygen-free atmospheres reduces cutting forces, tool wear, and subsurface deformation by suppressing tribo-oxidation [13-16], thereby enhancing chip suitability for SSR. However, fully inert chambers are impractical for industrial implementation.

Liquefied carbon dioxide ( LCO2 ) cooling represents a scalable alternative. It significantly lowers cutting temperatures [17], reduces tool wear [18], and avoids oil contamination associated with emulsions [19]. By delivering localized sub-zero cooling, LCO2 mitigates oxidation-driven degradation and may bridge the gap between conventional and inert machining.

Despite these advances, no study has systematically quantified the combined effect of milling parameters and cooling strategies to define an operational window for producing low-oxidation, SSRready chips. This work addresses that gap by evaluating the influence of machining parameters and cooling/lubrication conditions on chip oxidation. Process forces are monitored as a proxy for deformation energy, and chip quality is assessed via macroscopic oxidation indicators to identify parameter sets that minimize oxygen uptake. This research aims to provide a validated guideline for transforming titanium machining waste into a valuable resource for circular manufacturing.

Methodology

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Experimental plan and procedure

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A parametric study was conducted to define the machining process window. Cutting speed ( Vc ) was varied from 40 to 240 m/min, while the radial depth of cut ( ae ) ranged between 0.5 and 1 mm . Cutting speed primarily governs cutting temperature, while radial depth of cut influences chip
thickness and thus the rate of heat evacuation through the Ti6Al4V chips. Cooling and lubrication conditions were also modified, comparing conventional emulsion cooling with liquid carbon dioxide (LCO2)-assisted and dry machining (Figure 2a).

All remaining machining parameters, such as the feed per tooth ( fz ) and the axial depth of cut ( ap ), were kept constant at 0.1 mm/ tooth and 15 mm , respectively. Each condition was repeated three times to assess experimental variability. The combinations of highest Vc and ae were not tested under LCO2 and dry conditions due to the risk of tool failure caused by excessive thermomechanical loads.

Analysed output parameters for defining a sustainable machining process window for lowoxidation chips were: (i) cutting forces, (ii) chip oxide colour, and (iii) chip microstructure (Figure 2a). Cutting forces were recorded during machining, and chips were collected ex-situ in a dedicated clean container to avoid cross-contamination. Chip oxidation was qualitatively assessed via colour using a Leica DMS 1000 microscope at ×5 magnification. This evaluation followed established literature correlations [13] linking oxide layer composition and thickness to optical appearance.

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Fig. 2. (a) Experimental conditions and monitored outputs; (b) Chip oxide layer colour according to the oxide layer composition, adapted from [20]; (c) Colour grading of the chips selected for the study.

Fig. 2. (a) Experimental conditions and monitored outputs; (b) Chip oxide layer colour according to the oxide layer composition, adapted from [20]; (c) Colour grading of the chips selected for the study.

According to Keren et al. [20], chip colour reflects the oxide stratification and thermal history (Figure 2b). At lower cutting temperatures, chips exhibit a yellow/golden appearance, associated with a relatively thick single-layer TiO2+VO2 scale (~2μ m), with colour dominated by TiO2. At higher temperatures ( >1000∘C ), a bilayer oxide forms, consisting of an inner Al2O3 layer and an outer TiO2+VO2 layer (~1.5−1.7μ m), leading to a blue coloration driven by both VO2 formation and optical interference.

Under the highest thermal loads, the total oxide thickness decreases: the outer TiO2+VO2 layer thins to ~700−800 nm, while the inner Al2O3 layer grows. This outer-layer thickness promotes constructive interference in the violet range, resulting in a purple appearance. The formation of an inner Al2O3 layer indicates a high-temperature oxidation regime in which Ti- and V-rich oxides have already formed and Al is subsequently driven to react. This condition correlates with enhanced oxygen diffusion beneath the oxide scale into the titanium matrix [21]. Therefore, the observed colour evolution serves as a direct indicator of oxide chemistry, peak cutting temperature, and the extent of the underlying heat-affected zone (HAZ), and consequently reflects its suitability for SSR (Figure 2c).

After classifying the chips produced under the different cutting conditions according to their oxidation colour, representative samples were selected for microstructural examination of both the free and back surfaces of the chip (Figure 3a). The microstructure was analysed on polished cross sections that were subsequently chemically etched using Kroll's reagent ( 100 mLH2O,3.5 mLHF, and 7 mLHNO3 ). This preparation enabled clear observation of the free surface (where the characteristic saw-tooth morphology associated with adiabatic shear banding develops) as well as the back surface (Figure 3b). The back surface is of particular interest because it slides along the tool rake face and is subjected to high contact pressures and severe shear stresses [22]. Consequently, the influence of cutting temperature on the chip microstructure is expected to be most pronounced in this region, primarily through thermally driven phase transformations [23].

[picture]

Fig. 3. Microstructure analysis of the formed chips: (a) Detail of chip free and back surface; (b) Example of a chip cross-section microstructure and most relevant features.

Fig. 3. Microstructure analysis of the formed chips: (a) Detail of chip free and back surface; (b) Example of a chip cross-section microstructure and most relevant features.

Experimental setup

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Machining tests were performed on a LAGUN GVC 1000-HS machining centre equipped with through-spindle LCO2 delivery. A four-flute end mill (Gühring 60818.000) with four 0.5 mm internal cooling channels was mounted in a shrink-fit tool holder (Figure 4a). The LCO2 flow rate (qLCO2) was set to 120 g/min, while emulsion coolant ( pemul .), when used, was supplied externally at 15 bar. Ti6Al4V workpiece was mounted on a Kistler 9139AA dynamometer (Figure 4b), and cutting forces were acquired at 10 kHz over a machined length of 105 mm to ensure steady-state.

[picture]

Fig. 4. (a) End mill and tool holder employed; (b) Experimental setup for force acquisition.

Fig. 4. (a) End mill and tool holder employed; (b) Experimental setup for force acquisition.

The trigger for cutting force signal acquisition was synchronised with the machine-tool axis movements in order to minimise the time that the LCO2 jets were impinging on the Kistler dynamometer, and to make this time consistent across experiments. Controlling this was important as the LCO2 can cool the dynamometer and create thermal contractions on the piezoelectrics, creating signal drift, if the jets impinge for too long on the sensor. The acquired force signals were subsequently processed using a dedicated MATLAB script. A low pass filter was applied at 10% of the natural frequency of the experimental setup ( 1500 Hz ) and the region of the signal that corresponded to the cutting process was detected via up and down flanks. The selected representative force values for comparison between experimental conditions were the maximum filtered values of the stable region of the signal, as shown in Figure 5.

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Fig. 5. Processing of the force signals, detection of the cutting region and representative values.

Fig. 5. Processing of the force signals, detection of the cutting region and representative values.

Results and Discussion

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Cutting forces

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The evolution of cutting force components for each experimental condition is presented in Figure 6. The analysis focuses primarily on the tangential ( Fx ) and radial ( Fy ) forces. The axial component (Fz) was found to be negligible, as the side milling operation at low radial immersions ( ae<25% of tool radius) generates minimal vertical loading, and the tested variables showed no significant influence on this direction (Figure 6c).

At low radial depth of cut ( ae=0.5 mm ), no significant differences were observed between cooling strategies. However, a slight decreasing trend in the tangential component ( Fx ) is evident as cutting speed ( Vc ) increases. This phenomenon is characteristic of titanium alloys and is attributed to thermal softening within the primary shear zone. As cutting speed increases, the adiabatic temperature rise reduces the yield strength of the material, thereby lowering the specific cutting energy required for chip formation [23].

In contrast, at the higher radial depth of cut ( ae=1.0 mm ), the influence of the cooling strategy becomes pronounced. Radial forces (Fy) increased substantially across all conditions due to the larger engagement angle and chip load (Figure 6b). Notably, with regard to the tangential component, both LCO2 and dry conditions generated significantly higher forces than emulsion. This disparity is attributed to the superior tribological performance of the emulsion; its lubricating effect reduces the friction coefficient at the tool-chip interface. Conversely, the lack of lubrication in LCO2 and dry environments promotes higher temperatures and, potentially, adhesion of the workpiece material to the rake face [24].

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Fig. 6. Cutting forces results: (a) Tangential forces; (b) Radial forces; (c) Axial forces.

Fig. 6. Cutting forces results: (a) Tangential forces; (b) Radial forces; (c) Axial forces.

Chip macroscopic analysis - Oxide colour

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Following the oxide colour grading methodology described in Figure 2b and c , a qualitative comparison was performed by examining the oxide colours of chips produced under the different machining parameter sets. As shown in Figure 7, chips generated at low radial depth of cut values ( ae=0.5 mm ) exhibit more regular and well-defined morphology, whereas those produced at higher ae values display irregular shapes and chip agglomeration under LCO2-assisted and dry machining conditions. This behaviour can be attributed to the higher cutting temperatures associated with these conditions compared to emulsion-cooled machining. Increased friction at the tool-chip interface may have hindered stable shearing of the Ti6Al4V material and promoted local adhesion, leading to partial welding of the chips.

Notably, cutting speed ( Vc ) had the greatest effect at high radial depths of cut in both LCO2-assisted and dry cutting, whereas emulsion cooling consistently produced stable, low-oxidation chips, even under severe machining conditions.

Among the investigated strategies, emulsion cooling proved to be the most effective in mitigating chip oxidation due to a combined effect of tribological, physical, and thermal mechanisms. Firstly, the superior lubricity of the emulsion reduced tool-chip friction, thereby lowering heat generation at the source. Secondly, the continuous flood physically shielded the freshly formed chip surfaces from atmospheric oxygen. Finally, regarding thermal management, although LCO2 reaches significantly lower temperatures ( −78∘C ) via the sublimation of dry ice particles, its heat removal capability is limited by the low thermal conductivity of the gas phase and the brief contact time at the interface. In contrast, the liquid emulsion acts as a more efficient thermal sink, utilising the high heat capacity and convective heat transfer of the water-oil mixture to extract heat more effectively, as demonstrated by Pušavec et al. [25]. This superior cooling efficiency reduced the thermal load on the chips compared to LCO2 or dry machining.

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Fig. 7. Macroscopic chip analysis and qualitative oxide colours comparison along cutting conditions.

Fig. 7. Macroscopic chip analysis and qualitative oxide colours comparison along cutting conditions.

As illustrated in the bar chart in Figure 7, oxide colours associated with a significant development of an inner Al2O3 layer were predominantly observed under dry and LCO2-assisted machining
conditions, particularly at high cutting speeds or large radial depths of cut. These oxide states are considered potentially the most brittle and therefore the most detrimental for SSR routes [20].

Chip microstructure analysis

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After the macroscopic analysis of the chips, one representative chip of each colour (silver, golden, and blue/purple), indicated by arrows in the bar chart in Figure 7, was selected for a more detailed microstructural examination following the methodology shown in Figure 3. The main microstructural observations are presented in Figure 8. The analysis reveals clear differences in the chip back surface among the different conditions and oxide colours. Noticeable microstructural alterations are observed in the golden and blue/purple chips (Figure 8b and c) compared to the silver-coloured chips (Figure 8a).

According to Sun and Guo [26], the localized temperature rise taking place at the tool-chip interface at the secondary shear zone (where the back surface is formed), leads to a reduction in the volume fraction of the β-phase and a severe elongation of the remaining grains. The thickness of this phase-transformed layer increases with cutting speed, correlating with the higher thermal loads generated. However, this thermal influence is highly localized; the bulk chip microstructure remains largely unaffected due to the rapid quenching effect of the surrounding environment.

Regarding the bulk chip morphology, adiabatic shear bands were observed under all tested conditions. In the golden and blue/purple chips-indicative of higher cutting temperatures-the sawtooth frequency was lower, with more pronounced serrations, than in the silver chips. This increased chip segmentation is associated with larger fluctuations in cutting forces [27] and temperature [28], reflecting greater machining instability, as the shear bands accommodate higher localized strain [23]. These effects may ultimately degrade the machined surface topography [29].

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Fig. 8. Microstructure analysis of chips with different oxide colours: (a) Silver; (b) Golden; (c) Blue/Purple.

Fig. 8. Microstructure analysis of chips with different oxide colours: (a) Silver; (b) Golden; (c) Blue/Purple.

Conclusions

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This study investigated milling parameters and cooling strategies to produce low-oxidation Ti6Al4V chips suitable for solid-state recycling (SSR), providing insights into process windows for circular material flows. The main findings are:

Flood emulsion cooling proved to be the most robust strategy, consistently producing silver coloured, low-oxidation chips across all tested cutting speeds and radial depths of cut. It acts as a dual mechanism: lubricating the cutting zone to reduce heat generation at the source (reflected in lower tangential forces) and creating a physical barrier against atmospheric oxygen. This combination enables higher material removal rates while effectively preventing chip oxidation.

LCO2 cooling, a clean oil-free alternative, showed a limited process window. It effectively suppressed oxidation at lower material removal rates ( ae=0.5 mm ). However, under higher thermal loads ( ae=1.0 mm and elevated cutting speeds), the cooling capacity of LCO2 was exceeded, leading to "blue" and "purple" oxidized chips similar to those from dry cutting.

A trade-off between productivity and circularity is evident. LCO2 cooling produces clean, low-oxide chips at the cost of reduced material removal rates, whereas emulsion cooling supports higher productivity but may require subsequent cleaning prior to SSR.

Machining conditions that reduce friction, as achieved with emulsion cooling, are more effective in limiting oxidation than approaches that focus solely on lowering cutting temperature, such as LCO2 cooling, indicating that forces serve as a reliable proxy for assessing potential feedstock quality.

Chip microstructure and shear behaviour are strongly influenced by localized temperatures, with chips exhibiting golden or blue/purple oxides showing back-surface alterations, reduced β-phase content, elongated grains, and pronounced adiabatic shear bands, which indicate higher temperatures and greater machining instability compared with silver chips.
Future work will focus on the detailed chemical and microstructural characterisation of the oil and oxide layers on the chips, including quantifying the emulsion-derived oil film and measuring oxide thickness and composition. The aim is to correlate these layer properties with the interfacial bonding and performance of consolidated SSR components.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/DDF.450.127.pdf

The authors would like to express their great appreciation to REVIR (KK-2025-0114) and DIGIAERO (ZL-2024/00651) projects for their support to carry out this research.

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The original version of this paper is available on https://www.scientific.net/DDF.450.127.pdf

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Keywords Index

A

Adaptive Mesh Refinement On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Aluminum Alloy Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Artificial Intelligence Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

B

Boring Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

C

Cel On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Chatter Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

Chip Breakability The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Composite Materials Static Finite Element Modeling of Milling in Flexible Composite Plates

Crystal Structure Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Cutting Fluid Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

Cutting Force Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Cutting Forces Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

D

Deep Learning Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Drilling Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Dry Gear Hobbing Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

E

Ebsd Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

F

Fem Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

Fiber Metal Laminates (fmls) Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Finite Element On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Flank Wear Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Flexible Parts Static Finite Element Modeling of Milling in Flexible Composite Plates

Fly Cutting Test Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

G

Gear Hobbing Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

J

Johnson-cook On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

L

Laser Powder Bed Fusion Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Laser Surface Texturing Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Laser Texturing Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Layer Thickness Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

M

Machinability Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Machine Learning Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling; Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Machine Vision Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Machining Scaling Methodology for Large Boring Bars with Tuned Mass Dampers; Static Finite Element Modeling of Milling in Flexible Composite Plates

Microinjection Molding Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Milling Static Finite Element Modeling of Milling in Flexible Composite Plates

Milling Process Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Modeling Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

P

Polymer Turning The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Process Monitoring Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

R

Recycling; Oil-free Machining; Sub-zero Cooling; Titanium Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

Remaining Useful Life Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

S

Surface Integrity Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Sustainable Manufacturing Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

T

Taylor Impact Test On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Temperature Distribution Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Textured Tools The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Thermoplastic Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Tool Wear Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Tool Wear Classification Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Trimming Operation Static Finite Element Modeling of Milling in Flexible Composite Plates

Tuned Mass Damper (tmd) Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

Turn-milling Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

W

Wear Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials; Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Authors Index

A

Afrasiabi, M. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Ambrogio, G. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Arrazola, P. Scaling Methodology for Large Boring Bars with Tuned Mass Dampers; Static Finite Element Modeling of Milling in Flexible Composite Plates; Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

Arrieta, I. Scaling Methodology for Large Boring Bars with Tuned Mass Dampers

Artiushenko, V. Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

B

Bambach, M. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Basile, V. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Benardos, P. Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Bertolini, R. The Role of Tool Texture Design on the Chip Formation in PEEK Machining; Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Beutner, M. Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials; Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Bolvardi, H. Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Bottin, A. The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Bruschi, S. The Role of Tool Texture Design on the Chip Formation in PEEK Machining; Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

D

Dambly, V. Static Finite Element Modeling of Milling in Flexible Composite Plates

Ducobu, F. On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation; Static Finite Element Modeling of Milling in Flexible Composite Plates

E

Eich, M. Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

Etxebeste, M. Scaling Methodology for Large Boring Bars with Tuned Mass Dampers; Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

F

Fassi, I. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Ferrisi, S. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

G

Gartzonikas, D. Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Ghinatti, E. Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Gonzalez, H. The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Granjon, A. Static Finite Element Modeling of Milling in Flexible Composite Plates

Guido, R. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

H

Hackert-Oschätzchen, M. Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling; Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials; Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

I

Islam, T. Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

K

Karkalos, N. Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Karpuschewski, B. Potential of Cutting Fluid Application in Gear Hobbing of High-Strength Materials

L

Lang, S. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions

Lazoglu, I. Static Finite Element Modeling of Milling in Flexible Composite Plates

Lerez, C. Exploring Artificial Intelligence for Tool Wear Prediction in Turn-Milling

Liu, Z. Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Lümkemann, A. Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

M

Markopoulos, A. Classification of Cutting Tool Wear Evolution in CNC Turning Using Convolutional Neural Networks

Matsumura, T. Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Modica, F. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

Morano, C. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study

N

Nutte, M. Static Finite Element Modeling of Milling in Flexible Composite Plates

O

Oruna, A. Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

Otalora, H. Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

P

Pan, S. Cryogenic Machining of a Laser Powder Bed Fusion-Processed AISi7Mg Aluminum Alloy

Pantalé, O. On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Paucke, N. Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

R

Rivière-Lorphèvre, E. Static Finite Element Modeling of Milling in Flexible Composite Plates

Rodriguez, I. Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips

Rotella, G. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study; Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

S

Saffioti, M. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study; Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Simonetto, E. Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Spitaels, L. On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation

Sposato, A. Laser Surface Texturing of Cylindrical Ejector Pins for Functionalization of Injection Molding Systems: A Preliminary Study; Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

T

Tamura, S. Experimental and Analytical Study of Cutting Force and Temperature in Drilling of CF/PEEK Composite

Thomas, K. Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

U

Ukar, E. The Role of Tool Texture Design on the Chip Formation in PEEK Machining

Umbrello, D. Condition-Robust Tool Remaining Useful Life Prediction under Highly Variable Milling Conditions; On the Use of Adaptive Mesh Refinement for Modelling the Taylor Impact Test with the CEL Formulation; Drilling of Fiber Metal Laminates for Aerospace Applications: Experimental Evaluation of Surface Treatment Effects on Adhesive Strength and Hole Quality

Učík, M. Influence of the Crystal Structure of AICrN on Tool Coating Properties and Wear in Dry Gear Hobbing

Z

Zarate, G. Scaling Methodology for Large Boring Bars with Tuned Mass Dampers; Towards Solid-State Recycling of Ti6Al4V: Defining a Sustainable Machining Process Window for Low-Oxidation Chips