Cover

ISBN print: 978-3-0364-0982-5
ISBN eBook: 978-3-0364-1982-4
DOI book: 10.4028/b-x6ozNn
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Advanced Metal Casting

Edited by
Mohamed El Mansori

Advanced Metal Casting

Special topic volume with invited peer-reviewed papers only

Edited by
Mohamed El Mansori

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

All rights reserved. No part of the contents of this publication may be reproduced, processed or transmitted in any form or by any means without the written permission of the publisher.

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Volume 1188 of
Materials Science Forum
ISSN print 0255-5476
ISSN web 1662-9752

Full text available online at https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

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Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp -1-0

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

Accepted:
2026-04-14

Online:
2026-04-20

Preface

The original version of this paper is available on https://www.scientific.net/MSF.1188.-1.pdf

This issue targets the following aspects.

Novel casting techniques, including, for instance, semisolid and thixocasting, vacuum-assisted, counter-gravity and single crystal casting methods. In addition, low-energy melting techniques and the use of recycled materials in novel or established casting methods are of interest.

Numerical (computational fluid dynamics (CFD) and solidification simulation) as well experimental approaches to enhance casting quality, focusing typically on solidification dynamics, microstructure control, reduction of shrinkage porosity, melt cleanliness. Of interest are alloy modification, optimized mold and gating design, cooling rates etc.

Process control, leveraging real-time monitoring and digital twins and integrating advanced sensors, coupled with data analytics and machine learning.

Sustainability assessment of casting technologies, environmental impact assessment, waste minimisation, circular economy initiatives, resource efficiency strategies, lean in foundries etc.

Editor

The original version of this paper is available on https://www.scientific.net/MSF.1188.-1.pdf

Mohamed El Mansori

Co-editors

The original version of this paper is available on https://www.scientific.net/MSF.1188.-1.pdf

Konstantinos Salonitis
George-Christopher Vosniakos

Arts et Métiers ParisTech

Cranfield University
National Technical University of Athens

Table of Contents

Preface

Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

Ahmed Ktari, Souhail Housni, Jérémie Bourgeois, Mohamed El Mansori

Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Chris Michaelis, Eugen Koslowski, Anne Giese, Christian Schwarz, Matthias Hackert-Oschätzchen

Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Sebastian Häner, Dorothea Czempas, Emad Scharifi, David Bailly, Junhe Lian

RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Martina Campanella, Antonio Piccininni, Angela Cusanno, Pasquale Guglielmi, Gianfranco Palumbo, Matteo Duraccio, Felice Lembo

Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process

Nagasivamuni Balasubramani, Mohamed El Mansori

Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Sri Harini Rajendran, Nagasivamuni Balasubramani, Mohamed El Mansori

Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

Ahmed Ktari, Davis Lu, Griffin Ainsworth, Mohamed El Mansori

Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

Evangelos Pischinas, Emmanouil Stathatos, George Christopher Vosniakos

Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Tharmalingam Sivarupan, Arul Mozhi Varman Jayaraman Palanivel, Konstantinos Georgarakis, John Forde, Ben Shaw, Konstantinos Salonitis, Mark Jolly

Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Alessandro Morri, Barbara Reggiani, Rosario Squatrito, Manel da Silva, Giulia Zaniboni, Lorenzo Donati

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 1-10

doi: 10.4028/p-Otm77C

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

Submitted:
2025-12-23

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-20

Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

KTARI Ahmed 1,a*, HOUSNI Souhail 1, b, BOURGEOIS Jérémie 1,c and EL MANSORI Mohamed 1, d

1 Laboratoire MSMP-EA7350, Arts et Métiers ParisTech, 2 cours des Arts et Métiers, 13617 Aix-en-Provence, France

a  ahmed.ktari@ensam.eu, b  Souhail.housni@ensam.eu, c  Jeremie.bourgeois@ensam.eu, d  mohamed.elmansori@ensam.eu

Keywords: Low-pressure casting, cast solidification, FE simulation, machine learning prediction

Abstract

This paper presents a development methodology for a metamodel-based machine learning approach for spatiotemporal prediction of temperature and solid fraction evolution of aluminum castings during cooling under low pressure sand casting (LPSC) conditions, for various pouring temperatures ( Tp ) ranging from 614∘C to 720∘C. High-fidelity finite element (FE) simulations were performed, based on a representative case study produced by the LPSC process to generate a comprehensive database, recording nodal temperatures across the casting symmetry plane at different cooling times, and for several Tp values within the studied domain. Three different machine learning (ML) algorithms were evaluated using comparative metrics ( R2, MAE and MSE). Among the evaluated algorithms, the artificial neural network (ANN)-based ML model was selected for its superior predictive accuracy and robustness. The accuracy of the selected ML-model was assessed by comparing predicted and FE-simulated temperature fields. The results indicate that the predicted temperature error within the cast symmetry plane remains below 1%. Furthermore, a graphical user interface (GUI) was developed to visualize the predicted casting temperature field for different Tp values not used during the learning stage, as well as the corresponding solid fraction, which is computed based on the solidification curve of the aluminum alloy AlSi7Mg0.3 given by the ProCAST® database. This methodology could provide a fast, robust, and scalable framework for extending predictive models to higher-dimensional cases and diverse LPSC casting process configurations.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

Despite its millennia-long history, the increasing complexity of components and stricter process performance requirements make integrating Industry 4.0 technologies essential for reducing defects, energy consumption, and material waste, thereby improving the resilience, efficiency, and sustainability of foundry operations [1]. To bridge the gap between traditional metal casting practices and the objectives of Casting 4.0, several advanced technologies have been integrated into casting processes to enhance their intelligence and connectivity. These technologies enable the emergence of digital twin (DT) frameworks, which are virtual representations of casting processes designed to support real-time monitoring, decision-making, and process optimization. A DT links the physical process to its virtual model through continuous data exchange, allowing the model to reflect actual conditions and predict system behavior. In this framework, machine learning (ML) based metamodels play a central role by providing fast, simplified approximations of complex and computationally intensive physics-based simulations, making real-time analysis and control possible. Metamodels improve process optimization and monitoring by identifying optimal parameter combinations that produce components with the desired properties. When embedded in a DT, they enable continuous tracking and adjustment of process conditions to meet predefined objectives, improving product quality, service life, and overall sustainability [2].

Developing a metamodel requires access to extensive datasets describing the metal casting process. These data can be obtained from experiments [3], numerical simulations [4,5], published
literature [6], or hybrid approaches that combine multiple sources [7,8]. When time and financial resources allow, experimental data are generally preferred; however, they remain susceptible to human error and uncontrolled process variations. Physics-based simulations provide a safe alternative to physical experiments by reducing measurement variability and isolating the effects of individual input parameters. Because they are inherently deterministic, numerical simulations yield identical outputs for identical inputs, eliminating external disturbances but limiting the ability to replicate realworld variability. Building sufficiently large datasets for training metamodels can be costly. Therefore, sampling techniques such as stratified sampling, probabilistic sampling, and Taguchibased approaches were widely used to provide a cost-effective way to explore the design space with a limited number of design points while maintaining good generalization performance [2,4].

The application of metamodels based ML is used in various metal casting processes such as gravity casting [2-4], precision casting [9], continuous casting [10] and low-pressure (LP) casting [7,8]. Deng et al. [3] analyzed aluminum-silicon gravity-cast components with different mold coating thicknesses using surface images and roughness measurements. Six convolutional neural network (CNN) metamodels were developed to predict surface roughness, with three achieving errors below 2μ m. Ktari and Elmansori [4] developed an artificial neural network (ANN)-based metamodel for predicting the ingate velocity of liquid metal in gravity sand casting for different filling system design parameters and to optimize the functional design parameters that keep the ingate velocity below critical values to avoid defects such as bubbles and oxide bi-films. Vosniakos et al. [9] developed ANN metamodels for precision investment casting to predict product quality based on different input parameters such as mold temperature, casting material, and the number and location of feeding points. Their performance was evaluated using training and generalization errors, and an expandable user interface was implemented for practical use. Diniz et al. [10] evaluated three deep neural network approaches to detect clogging in continuous steel casting using historical process data, with ANN as a baseline, and reported superior performance on imbalanced industrial data, with the spatiotemporal model achieving nearly 99% recall and 98% precision. Duan et al. 2023 [7] proposed a ML approach using a multimodal neural network including visual, textural and numerical features for designing an efficient gating system for large, thin-walled castings. Shahane et al. [7] conducted a sensitivity analysis to identify initial and wall temperatures that improve microstructure and yield strength of the cast in low-pressure die casting process by using an 3D finite-volume simulations, ANN predictions, and NSGA-II optimization algorithm.

The above studies shows that ML-based metamodels have been applied in the past five years to solve multiphysics and multiscale phenomena occurring in casting processes, particularly in LPC, which is widely used for producing high quality casting from light alloys such as aluminum and magnesium. These models are used to optimize process parameters such as the design of the gating system and temperature conditions. However, the application of ML metamodels has so far been limited to relatively small-scale trials and does not fully address real industrial cases. Further investigation is particularly needed for the low-pressure casting (LPC) process to better understand solidification behavior under applied pressure, which enables continuous feeding during solidification and helps reduce casting defects [11].

This paper aims to present a methodology for building a metamodel for the LPSC process that can predict the temperature and the resulting solid fraction throughout the casting at any time during its cooling within a sand mold under LPSC conditions.

Methodology

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

In this study, a high-fidelity finite element (FE) model was employed to perform simulations using ProCAST® software. Numerous simulations were conducted, each corresponding to a different pouring temperature ( Tp ) of the AlSi7Mg0.3 alloy ranging from 614∘C and 720∘C. Fig. 1 shows the initial casting and mold design as well as the associated FE model, which was validated through fully instrumented experiments. Further details of the FE model can be found in Ref. [12].

[picture]

Fig. 1 FE modeling of the cooling problem in LPSC process

Fig. 1. FE modeling of the cooling problem in LPSC process

The temperature values computed at each node from the simulation result was extracted and collected in a database using Python ®. The resulting dataset was postprocessed through normalization and subsequently divided into three subsets: learning, validation and test. Several ML algorithms including polynomial regression, XGBoost and artificial neural networks (ANN) were implemented and fine-tuned during the training phase to optimize their hyperparameters. Based on evaluation metrics such as mean squared error (MSE) and the coefficient of determination ( R2 ), the best algorithm was selected for the prediction of the casting temperature map (Fig. 2) and to calculate the corresponding solid fraction values.

[picture]

Fig. 2 Data processing workflow from FE simulation to ML results visualization

Fig. 2. Data processing workflow from FE simulation to ML results visualization

Although the metamodel uses a 2D symmetry plane, the training dataset was extracted from full ProCast ® 3D computations. This ensures that the input data inherently capture out-of-plane thermal gradients and their effects on solidification, making the training dataset consistent for both learning and subsequent 2D predictions. The 2D plane is therefore not a simple geometric reduction but a projection that retains the influence of 3D thermal behavior. This approach aims to assess the
feasibility of the neural network-based metamodel at a reduced computational cost while still considering the essential physics of the 3D process, allowing assessment of the validity of this method before its full scalability to 3D simulations.

Data Generation and Metamodel Building

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

As described below, thirteen FE-simulations were conducted using ProCast (R) software to simulate the temperature evolution in the case study casting geometry, made of Al -alloy (AlSi7Mg0.3) for different Tp ranging from 614∘C (liquidus +1∘C ) to 720∘C. The temperature evolution of the nodes located on the symmetry plane was extracted using ERFH5 files and imported into Phyton ® for data preprocessing. Fig 3 shows the structure of the extracted ERFH5 file. These data undergo a preprocessing step, transforming them into a tabular format of the type ( X,Y,Z,TP,Tstate_1 ,Tstate_2, …,Tstate_n  ), from which the neural network input vector is generated. Each row represents a spatial node, treated as an independent input during training, allowing the network to learn the relationship between node features and temperature evolution. Using a multi-output regression model, the network simultaneously predicts all temperatures associated with a given node, implicitly capturing temporal evolution from the input-output pairs between consecutive states ( i→i+1 ). While sequence-based models such as Long Short-Term Memory (LSTM) can explicitly model temporal correlations, this approach is sufficient, as the problem is formulated as supervised regression at each node and the dataset inherently reflects the temporal dynamics.

[picture]

Fig. 3 Hierarchical view of the ERFH5 file highlighting the dataset structure

Fig. 3. Hierarchical view of the ERFH5 file highlighting the dataset structure

The dataset was divided into three subsets for training, validation, and testing, containing approximately 75%,10%, and 15% of the simulation case, respectively (Table 1).

Table 1 Dataset partition for train, validation and test used for FE simulation

Dataset | Tp [°C]
Training | 614; 622; 630; 647; 655; 677; 685; 690; 705; 720
Validation | 710
Test | 635; 664

Three ML algorithms - polynomial regression, XGBoost, and ANN - were used in the learning process because they are well-suited for solving regression problems. In this work, it is important to note that, each node at a given spatial coordinate and simulation time has only a single input variable (the nodal pouring temperature, Tp , which is identical for the initial condition) and a single output (the nodal temperature). Therefore, for this simplified 2D geometry, the number of simulations is
considered sufficient, as the required number depends strongly on the quality of the dataset, the number of input parameters and the degree of nonlinearity in their variations over time and in their relationships [13]. In this work, the early stopping method was used during neural network training, with an epoch number of 1000 specified as an arbitrarily large value. The error on the validation dataset (the simulation result for Tp=710∘C ) was monitored during training. Both training and validation errors decrease during the initial learning phase, but when the neural network model begins to overfit, the validation error starts to increase. The network weights and biases were saved at the point of minimum validation set error, enabling the selection of an optimal neural network model that balances bias and variance [14]. In this model, the errors for the training, validation, and test datasets were found to be comparable, indicating that the model generalizes well and does not overfit the data. This shows that implementing a more sophisticated cross-validation strategy is unnecessary for the present study.

A grid-search procedure was conducted to fine-tune the hyperparameters of all three algorithms. This efficient exploration of predefined parameter spaces enabled the identification of optimal hyperparameter configurations that produced the best overall model performance (Table 2). The results show that among the tested algorithms, the ANN achieved the best metric values. The minimal MAE ( 1.5∘C ) and MSE ( 4.76∘C2 ) values, along with the maximum R2(0.99) value, indicate the highest predictive accuracy; therefore, it was selected for performing the thermal prediction of the casting during the cooling phase.

Table 2. Optimal hyperparameter settings and performance metrics for ML algorithms

Algorithm | Optimal hyperparameters | MAE [°C] | MSE [°C²] | R²
Polynomial | degree = 7 | 5.922 | 93.933 | 0.984
XGBoost | n_estimators = 100; max_depth = 5 learning rate = 0.01 | 7.192 | 82.322 | 0.982
ANN | hidden layers (265, 64, 256, 1024); activation = Relu; learning rate = 0.001; batch size = 32 | 1.558 | 4.766 | 0.999

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

ML metamodel validation

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

The ML model is validated both qualitatively, through spatial temperature maps at different cooling times, and quantitatively, by comparing the simulated and predicted temperature evolution across several casting zones during cooling. Fig. 4 illustrates the 2D temperature distribution on the symmetry plane of the case-study part during cooling under LPSC conditions at three different cooling times, for an initial filling temperature of 630∘C. The maps show the progressive heat dissipation throughout the part, with hotter regions persisting near thicker sections and faster cooling occurring in thinner areas. The comparison between FE simulation and ML prediction indicates excellent agreement, with the temperature error below 1% throughout the cooling phase. This small error highlights the high accuracy and effectiveness of the ML model in reproducing the thermal behavior of the casting.

[picture]

Fig. 4 FE simulation vs. ML prediction of the cast temperature and its deviation error ( Tp=630∘C )

Fig. 4. FE simulation vs. ML prediction of the cast temperature and its deviation error ( T p = 630 ∘ C )

Fig. 5 shows a comparison between the FE simulation and ML predictions of temperature evolution in the casting during the cooling phase across five zones, each with distinct thermal behavior under LPSC cooling conditions.

[picture]

Fig. 5 Temperature evolution in various casting zones: FE simulation vs. ML prediction ( Tp=630∘C )

Fig. 5. Temperature evolution in various casting zones: FE simulation vs. ML prediction ( T p = 630 ∘ C )

The temperature in the gate remains nearly constant due to its proximity to the riser tube, which serves as the fluid flow inlet. In contrast, the vent cools rapidly because it is the thinnest region, with a diameter of only 3 mm . The middle of the riser cools more slowly, as it is the thickest section and has the highest thermal modulus. Overall, the results show that the ML model accurately predicts 2D temperature map evolution across multiple spatially disconnected regions, capabilities that conventional FE simulations cannot achieve without performing 3D modeling. The curves illustrate how closely the ML model reproduces the thermal behavior captured by the FE simulation across different regions of the part, demonstrating its ability to provide rapid and reliable predictions suitable for real-time process monitoring and control required for the DT framework.

Temperature and solid fraction prediction

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

Once validated, the ANN-based model enables prediction of the casting temperature field during the cooling phase for Tp values not used in the learning phase. Using Python ® , the solid fraction at each node was computed from the solidification curve (Fig. 6) to compute the progression of solidification.

[picture]

Fig. 6 Fraction solid vs. temperature ( AlSi7Mg0.3, ProCAST® database)

Fig. 6. Fraction solid vs. temperature ( A l S i 7 M g 0.3 , ProCAST ® database)

A graphical user interface (GUI) was developed to visualize the predicted temperature and solid fraction enabling the examination of their evolution across the casting symmetry plane during cooling stage. Fig. 7 illustrates the 2D fields of predicted temperature and solid fraction during cooling at a pouring temperature ( Tp ) of 700∘C. The temperature evolution follows the same trend observed in previous FE-simulation results, and the solid fraction develops accordingly. Solidification begins in the thinnest regions, where the temperature drops most rapidly, and then progresses toward the two casting hot spots: one at the bottom near the gating system and the other at the top. By approximately 200 s , the casting is fully solidified except for the riser. At the end of cooling, only the region adjacent to the gating system remains liquid.

[picture]

Fig. 7 ML-predicted spatiotemporal temperature and fraction solid ( Tp=700∘C )

Fig. 7. ML-predicted spatiotemporal temperature and fraction solid ( T p = 700 ∘ C )

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

This study demonstrated that the developed ANN-based metamodel can accurately predict the spatiotemporal casting temperature field across a range of Tp values within the studied domain for a case produced using the LPSC process. A high-fidelity finite element (FE) model was first used to generate a database including nodal temperatures across the casting symmetry plane at different cooling times and for different Tp values. Three regression-based ML algorithms - polynomial regression, XGBoost, and ANN - were evaluated using early stopping and grid-search techniques. Among them, the ANN-based model demonstrated superior performance, achieving the lowest MAE ( 1.5∘C ) and MSE ( 4.76∘C2 ), as well as the highest R2 value ( 0.99 ), and was therefore selected for thermal prediction of the casting during the cooling phase. The metamodel was validated through
qualitative comparison with FE simulation results, demonstrating excellent predictive performance, with temperature deviation errors below 1% during the solidification process. A graphical user interface (GUI) was finally developed to visualize the predicted casting temperature field for different Tp values not used during the learning stage, as well as the corresponding solid fraction, which is computed with high accuracy based on the solidification curve of the aluminum alloy AlSi7Mg0.3.

The proposed metamodel reduces simulation costs and enables rapid, comprehensive 2D thermal analysis of the casting during the LPSC process, offering a potential alternative to time-consuming finite element (FE) simulations. Its demonstrated robustness in 2D spatiotemporal predictions can be readily extended to complex 3D geometries. Although this methodology can be applied in 3D, its use for geometries beyond the case study remains limited by the time required for process validation, which involves performing new FE simulations and fully retraining the metamodel.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.1.pdf

I. Jalilvand, J. Jang, B. Gopaluni, A.S. Milani, VR/MR systems integrated with heat transfer simulation for training of thermoforming: A multicriteria decision-making user study, Journal of Manufacturing Systems 72 (2024) 338-359. https://doi.org/10.1016/j.jmsy.2023.11.007.

A. Ktari, M. El Mansori, Digital twin of functional gating system in 3D printed molds for sand casting using a neural network, J Intell Manuf 33 (2022) 897-909. .

F. Deng, X. Rui, S. Lu, Z. Liu, H. Sun, W. Volk, Deep learning-based inline monitoring approach of mold coating thickness for Al-Si alloy permanent mold casting, Int J Adv Manuf Technol 130 (2024) 565-573. .

A. Ktari, M. El Mansori, Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes, Int J Adv Manuf Technol 114 (2021) 981-995. .

A. Ktari, M. Elmansori, Bridging FEM and Artificial Neural Network in gating system design for smart 3D sand casting, Procedia Manufacturing 51 (2020) 795-800. .

I. Pastor-López, B. Sanz, A. Tellaeche, G. Psaila, J.G. De La Puerta, P.G. Bringas, Quality assessment methodology based on machine learning with small datasets: Industrial castings defects, Neurocomputing 456 (2021) 622-628. .

Z. Duan, W. Chen, X. Pei, H. Hou, Y. Zhao, A multimodal data-driven design of low pressure die casting gating system for aluminum alloy cabin, Journal of Materials Research and Technology 27 (2023) 2723-2736. .

S. Shahane, N. Aluru, P. Ferreira, S.G. Kapoor, S.P. Vanka, Optimization of solidification in die casting using numerical simulations and machine learning, Journal of Manufacturing Processes 51 (2020) 130-141. .

G.-C. Vosniakos, V. Galiotou, D. Pantelis, P. Benardos, P. Pavlou, The scope of artificial neural network metamodels for precision casting process planning, Robotics and Computer-Integrated Manufacturing 25 (2009) 909-916. .

A.P.M. Diniz, P.M. Ciarelli, E.O.T. Salles, K.F. Coco, Use of deep neural networks for clogging detection in the Submerged Entry Nozzle of the continuous casting, Expert Systems with Applications 238 (2024) 121963. .

G.T. Kridli, P.A. Friedman, J.M. Boileau, Manufacturing processes for light alloys, in: Materials, Design and Manufacturing for Lightweight Vehicles, Elsevier, 2021: pp. 267-320. .

A. Ktari, M.E. Mansori, Towards remanufacturing of failed parts through rapid low-pressure sand-casting (LPSC) process, Int J Adv Manuf Technol 139 (2025) 2569-2588. .

D. Rajput, W.-J. Wang, C.-C. Chen, Evaluation of a decided sample size in machine learning applications, BMC Bioinformatics 24 (2023) 48. .

O.A. Montesinos López, A. Montesinos López, J. Crossa, Overfitting, Model Tuning, and Evaluation of Prediction Performance, in: Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer International Publishing, Cham, 2022: pp. 109-139. .

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The original version of this eBook is available on https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 11-20

doi: 10.4028/p-7YjXqc

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

Submitted:
2026-01-16

Revised:
2026-02-18

Accepted:
2026-02-22

Online:
2026-04-20

Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

Chris Michaelis 1,a*, Eugen Koslowski 2, b, Anne Giese 2,c, Christian Schwarz 3, d, Matthias Hackert-Oschätzchen 1,e

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

2 Gas- und Wärme-Institut Essen e.V., Abteilung Industrie- und Feuerungstechnik, Hafenstraße 101, 45356 Essen, Germany

3 HMT Höfer Metall Technik GmbH & Co. KG, Gewerbering 32, 06333 Hettstedt, Germany

a  chris.michaelis@ovgu.de, b  eugen.koslowski@gwi-essen.de, c  anne.giese@gwi-essen.de, d  c.schwarz@hmt-alu.de, e  matthias.hackert-oschaetzchen@ovgu.de

(*corresponding author)

Keywords: secondary aluminium, hydrogen substitution, molten aluminium, sustainability.

Abstract

The decarbonization of the aluminium industry requires a transition from fossil fuels to sustainable energy carriers. This study investigates the substitution of natural gas (NG) with hydrogen (H2) in reverberatory furnaces, analyzing the impact on melt quality, furnace integrity and exhaust emissions. Experimental investigations were conducted in a specifically designed furnace setup combining electrical heating with a burner system capable of operating with variable fuel blends ranging from pure natural gas to 100 vol. −% hydrogen. The results demonstrate that the hydrogen content in the aluminium melt depends on the atmospheric conditions - water vapour content in the atmosphere - during the melting and heating phases. In contrast, the holding phase exhibited a quasistatic behavior with negligible further hydrogen uptake, due to the isothermal process control. Numerical simulations (CFD) revealed that admixture rate exceeding 80 vol. −%H2 leads to significantly higher adiabatic flame temperatures. This results in the formation of local hotspots on the furnace walls and requiring the use of high-performance refractory linings. Furthermore, these thermal conditions correlated with a major increase in NOx emissions, despite a successful reduction in CO2 output. Considering the material quality, X-ray computed tomography (XCT) analysis indicated a marginal increase in volume porosity with higher hydrogen fractions. However, tensile testing confirmed that this porosity did not compromise the mechanical performance, as yield strength and ultimate tensile strength remained unaffected across all fuel mixtures. The study concludes that standard degassing procedures are sufficient to reduce the increased initial hydrogen load, showing that hydrogen combustion for secondary aluminium production is feasible.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

The global demand for aluminum is experiencing a major change, with a growing emphasis on recycling and circular economy principles. Secondary aluminium production is continuously increasing because it offers different economic and environmental advantages compared to primary extraction methods [1]. However, although recycling needs less energy than primary electrolysis, the melting process in secondary production is still energy-intensive and relies heavily on fossil fuels, mainly natural gas. To reach international climate targets, the decarbonization of these thermal processes is necessary. Therefore, green hydrogen ( GH2 ) is a key solution. The integration of GH2 into aluminium production not only links the mining and metals sector with the renewable energy sector but also contributes significantly to a more sustainable energy system. While in primary production GH2 can function as a reducing agent for alumina [2], its role in secondary aluminium facilities lies in the substitution of fossil fuels for thermal energy. Recent assessments have highlighted the economic and environmental feasibility of integrating green hydrogen into aluminium
recycling facilities, especially evaluating its potential as a replacement for natural gas in smelting, melting and refining furnaces [3].

The transition from natural gas to hydrogen is not only a binary choice but often involves a gradual blending process. The impact of this fuel switching on carbon footprint is significant. As shown in Fig. 1, there is a direct correlation between the hydrogen fraction in the fuel blend and the resulting environmental benefit. The graph shows that while initial blends provide small benefits, a complete transition to a 100vol.−% equivalent hydrogen content is required to eliminate direct combustion emissions of CO2 entirely [4].

This study focuses on the correlation between the hydrogen content in the fuel blend and the hydrogen uptake in the aluminium melt (solubility), assessing whether high-percentage hydrogen blends can be used without compromising the quality of the melt and the secondary aluminium produced.

[picture]

Fig. 1. CO2 emission reduction with equivalent H2 admixture (constant thermal output)

Fig. 1. C O 2 emission reduction with equivalent H 2 admixture (constant thermal output)

However, the implementation of hydrogen fuel blends introduces various challenges such as plant engineering, safety management, and metallurgical integrity. From an operational perspective, the significantly higher adiabatic flame temperature of hydrogen leads to increased thermal stress on the refractory lining, especially within unmodified furnace geometries. Nevertheless, this thermal load can be effectively mitigated through appropriate design adaptations. As the hydrogen fraction increases, the furnace atmosphere undergoes a substantial shift. Crucially, the substitution of natural gas with hydrogen implies a water vapor increase of approximately 84% under air-fired conditions and 50% under oxy-fuel conditions, which raises concerns about enhanced attack mechanisms on refractory materials [5]. While partial additions up to 20% may not drastically compromise kiln lifespan, the strong increase in emitted water vapor at higher blends requires close monitoring [5].

Furthermore, the clear combustion characteristics require major modifications to safety protocols and maintenance plans. Conventional maintenance strategies show limitations here. Furthermore, time-based or condition-based approaches are difficult because continuously monitoring material changes (e.g., hydrogen embrittlement) is challenging [4].

Hydrogen Absorption. The interaction between the furnace atmosphere and the molten metal depends on the presence of hydrogen sources and the thermodynamic laws of solubility. In gas-fired furnaces, the primary source of hydrogen is the decomposition of moisture and hydrocarbons derived from the fuel combustion process. The transition from natural gas (CH4) to hydrogen (H2) significantly alters the chemical composition of the combustion gases. While the combustion of
natural gas produces both carbon dioxide ( CO2 ) and water vapor ( H2O ), the combustion of pure hydrogen results exclusively in the formation of water vapor, as described by Eq. 1 [6,7]:

2H2+O2→2H2O.(1)

Therefore, replacing natural gas with hydrogen significantly increases the partial pressure of water vapor (H2O) in the furnace atmosphere. This leads to a wetter environment above the melt. The water vapor does not dissolve directly. Instead, it reacts at the liquid aluminium interface, reducing the vapor to atomic hydrogen and forming aluminium oxide, see Eq. 2 [6-8]:

3H2O+2Al↔2Al2O3+6H―.(2)

This reaction drives atomic hydrogen (H¯) into the melt, where it diffuses rapidly. The solubility of this atomic hydrogen in the liquid phase follows Sievert's Law. This law states that the equilibrium concentration of a diatomic gas dissolved in a metal is proportional to the square root of the partial pressure of the gas in equilibrium with the melt. The relationship is expressed as Eq. 3 [6-8]:

S=K(T)·pH2.(3)

Where:

S is the solubility of hydrogen (typically in cm3/100 g ).

pH2 is the partial pressure of hydrogen gas.

K(T) is the temperature-dependent equilibrium constant.

Since K(T) follows an Arrhenius-type relationship, hydrogen solubility increases exponentially with molten metal temperature. Therefore, the combination of a high-temperature flame (characteristic of hydrogen combustion) and a high partial pressure of water vapor creates the worst-case scenario for hydrogen uptake, significantly increasing the risk of porosity in the casting process. The increase in water vapor content directly drives hydrogen solubility in the molten aluminium. Since the solubility drops so sharply, this excess hydrogen becomes supersaturated and precipitates as molecular gas ( H2 ), forming interdendritic porosity in the casting structure. Therefore, precise temperature control is important; higher melt temperatures not only accelerate oxidation but also exponentially raise the saturation limit, compare Fig. 2 [6].

[picture]

Fig. 2. Temperature dependence of hydrogen absorption in A199.8 exposed to natural gas and hydrogen combustion atmospheres. Experiments were conducted in a 3-chamber furnace with closedloop melt circulation, using an AlSCAN probe for continuous hydrogen measurement in the melt. [9]
Syvertsen et al. [8] demonstrated a correlation between the water vapor fraction in the combustion gas and hydrogen uptake in the melt. Data derived from Fig. 3 indicates that doubling the H2O content in the exhaust gas results in an approximate 33% increase in dissolved hydrogen concentration.

Fig. 2. Temperature dependence of hydrogen absorption in A199.8 exposed to natural gas and hydrogen combustion atmospheres. Experiments were conducted in a 3-chamber furnace with closedloop melt circulation, using an AlSCAN probe for continuous hydrogen measurement in the melt. [9] Syvertsen et al. [8] demonstrated a correlation between the water vapor fraction in the combustion gas and hydrogen uptake in the melt. Data derived from Fig. 3 indicates that doubling the H 2 O content in the exhaust gas results in an approximate 33 % increase in dissolved hydrogen concentration.

While the general trend of increased hydrogen absorption is valid, the quantitative results require critical review. Given that the measurement methodology relies on Sievert's Law and is therefore highly sensitive to temperature, the reported values may overpredict the actual amount of dissolved hydrogen in the aluminium melt due to thermal variations.

[picture]

Fig. 3. Temporal evolution of furnace atmosphere and melt parameters during natural gas combustion over two experimental days. Experiments were conducted in a 20 -ton furnace equipped with a launder loop and metal pump. [8]

Fig. 3. Temporal evolution of furnace atmosphere and melt parameters during natural gas combustion over two experimental days. Experiments were conducted in a 20 -ton furnace equipped with a launder loop and metal pump. [8]

Tichy et al. [7] investigated the progression of hydrogen uptake across specific process stages. A critical observation seen in Fig. 4 is the significant rise in dissolved hydrogen during the transition from melting to heating. While the initial melting phase shows comparable low levels for both fuels, the heating phase drives a clear increase in hydrogen absorption. During the subsequent holding phase, this trend stabilizes; the hydrogen content remains elevated with no significant further uptake or natural degassing observed. However, the final process step provides the most vital conclusion for process safety: regardless of the higher initial gas load induced by H2 combustion, degassing treatment proves highly effective. As shown in the after degassing data, the hydrogen content for both Natural Gas (NG) and H2 is successfully reduced to nearly identical, low levels, demonstrating that standard refining procedures can fully compensate for the increased uptake.

[picture]

Fig. 4. Hydrogen levels at different process steps under NG and H2 combustion atmospheres. Direct melt measurements were performed in a rotary tilting furnace (melting) and a single-chamber furnace (holding). [7]

Fig. 4. Hydrogen levels at different process steps under NG and H2 combustion atmospheres. Direct melt measurements were performed in a rotary tilting furnace (melting) and a single-chamber furnace (holding). [7]

Experimental Methodology

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

Material. A remelted secondary alloy with 0.40−0.45wt% of Si and a Mg content of 0.45−0.5wt.% was used in this work. The typical chemical composition of the base alloy is presented in Table 1.

Table 1. Chemical composition of the aluminium alloy

Alloy | Si | Mg | Fe | Cu | Mn | Cr | Zn | Ti | Al
AA6060 | 0.3 – 0.60 | 0.35 – 0.6 | 0.10 – 0.3 | 0.10 | 0.1 | 0.05 | 0.15 | 0.10 | Rem.

Experimental Setup. The main part of the investigation is the experimental furnace shown in Fig. 5. It is equipped with extensive instrumentation for temperature monitoring, exhaust gas analysis and in-situ hydrogen measurement equipment. In this setup, the aluminium melt is maintained electrically at a constant target temperature of 720∘C, while the headspace above the melt is exposed to a defined combustion atmosphere. These atmospheres are generated using a self-made and modified smallscale burner. A gas mixing system utilizing mass flow controllers (MFCs) allows for the precise variation of the fuel gas composition, ranging from 100vol.−% natural gas (NG) to 100vol.−% hydrogen ( H2 ). To prevent the ingress of ambient air (false air), the furnace chamber is maintained under constant positive pressure. This configuration allows long-term experiments under strictly controlled boundary conditions. Consequently, changes in melt quality - specifically the hydrogen content - can be detected via continuous direct measurement (in-situ) to characterize the absorption behavior as a function of the prevailing atmosphere.

[picture]

Fig. 5. Experimental setup long-term test, based on [6].

Fig. 5. Experimental setup long-term test, based on [6].

In-Situ Hydrogen Measurement. For the quantitative determination of the dissolved hydrogen content in the liquid aluminium melt, the portable analysis system Hycal (IDECO GmbH / EMC) was applied. In contrast to the Density Index Method (Reduced Pressure Test), this system enables a direct, real-time measurement of the hydrogen concentration within the melt [10].

The sensor employed is based on the principle of an electrochemical hydrogen concentration cell. The core element of the sensor consists of a proton-conducting ceramic CaZrO3-In acting as a solidstate electrolyte. A solid-state reference material is enclosed within the sensor, providing a constant and known hydrogen concentration (reference electrode). The final value in ml/100g is calculated taking into account Eq. 3 Sievert's Law as well as alloy-specific correction factors stored within the device [10].
Numerical Setup and Boundary Conditions. The numerical investigation of the reverberatory furnace burner was performed using the commercial CFD code ANSYS Fluent 2023 R2. The simulations were conducted under steady-state conditions by solving the Reynolds-Averaged NavierStokes (RANS) equations. To ensure geometric fidelity and solution accuracy, the computational domain was discretized using a polyhedral mesh consisting of approximately 3.1 million cells.

For the physical modeling of the flow field, the Realizable k-epsilon turbulence model was selected because its improved performance in flows involving rotation and strong pressure gradients. The combustion process was simulated using the Probability Density Function (PDF) equilibrium model, assuming fast chemistry. Radiative heat transfer, which is dominant in this high-temperature environment, was calculated using the Discrete Ordinates (DO) radiation model. Furthermore, the simulation fully accounted for the heat transfer to the furnace walls to accurately predict the thermal boundary conditions and wall loads.
Porosity Analysis. Additionally, the specimens underwent tomographic inspection (GE Nanotom) with source settings of 80 kV and 160μ A. Post-reconstruction analysis of the volume data (voxel size: 20μ m ) was performed using Volume Graphics Studio MAX to determine the volume porosity. Tensile Test. Specimens were taken from casted round bars for tensile tests, which were performed in a Hegewald and Peschke model inspect 250 with 250.000 kg maximum load and a head displacement speed of 1 mm/min. The elongation was measured with an extensometer type MFN-A. The size of the test specimens corresponded to the DIN 50125-B 6×30 standard with a diameter of 6 mm .

Results

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Dissolved Hydrogen. In Fig. 6, the dissolved hydrogen levels in the melt rise significantly with increasing water vapor content in the fuel gas. Corresponding to [7], these results show that the atmospheric conditions during the melting and heating phases determine the final hydrogen concentration. Specifically, the water vapor content is the main factor. The in-situ measurement results from the reference natural gas process and the stepwise hydrogen admixture up to 100vol.−% demonstrate a quasi-static behavior of the hydrogen content (see Fig. 7), effectively ruling out significant hydrogen uptake during the holding periods after melting.

[picture]

Fig. 6. Impact of fuel gas composition on melt quality (regarding dissolved hydrogen content). Experiments were conducted on an electrically maintained melt ( 720∘C ) under positive pressure, exposed to variable burner atmospheres (NG to 100vol.−%H2 ) with continuous in-situ hydrogen monitoring.

Fig. 6. Impact of fuel gas composition on melt quality (regarding dissolved hydrogen content). Experiments were conducted on an electrically maintained melt ( 720 ∘ C ) under positive pressure, exposed to variable burner atmospheres (NG to 100 v o l . − % H 2 ) with continuous in-situ hydrogen monitoring.

Furthermore, the critical hydrogen uptake occurs mainly during the melting and heating phases as the aluminium scrap transitions from the solid phase through the semi-solid state to the fully liquid phase. In contrast, the subsequent holding period shows a quasi-static behavior with no significant change
in hydrogen content, compare Fig. 7. This observation differs from the findings of [8], a discrepancy likely due to the strict isothermal conditions maintained in this study (constant Temperature), whereas [8] may have operated under fluctuating temperatures. However, these results confirm the data reported by [7,11].

[picture]

Fig.7. Temporal evolution of dissolved hydrogen content and melt temperature during long-term holding experiments.

Fig.7. Temporal evolution of dissolved hydrogen content and melt temperature during long-term holding experiments.

Simulation. The combustion of 100vol.−% hydrogen leads to a significantly higher adiabatic flame temperature. This increases the thermal load on the furnace walls and creates a risk of localized overheating (hotspots). These hotspots can cause reduction of strength or structural damage, see Fig. 8. Consequently, a modification of the furnace lining is necessary for hydrogen application. This requires a specific selection of refractory materials specifically engineered to withstand the elevated thermal stresses caused by hydrogen firing.

[picture]

Fig. 8. Comparison of simulated wall temperatures for Natural Gas (NG) and Hydrogen ( H2 ) combustion at a constant thermal power of 2500 kW .

Fig. 8. Comparison of simulated wall temperatures for Natural Gas (NG) and Hydrogen ( H 2 ) combustion at a constant thermal power of 2500 kW .

Exhaust Emissions. An 80vol.−% hydrogen blend achieved a CO2 reduction of up to 50%. However, a disadvantage was observed due to nitrogen oxides. The NOx levels increased noticeably at admixture rates higher than 70−80vol.−%H2 (see Fig. 9). These results match the findings of Tichy et al. [7], identifying the increased flame temperatures as the primary driver for thermal NOx formation - an effect that can be suppressed through appropriate burner adjustments and process optimization.

[picture]

Fig. 9. Flue gas composition and NOx emissions as a function of hydrogen content in the fuel gas.

Fig. 9. Flue gas composition and N O x emissions as a function of hydrogen content in the fuel gas.

Volume Porosity. The X-ray computed tomography (XCT) analysis reveals a clear correlation between the hydrogen fraction in the combustion gas and the resulting volume porosity in the cast specimens, see Fig. 10. While the reference natural gas process produced the lowest porosity of approximately 0.25%, the porosity increased with rising hydrogen admixture, reaching a peak of roughly 0.38% at the 80vol.−% blend. This graph confirms that the elevated water vapor concentration in the furnace atmosphere leads to increased hydrogen uptake and subsequent pore formation during solidification. However, the mechanical testing data (Fig. 11) shows that this slight increase in porosity did not result in significant loss of the static mechanical properties, as yield strength and tensile strength remained largely consistent across all fuel mixtures. This indicates that the porosity induced by hydrogen combustion remains within a critical tolerance range, preserving the macroscopic structural integrity of the components.

[picture]

Fig. 10. Volume porosity of cast specimens determined via X-ray computed tomography (XCT).

Fig. 10. Volume porosity of cast specimens determined via X-ray computed tomography (XCT).

Mechanical Strength. The yield strength ( Rp0.2 ) remained largely unaffected by the variation in fuel gas mixtures, as shown in Fig. 11. There was no significant difference between natural gas, hydrogen blends, and pure hydrogen, with an average value of approximately 44 MPa observed across all casts. Similarly, the ultimate tensile strength ( Rm ) and elongation at fracture ( A ) appeared to show no major dependence on the fuel mixture. The highest tensile strength was recorded for the 20vol.−% hydrogen blend (115MPa), while the lowest value of 108 MPa was observed for both the 70vol.−% mixture and pure hydrogen, resulting in a maximum difference of only 7 MPa . Taking into account the ductility, 50 vol. −% hydrogen blend provided the highest elongation at fracture ( 15% ), whereas the 70vol.−% mixture resulted in the lowest value of 12%. Despite prolonged holding
periods under a combustion atmosphere, the mechanical properties remained consistent with the results from melting trials with direct flame contact and no holding time reported in [11]. This is notable even though the hydrogen content in the melt increased, as shown in Fig. 6.

[picture]

Fig. 11. Impact of fuel gas composition on aluminium quality (regarding tensile strength).

Fig. 11. Impact of fuel gas composition on aluminium quality (regarding tensile strength).

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

This present study examines hydrogen uptake throughout the melting and holding stages and assesses the effects of H2 and NG/H2 combustion on casting properties. The results demonstrate that the hydrogen content in the melt depends on the atmospheric conditions - specifically the water vapor concentration - during the melting and heating phases. In contrast, the subsequent holding phase showed no significant increase in dissolved hydrogen, indicating a quasi-static behavior once the target temperature is reached. Due to the process boundary conditions, numerical simulations identified a critical challenge for furnace integrity: Admixture rates over 80vol.−%H2 lead to significantly higher flame temperatures. This causes local hotspots on the furnace walls, which require high-performance refractory linings. Furthermore, these elevated adiabatic flame temperatures correlated with a measurable increase in NOx emissions. In terms of material quality, a slight increase in volume porosity was observed with higher hydrogen admixtures. However, this effect can be effectively reduced through standard degassing procedures. In conclusion, the study confirms that despite the changed combustion atmosphere, there is no negative influence on the tensile properties of the cast components. This validates that hydrogen firing is feasible in secondary aluminium furnaces.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

This study is funded by the Federal Ministry of Research, Technology and Space (BMFTR) with the funding code: 01LJ2106A-C and by the Federal Ministry for Economic Affairs and Energy (BMWE) with the funding code: 03EN2080A-D.

Supported by:

With funding from the:
Federal Ministry
for Economic Affairs

Federal Ministry of Research, Technology and Energy and Space

on the basis of a decision
by the German Bundestag

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.11.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 21-31

doi: 10.4028/p-7Yeu6A

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

Submitted:
2026-01-17

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-20

Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

The original version of this paper is available on https://www.scientific.net/MSF.1188.21.pdf

Sebastian Häner 1,a*, Dorothea Czempas 1, b, Emad Scharifi 1,c, David Bailly 1, d, Junhe Lian 1,e

1 Full Institute of Metal Forming (IBF), RWTH Aachen University, 52072 Aachen, Germany

a  sebastian.haener@ibf.rwth-aachen.de, b  dorothea.czempas@ibf.rwth-aachen.de, c  emad.scharifi@ibf.rwth-aachen.de, david.bailly@ibf.rwth-aachen.de, ejunhe.lian@ibf.rwth-aachen.de

(*corresponding author)

Keywords: Twin-Roll Casting, Aluminum, Microstructure, Mechanical Properties

Abstract

High-strength and recycling tolerable aluminum alloys make a significant contribution to weight reduction in modern lightweight construction. The advantages of aluminum alloys in terms of their low density combined with high strength can be significantly improved by the alloy composition. In contrast to the conventionally established process route, high-magnesium alloys can be produced using the twin-roll strip casting process. This allows additional process steps such as hot rolling and annealing to be drastically reduced in the economical production of near-net-shape strips, saving emissions and energy consumption. The strip casting process has already been applied to numerous aluminum alloys and enables their production, although the understanding of advanced alloys in this area is not yet fully understood because of its limited production in industry-related research due to the complexity of the process. However, transferring the high strength generated during rapid solidification into usable sheet performance remains challenging, especially at elevated Mg contents, where segregation, casting-related defects, and solute-affected recrystallization can limit ductility and processability. This study investigates the potential of a high-magnesium aluminum alloy produced by vertical strip casting. The properties of the alloy are correlated with the microstructural and mechanical characteristics and developed on the basis of an industrial reference alloy. For this purpose, an EN AW 5182 and an AlMg10 alloy were processed. The results show that highmagnesium alloys can be produced and processed using strip casting. In terms of the high-magnesium alloy, improved results can be achieved compared to the industrial EN AW 5182 alloy. Key findings: The strength of high-magnesium alloy is significantly above those of the EN-AW 5182 after strip casting enabling nearly 600 N/mm2 tensile strength, but the final properties are below this potentially possible characteristic after strip casting, presumably due to non-ideal recrystallization and an insufficiently adapted process route including rolling and annealing parameters.

Introduction

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High-strength aluminum alloys are essential for use in the automotive and aerospace industries. Their comparatively high strength and low density, combined with corrosion properties, enable their effective use in lightweight construction. Different alloy compositions, particularly those in the 6xxx and 7xxxx groups, account for the largest share of such applications. While 5xxx Al-Mg alloys generally show good corrosion resistance, high-Mg grades can become sensitized ( β-phase precipitation at grain boundaries) during thermal exposure, increasing susceptibility to intergranular corrosion and environmentally assisted cracking [1]. The development of high-alloyed aluminum that does not need to be age-hardened is proving to be insufficiently using conventional manufacturing methods [2]. Negative effects with significantly higher alloying contents, such as segregation and demixing, as well as the formation of intermetallic phases, represent the current limitations [3]. To suppress these effects, vertical twin roll strip casting can be used [4]. This process is characterized by its high cooling rates in conjunction with the production of strips close to their final dimensions [5]. Furthermore, strip casting allows an increased usage of scrap due to the forced dissolution of
accompanying elements as a result of rapid solidification and combines this with higher process speeds than is usual in conventional production [5,6]. The aluminum-magnesium alloys that can currently be produced using existing technology are limited to contents of 4.5 up to 5.5wt.−%. Higher alloy proportions lead to increased diffusion phenomena after casting into ingots and the formation of embrittling β-AlMg phases. The formation of the this phase is a critical aspect in terms of recrystallization behavior, which promotes nucleation and precipitations [7]. These have negative effects in the form of cracking and limited formability during further processing by hot or cold rolling. Aluminum alloys with magnesium contents of 10wt.−% cannot therefore be produced economically and profitably [8]. The possibility of hot rolling can be used to circumvent the increased strength during forming can be achieved, but in conjunction with elevated temperatures and a high rolling degree, this contributes to the formation of a coarse-grained structure and negatively affects the mechanical properties in terms of grain size [9]. Due to the high solubility of magnesium in aluminum, increased proportions of intermetallic phases can be precipitated during equilibrium cooling, as occurs in conventional production. With a solubility of less than 1wt.−% magnesium at room temperature, the formation of the embrittling β-AlMg phase is caused and the potential alloy cannot be handled [10]. With significantly increased cooling rates, as is the case in vertical strip casting, this effect can be almost completely avoided and supersaturated mixed crystals are formed [11]. The potential of vertical strip casting for these alloys in conjunction with exemplary thickness on an industrial scale has not been fully researched. The aim of this study is to present the material properties resulting from the strip casting process and further processing for high-strength aluminummagnesium alloys in comparison to a conventional EN AW 5182 alloy also produced via strip casting as reference. This work focuses on the connection between rapid cooling and the resulting development of the microstructure, while correlating this with the mechanical characteristics of the full process. For this purpose, both a 5182 alloy and an AlMg10 alloy were produced using vertical two-roll strip casting and processed to a final thickness of 1.0 mm by hot and cold rolling. The final recrystallization annealing serves to produce a strip material that can be further processed. In addition to the microstructure, the grain structures and potentially embrittling intermetallic phases are characterized and correlated with the mechanical properties. The results of the process route starting with strip casting are presented for both alloys in order to enable a comparison of the potential using this manufacturing route with industrial standards.

Materials and Methods

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Strip casting, rolling, recrystallization. A 5182-aluminum alloy and an AlMg 10 alloy were produced using the vertical two-roll strip casting machine at the Institute for Metal Forming (IBF) at RWTH Aachen University. For this purpose, 120 kg of each alloy was melted in an induction furnace and cast onto two counter-rotating casting rolls with a width of 150 mm using a low-pressure furnace and casting system. The process was controlled by means of force control between the two casting rolls to ensure constant heat transfer between the casting roll surface and the solidifying melt. After this, the solidified strip was cooled in still air and prepared for hot rolling. To do this, strip sections were heated to 400∘C for 10 minutes and then rolled from their initial thickness of 2.4 mm to 1.8 mm using a duo-roll setup, resulting in a 25% reduction. This step serves to close any pores and cavities that may have formed as a result of rapid solidification during the strip casting process. In addition, the embrittling casting structure can thus be eliminated. In the final cold rolling process, the sheet sections were rolled to a final thickness of 1.0 mm in two passes, annealed at 360∘C for 90 seconds, and cooled in still air. To achieve this state, as shown in Fig. 1, further processing is carried out with the corresponding process steps under the range of maximum solubility of magnesium in aluminum. Due to the lower degree of deformation during cold rolling in comparison of typical industrial processes, an increased annealing temperature of 360∘C was set for recrystallization. To avoid the formation of new intermediate phases, a short annealing time and rapid cooling were found to be advantageous.

[picture]

Fig. 1. Binary phase diagram in at.-% of Al-Mg with potential cooling route via strip casting [10]

Fig. 1. Binary phase diagram in at.-% of Al-Mg with potential cooling route via strip casting [10]

To determine possible deviations in the alloyed elements, the chemical composition of the cast strip material was determined. This was determined using spark emission spectroscopy, which was performed with a Spectro Funkenspectrometer MAXx. Average values from at least 5 measurements are given.

Samples for microscopic and scanning electron microscopic examination were taken from the sheets produced in the various process steps. The microstructure was recorded on RD-ND (rolling and normal direction) using a Keyence VHX7000. Barker's etching agent was used to contrast the mechanically grinded and polished samples. These were etched at a voltage of 20 V for 30 seconds. The scanning electron microscope images were taken on a ThermoFisher Helios 5 Hydra UX. For this purpose, conductively embedded samples on RD-ND were also mechanically grinded, polished and slightly electrochemical polished for improved separation and visualization of grain structures. Overview images were recorded at an acceleration voltage of 5 kV using secondary and backscattered electrons. The different element distributions (spectra and linescans) were recorded at an acceleration voltage of 15 kV . The ElitePlus EDX detector from EDAX was used.

To determine the mechanical characteristics, A50 tensile specimens were cut out of the strip casting samples and the processed sheets in the rolling direction using water jet cutting. These were tested quasi-statically on a Zwick100 from the manufacturer ZwickRoell with video extensometer in accordance with DIN EN ISO 6892-1 [12]. To ensure reproducibility, three samples in longitudinal direction were tested with an strain rate of 0.0011/s for each case.

Results and Discussions

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Chemical composition. The measured chemical composition of both aluminum alloys after vertical two-roll strip casting is shown in Table 1. While the EN AW 5182 alloy is within the typical industrial limits, the potential high-magnesium alloy contains a magnesium content of 10.17wt.−%. [13]. Other accompanying elements such as manganese or silicon are negligible because of their low quantities. Due to the minor deviations in the alloying elements, the differences in terms of microstructure and mechanical properties can primarily be reduced to the influence of the magnesium content.

Table 1. Chemical composition of produced and investigated aluminum alloys after strip casting in wt.%

| Mg | Si | Mn | Fe | Al
EN AW 5182 | 4.39 | 0.17 | 0.34 | 0.30 | 94.70
AlMg10 | 10.17 | 0.18 | 0.30 | 0.33 | 88.86

Mechanical and microscopic properties. The microstructure of the strip-cast alloy is shown in Fig. 2. Typical characteristics of the strip casting process can be seen here. In addition to the dendritic outer structure directed toward the center of the strip, globular portions are visible in the center of the strip. This microstructural subdivision arises from the solidification dynamics during strip casting. Dendritic structures nucleate and grow depending on the surface roughness of the casting rolls, extending into the molten metal. The rotation of the rolls subsequently directs these advancing dendrites toward the casting gap, influencing the final solidification morphology. There, the two strip shells that form are welded together due to the strip forming force. The residual melt still present in the center of the strip finally solidifies into globular crystals, as no further force acts on the solidifying strip.

[picture]

Fig. 2. Overview and detailed grain structure with boundaries a) near surface and b) in core with SEM (BSE) of EN AW 5182 alloy

Fig. 2. Overview and detailed grain structure with boundaries a) near surface and b) in core with SEM (BSE) of EN AW 5182 alloy

Due to slower cooling in the middle part of the strip compared to the outer band shells, more pronounced segregation occurs at the grain boundaries. The grain boundaries in the middle section of the strip exhibit magnesium concentrations of over 7wt.−% Magnesium, with simultaneous enrichment of silicon, iron, and manganese as it can be seen in Fig. 3.

[picture]

Fig. 3. EDS measurement with chemical composition of segregation for EN AW 5182

Fig. 3. EDS measurement with chemical composition of segregation for EN AW 5182

This is also evident in the distribution of alloying elements across the sheet thickness as shown in Fig. 4. The line scan starting at the sheet surface shows almost no fluctuations in magnesium concentration across the first area of directional solidification. After the transition to the globular structure in the middle of the strip, there are increased deviations, which can be attributed to the slowed cooling. Between the globularly solidified grains, the described precipitates are partially visible at the grain boundaries.

[picture]

Fig.4. Linescan with element distribution via normal direction of EN AW 5182

Fig.4. Linescan with element distribution via normal direction of EN AW 5182

[picture]

Fig. 5. Microscopic overview of microstructure with detailed images of surface and core area of EN AW 5182

Fig. 5. Microscopic overview of microstructure with detailed images of surface and core area of EN AW 5182

A similar behavior during solidification can also be observed in the high-magnesium alloy. The grain structure around the outer area of the strip has a homogeneous structure and grain size. The core
area of the strip is characterized by a globular grain shape and stronger segregation. Visually, similar differences between the center and outer regions can be detected as seen in Fig. 6.

[picture]

Fig. 6. Overview and detailed grain structure with boundaries a) near surface and b) in core with SEM (BSE) of AlMg10

Fig. 6. Overview and detailed grain structure with boundaries a) near surface and b) in core with SEM (BSE) of AlMg10

This also shows an enrichment of alloying elements at the grain boundaries as shown in Fig. 7. With a magnesium content of 18.2wt.%, which is significantly higher that that of the EN AW 5182 alloy and the amount of the alloy. Furthermore, increased oxygen content can be detected compared to the EN AW 5182 alloy. In addition to possible effects caused by slight electrochemical polishing, these can be attributed to oxide compounds in the magnesium source material and oxidation during the casting process.

[picture]

Fig.7. EDS measurement with chemical composition of segregation for AlMg 10

Fig.7. EDS measurement with chemical composition of segregation for AlMg 10

[picture]

Fig. 8. Microscopic overview of microstructure with detailed images of surface and core area of AlMg 10

Fig. 8. Microscopic overview of microstructure with detailed images of surface and core area of AlMg 10

This finding can be confirmed by characterizing the respective element distributions. The line scan, starting at the strip surface, show no significant fluctuations in alloy content as they progress toward the center of the strip. The differences in concentration are particularly noticeable in the highmagnesium alloy, where they are less than 1wt.%. Starting from the strip surface, a slight decrease in concentration of magnesium can be seen from around 800μ m. This is due to the different solidification processes involved in strip casting. Within the outer strip shell up to approximately 500μ m, the distribution is almost homogeneous. Due to the faster solidification, no diffusion processes can take place and homogeneously distributed mixed crystals are present in the material. In the middle range from 500μ m, an initial decrease in magnesium concentration to approximately 9wt.−% can be observed. Isolated fluctuations and the reduced concentration indicate a change in solidification behavior and slower cooling due to residual heat still present. Diffusion processes can take place on a small scale, leading to a slight increase in magnesium enrichment at the grain boundaries. This observation is consistent with the microstructures.

[picture]

Fig. 9. Linescan with element distribution via normal direction of AlMg 10

Fig. 9. Linescan with element distribution via normal direction of AlMg 10

The phenomenon of center segregation, which occurs in other strip casted alloys, especially those containing iron and silicon, cannot be observed. This phenomenon is based on the remaining melt, which briefly enriches itself with the alloying elements. Due to the maximum solubility of 18wt.%, even the residual melt in the center of the strip and a sufficiently high cooling rate suppress formation of segregation ovoer the strip thickness.

EN AW 5182

Fig. 10. Stress-strain curves of EN AW 5182 alloy and AlMg10 after strip casting

These results are confirmed by the mechanical property characterization shown in Fig. 10. A comparison of the two alloys reveals the expected stress-strain curves. With a yield strength of 250 MPa and a tensile strength of 425 MPa , the EN AW 5182 alloy exhibits the maximum achievable characteristic values due to the almost complete dissolution of the alloying elements. In comparison, significantly higher values are observed for both the yield strength and the tensile strength of the high-magnesium alloy. A yield strength of 450 MPa and a tensile strength of 590 MPa illustrate the effect of the significantly more pronounced solid solution strengthening. This mechanism is accompanied by a reduction in total elongation after failure, which, at 1.2% is significantly lower than that of the EN AW 5182 alloy. Furthermore, the decrease in total elongation after failure can be attributed to the presence of casting defects. Strip-cast materials typically exhibit solidification voids and micro-pores in the center of the strip, which also reduce the maximum elongation. This can be seen in the EN AW 5182 alloy, which has an elongation at break of 12% compared to standard processes. After cold rolling and recrystallization annealing, a fine-grained microstructure continues to develop in both alloys. As can be seen in Fig. 11 and 12, both alloys in general exhibit coarser structures at the grain boundaries.

[picture]

Fig. 11. Grain structure and boundaries with SEM of EN AW 5182

Fig. 11. Grain structure and boundaries with SEM of EN AW 5182

[picture]

Fig. 12. Grain structure and boundaries with SEM of AlMg10

Fig. 12. Grain structure and boundaries with SEM of AlMg10

A comparison of the two alloys reveals differences in terms of grain structure and the presence of segregation phenomena. The EN AW 5182 alloy exhibits coarser structures at the grain boundaries, which extend over a larger area. In contrast, the high-magnesium alloy exhibits significantly less pronounced features of this type. The structures are more finely distributed and range in size up to 10μ m. The finer grain structure, which can also be seen in Fig. 13, can be explained by the increased proportion of alloying elements. The recrystallization behavior, which depends on the chemical composition, the introduced deformation, and the thermal energy, is inhibited by the high proportion of magnesium in the mixed crystals. With an annealing time of 90 seconds, grain growth after recrystallization cannot proceed to the same extent as in the EN AW 5182 alloy.

[picture]

Fig. 13. Microscopic overview of microstructure of a) EN AW 5182 and b) AlMg10

Fig. 13. Microscopic overview of microstructure of a) EN AW 5182 and b) AlMg10

Furthermore, this grain structure a explains the stress-strain curves shown in Fig. 14. With a yield strength of 280 MPa and a tensile strength of 360 MPa , the processed EN AW 5182 alloy is within standard specifications. Compared to the strip-cast starting material, the high-magnesium alloy also has a yield strength of 300 MPa and a tensile strength of 410 MPa . On the one hand, these values are above those of the EN AW 5182 alloy, but on the other hand, they are significantly below the initial properties after strip casting. With a maximum elongation of 2.8%, the plasticity shows improved values. Various factors may be responsible for these properties: The high-magnesium alloy has a finer-grained structure with increased segregation at the grain boundaries. According to the HallPetch relationship, the ideal grain size for a medium-magnesium alloy is <50μ m [14]. This is not achieved when viewed under light microscope, which also leads to reduced strength. Furthermore, if the degree of deformation during rolling is not sufficiently high and casting defects are still present, this can lead to easier crack formation and propagation.

EN AW 5182

Fig. 14. Stress-strain curves of EN AW 5182 alloy and AlMg10 after final annealing

AlMg10

related cost reductions, strip casting is characterized by high cooling rates, enabling the production of strips with final thicknesses of around 1 mm using minimal rolling and annealing steps. With a magnesium content of 10wt.%, higher strengths can be achieved compared to conventionally producible alloys. A tensile strength of 410 MPa with a fracture strain of 2.8% demonstrates the microstructure development based on the EN AW 5182 alloy under identical processing. Despite an almost homogeneous chemical composition across the sheet thickness, differences in initial properties remain evident. The possible tensile strength of 590 MPa achieved directly after strip casting cannot be retained during further processing. Inhomogeneous grain sizes and varying solidification structures reduce the mechanical properties, and the recrystallized grain structure is not fully developed. Also, the hot rolling parameters combined with the reduced deformation during cold rolling inhibit recrystallization kinetics and influence the final microstructure decisively. This work highlights the potential of strip casting for producing such alloys and provides insights into the resulting microstructures. Further research should focus on optimizing subsequent process and annealing parameters to preserve the high strength after strip casting and achieve an ideal grain size and recrystallization state. Adjusting strip casting parameters and alloy design, including increased levels of accompanying elements, offers further potential to improve homogeneity, promote recrystallization, and enable recycling-tolerant alloys with higher magnesium contents.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.21.pdf

Lyndon, J.A.; Gupta, R.K.; Gibson, M.A. et al.: Electrochemical behaviour of the β-phase intermetallic (Mg2Al3) as a function of pH as relevant to corrosion of aluminium-magnesium alloys. In: Corrosion Science 70 (2013), S. 290-293. https://doi.org/10.1016/j.corsci.2012.12.022.

Jin, J.W.; Zhang, Z.J.; Li, R.H. et al.: Mechanical properties of three typical aluminum alloy strips prepared by twin-roll casting. In: Journal of Materials Research and Technology 28 (2024), S. 500-511. .

Haga, T.; Tkahashi, K.; Ikawaand, M. et al.: Twin roll casting of aluminum alloy strips. In: Journal of Materials Processing Technology 153-154 (2004), S. 42-47. .

Lv, Z.; Du, F.; An, Z. et al.: Centerline segregation mechanism of twin-roll cast A3003 strip. In: Journal of Alloys and Compounds 643 (2015), S. 270-274. .

Haga, T.; Komeda, K.; Mtsuoka, K. et al.: Roll Casting of Recycled AA5182. In: Applied Mechanics and Materials 121-126 (2011), S. 4667-4670. .

Haga, T.: High Speed Roll Caster for Aluminum Alloy. In: Metals 11 (2021), Heft 3, S. 520. .

Sauvage, X.; Enikeev, N.; Valiev, R. et al.: Atomic-scale analysis of the segregation and precipitation mechanisms in a severely deformed alloy. In: Acta Materialia 72 (2014), S. 125-136. .

Ściężor, W.; Mamala, A.; Kowal, R. et al.: Research on the Production Process of High-Strength Products from Aluminum-Magnesium Alloys for their Direct Processing into Sheets Via Cold Rolling. In: Archives of Metallurgy and Materials (2019), S. 1487-1493. .

Huskins, E.L.; Cao, B.; Ramesh, K.T.: Strengthening mechanisms in an alloy. In: Materials Science and Engineering: A 527 (2010), Heft 6, S. 1292-1298. .

Murray, J.L.: The Al-Mg (Aluminum-Magnesium) System. In: Bulletin of Alloy Phase Diagrams (1982), Vol. 3 No.1.

Haga, T.; Yamazaki, K.; Watari, H. et al.: Effect of Roll Load on Microstructure of Roll Cast Strip. In: Materials Science Forum 1097 (2023), S. 25-32. .

DIN-Normenausschuss Zugversuch: DIN EN 6892-1, Metallische Werkstoffe - Teil 1: Prüfverfahren bei Raumtemperatur; Deutsche Fassung EN ISO 6892-1:2019.

DIN-Normenausschuss Nichteisenmetalle: DIN EN 573-3, Aluminium und Aluminiumlegierungen -Chemische Zusammensetzung und Form von Halbzeug -Teil 3: Chemische Zusammensetzung und Erzeugnisformen; Deutsche Fassung EN 5733:2019+A2:2023.

PAO, P.; JONES, H.; CHENG, S. et al.: Fatigue crack propagation in ultrafine grained alloy. In: International Journal of Fatigue 27 (2005), 10-12, S. 1164-1169. .

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The original version of this eBook is available on https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 33-44

doi: 10.4028/p-qxB2nd

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

Submitted:
2026-01-18

Revised:
2026-02-24

Accepted:
2026-02-24

Online:
2026-04-20

RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

CAMPANELLA Martina 1,a*, PICCININNI Antonio 1, b, CUSANNO Angela 1,c, GUGLIELMI Pasquale 3,d, PALUMBO Gianfranco 1,e, DURACCIO Matteo 2,f and LEMBO Felice 2,g

1Politecnico di Bari, Italy ²Master Italy srl, Italy ³Università degli Studi della Basilicata, Italy

a* m.campanella12@phd.poliba.it, b antonio.piccininni@poliba.it, c angela.cusanno@poliba.it, d pasquale.guglielmi@unibas.it, e gianfranco.palumbo@poliba.it, f m.duraccio@masteritaly.com, gf.lembo@masteritaly.com

Keywords: HPDC, Air Entrainment, Response surface methodology, Optimization.

Abstract

High-Pressure Die Casting (HPDC) processes are often affected by complex thermo-fluiddynamic phenomena that lead to casting defects and premature die degradation. In this study, an approach based on the Response Surface Methodology (RSM) is proposed to improve the quality of the cast part (aluminum window brackets) and extend the dies' service life by introducing limited modification to the geometry of the die cavities.

A multi-physics numerical model was initially built up to reproduce the filling and thermal behavior of the process. Infrared thermography, used to validate the numerical results, confirmed the accuracy of the model, with an average temperature error of approximately 2%. The analysis revealed that the baseline configuration (i.e. the dies' geometry currently adopted in the industrial process) was characterized by non-negligible thermal imbalances (temperature gradients of about 50∘C and localized hot spots associated with high melt velocities), which reflected in the occurrence of flashes, metallization, and impression pad damage.

New die geometries with the aim of improving the thermal uniformity while reducing the temperature gradients were investigated by varying the geometrical properties of the gating system according to a DoE-based approach. The numerical results, collected in terms of total amount of porosity in the casting critical areas, were used to train accurate metamodels that, in turns, were adopted as the starting base for a multi-objective optimization. Results from the optimization allowed to identify different scenario, each characterized by a specific geometry of the gating system able to remarkably reduce the occurrence of porosity in the cast part (up to 42% less than the current condition). The results demonstrate that the proposed methodology enables effective and sustainable optimization of HPDC processes without costly trial-and-error approaches.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

High-pressure die casting (HPDC) is one of the most widely adopted manufacturing processes for the large-scale production of aluminum components with complex geometries and tight dimensional tolerances. Thanks to its high productivity and repeatability, HPDC is extensively used in the automotive and building sectors; however, the process is inherently affected by severe thermal and mechanical loads that strongly influence both casting quality and die service life [ 1,2 ].

During each production cycle, molten aluminum is injected into the die at high velocity and pressure, followed by rapid solidification and cyclic die opening and closing. These conditions generate non-uniform temperature fields within the die, leading to localized hot spots, steep thermal gradients, and differential thermal expansion between die components. Such phenomena are widely recognized as primary causes of casting defects-including porosity, flash formation, soldering, and incomplete filling-as well as premature die wear and surface degradation [3-5].

Among the various process-related issues, thermal imbalance in the gating and runner system plays a critical role. Localized overheating and high melting velocities in these regions can promote air entrapment, metallization, and accelerated degradation of critical die features. Traditional
countermeasures often rely on extensive die redesigns or advanced cooling solutions, which can be costly and difficult to implement in existing industrial production lines.

In this context, computer-aided engineering (CAE) solutions have emerged as powerful tools for understanding the complex thermo-fluid-dynamic behavior of the HPDC process and for supporting process optimization without relying on trial-and-error approaches. In particular, the integration of numerical simulations with Design of Experiments (DoE) and Response Surface Methodology (RSM) enables a systematic exploration of the design space, allowing the influence of multiple geometric and process parameters to be quantified efficiently [6, 7].

In the present work, a simulation-driven methodology is applied to an industrial HPDC process for the manufacturing of aluminum window brackets. Early life defects observed during production motivated the development of a high-fidelity numerical model of the process. Once created the FE model according to the real process and validated against experimental temperature measurements, a modified gating system based on a closed casting configuration was proposed. A DoE-based approach combined with response surface modeling is then employed to optimize the geometry and position of flow-regulating bottlenecks, with the objective of reducing porosity in critical regions of the casting. The study demonstrates how a combined CAE-RSM-optimization framework can improve process stability and die durability through low-impact, cost-effective design modifications[5,8].

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

Industrial case study and problem description. This study investigates the high-pressure die casting (HPDC) of aluminum window brackets, selected as an industrial case study due to their structural role within window frame assemblies. These components ensure the mechanical connection between frame profiles and are therefore critical for the dimensional stability and durability of the window system.

The brackets are produced on a cold-chamber HPDC press (OMS550), characterized by a closing force of 5,600kN and a maximum injection force of 520 kN , with an annual production target of approximately 500,000 units. The die is designed as a multi-cavity system and allows the simultaneous production of two different bracket geometries-referred to as male and female components-which are subsequently assembled together by means of a bolt, as shown in Fig. 1.

[picture]

Fig. 1 Two assembled Aluminum widow brackets

Fig. 1. Two assembled Aluminum widow brackets

The HPDC process follows a well-defined operational sequence with a total cycle time of 34.2 s . Filling and solidification are completed within approximately 10 s to ensure adequate cooling before part ejection. After die opening, the cast parts are removed by a robotic arm, while lubrication and air-blowing operations are performed to prepare the die for the subsequent cycle.

Despite the die being designed for long service life, visual inspections carried out after approximately 2,000 production cycles - corresponding to about 1% of the expected die lifetime revealed the presence of several critical defects affecting both the cast parts and the die. On the casting side, flashes were observed along the parting lines, as shown in Fig.2. These defects are caused by molten aluminum leakage under high pressure and require additional deburring operations, leading to increased production time and costs.

[picture]

Fig. 2 Final cast part: flashes are evident close to the brackets

Fig. 2. Final cast part: flashes are evident close to the brackets

In addition, significant degradation phenomena were identified on the die surfaces. As illustrated in Fig.3, lighter regions correspond to areas affected by aluminum adhesion (metallization), resulting from the combined mechanical and chemical interaction between molten aluminum and the die. Over repeated cycles, aluminum build-up alters the local thermal behavior, degrades surface finish, and accelerates die wear. The use of a solvent-based release agent (Chem-Trend #8802), although effective in reducing thermal shock, was found to be less effective in preventing residue accumulation.

[picture]

Fig. 3 Metallized regions on the die surface

Fig. 3. Metallized regions on the die surface

Finally, crushed impression pads were detected in localized regions of the die, particularly near small geometric features, as shown in Fig.4. These defects are associated with the combined action of cyclic thermal fatigue and high mechanical loads. Repeated thermal expansion and contraction promote microcrack initiation and progressive plastic deformation, a phenomenon further intensified during the HPDC intensification phase due to the extremely high internal pressures acting on the die.

[picture]

Fig. 4 Defect on the die surface: crushed impression pads on the surface of the die

Fig. 4. Defect on the die surface: crushed impression pads on the surface of the die

Numerical simulation of the HPDC process. To investigate the HPDC process in depth, identify the root causes of the observed defects and evaluate effective corrective actions, a numerical modeling strategy based on multi-physics simulations was adopted. The simulations were performed using the commercial finite element software CASTLE, specifically developed for High-Pressure Die Casting applications and capable of coupling fluid-dynamic and thermal analyses within a unified framework. Both Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) solvers were employed to capture the complex thermo-fluid behavior of the process throughout the entire casting cycle. Once calibrate the FE model, two main aspects could be addressed: (i) the fluid flow analysis provided a detailed visualization of the cavity filling, allowing a precise identification of the regions more exposed to shrinkage porosity problems, oxide formation, air entrapment, and misruns; (ii) the thermal analysis identified hot spots, non-uniform cooling, and steep thermal gradients, which have a marked influence on the thermal fatigue, metallization, and impression pad degradation. The CAD geometry of the die, gating system, casting, and cooling channels were imported in STEP format and discretized using a hybrid meshing strategy. Hexahedral elements were employed in the bulk regions of the die, while finer tetrahedral and prismatic elements were adopted in geometrically and physically critical areas, including thin sections, ingates, runners, impression pads, and cooling channels. Mesh refinement was defined to ensure an adequate resolution of local phenomena, with smaller element sizes applied in regions characterized by high thermal gradients and strong fluidstructure interactions. The final computational mesh consisted of approximately 12 million elements.

Material properties for both the casting alloy (EN46100, AlSi11Cu2Fe) and the die material (H13 with Aluminum Coating), were directly selected from the software internal material database. Some of the EN46100 properties accessible by the operator are listed in Table 1.

Table 1 List of properties of the EN46100, A1Si11Cu2Fe alloy (internal library of the CASTLE software)

Property | Value
Density (liquid phase) | 2460 [kg/m³]
Density (solid phase): | 2657 [kg/m³]
Latent heat of solidification: | 514,989 [J/kg]
Thermal conductivity (solid phase): | 143.2 [W/m·K]
Thermal conductivity (liquid phase) | 61.8 [W/m·K]
Liquidus temperature | 863 [°C]
Solidus temperature | 798 [°C]

Process boundary conditions, including the plunger velocity profile, injection pressure, cycle timing, and cooling strategy, were defined according to the actual industrial operating conditions. The
numerical simulations were carried out over multiple consecutive 15 HPDC cycles in order to capture the initial transient warm-up phase and reach thermal steady-state conditions. During the simulations, the heat transfer coefficient between the cooling water and the die surfaces was not imposed as a constant value but internally estimated as a function of coolant flow rate, channel geometry, and water temperature, ensuring a realistic representation of the heat exchange mechanisms.

Design of the new gating system. Once calibrate the FE model, the geometry of the die cavity was redesigned in order to overcome the limitation of the current solution. Two routes were investigated: (i) closing the gating system in the top part of the die while keeping the same cross section and (ii) introduce bottlenecks in the gating system to choke the flow.
The former solution is visually reported in Fig. 5 where the modification of the cavity is clearly evident.

[picture]

Fig. 5 Comparison between the current configuration, the proposed one (i) and configuration with bottlenecks (ii)

Fig. 5. Comparison between the current configuration, the proposed one (i) and configuration with bottlenecks (ii)

As for the latter aspect, the aim was to optimize the use of the bottlenecks by univocally identifying their geometry and position according to three main parameters. In particular: (i) the parameter L (Fig. 6a) refers to the width of the bottleneck, (ii) the parameters H (Fig. 6b) refers to its height and (iii) the parameter pos identifies the position (Fig. 6c reports simultaneously three possible positions).

[picture]

Fig. 6 Identification of the three main parameters univocally identifying the restriction of the gating system: a) width L,b ) height H and c ) location pos

Fig. 6. Identification of the three main parameters univocally identifying the restriction of the gating system: a) width L , b ) height H and c ) location pos

Optimization of the bottleneck started from the definition of an ordered plan of numerical simulation, arranged according to a Central Composite Design while varying the three introduced main parameters within the interval ranges reported in Table 2 (possible values of the parameter pos are expressed in a normalized form, where 0 refers to a position of the bottleneck close to the brackets).

Table 2 Variation ranges of the identified input parameters

| L [mm] | H [mm] | Pos [-]
Lower Bound | 1 | 3 | 1
Upper Bound | 9.15 | 12 | 0

The distribution of the 15 designs within the design space is graphically reported in Fig. 7a. For each simulation, the casting quality was evaluated by focusing on the most critical geometrical regions, identified based on both industrial feedback and preliminary numerical analyses. Therefore,
two descriptive response variables were defined: (i) the total Air Porosity of the first row of brackets (grouped by the violet box in Fig. 7b), labelled as AP_row1 and (ii) the total Air Porosity of the middle branch of brackets (grouped by the green box in Fig. 7b), labelled as AP_mc. The Fig. 7b corresponds to the alphal field (air volume fraction), re-parameterized with respect to the maximum pressure available during the third phase of the injection cycle, and filtered using a 5% air threshold. This representation was adopted to allow a more objective and consistent comparison among different simulation configurations, independently of local pressure fluctuations. The response variables AP_row1 and AP_mc represent the total entrapped air volume integrated over the selected regions of interest (ROIs). Entrapped air was quantified from the CFD scalar field α1, corresponding to the local air volume fraction. After applying a 5% air threshold to ensure consistent comparison among simulations, the total air porosity in a region Ω was computed as:

AP=∫Ωα1dV

The resulting value (m3) represents the total entrapped air within the ROI, providing a continuous and physically consistent measure rather than a binary classification.

[picture]

Fig. 7 RS-based design of the bottlenecks: a) graphical representation of the CCD plan, b) identification of the output response

Fig. 7. RS-based design of the bottlenecks: a) graphical representation of the CCD plan, b) identification of the output response

Data collected from the 15 simulations in terms of the mentioned parameters were subsequently fitted by an interpolating anisotropic Kriging algorithm. The so-trained metamodels (one for each response variable) were subsequently used as the base for a virtual optimization managed by a multiobjective genetic algorithm (MOGA-II). The MOGA, using its main genetic operators, evolved through 1000 virtual generations with the aim of determining the best values of the input parameters to reduce the porosity in the most critical areas (i.e., minimize both AP_row1 and AP_mc).

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

Model validation and root cause analysis. The results from the thermal model were analyzed in terms of temperature distribution along four linear paths, two per die, as shown in Fig. 8. The average temperature calculated for each single path revealed not only a pronounced non-uniform temperature distribution over the die surfaces (with gradient along the vertical direction of around 50∘C ) but also between the two dies.

[picture]

Fig. 8 Surface temperature distribution (before spraying the lubricant) over the movable (left) and fixed (right) die once reached the steady-state condition. Lines A-D indicate the paths along which the temperatures were monitored.

Fig. 8. Surface temperature distribution (before spraying the lubricant) over the movable (left) and fixed (right) die once reached the steady-state condition. Lines A-D indicate the paths along which the temperatures were monitored.

The numerical results were compared with the experimental acquisition (FLIR T6xx infrared thermography): Fig. 9 shows the distribution of the temperature at the 15th  cycle immediately before the lubrication and air-blowing phases, along with the average values calculated along the same path investigated in the numerical simulation.

[picture]

Fig. 9 Infrared thermography of the movable (left) and fixed (right) dies, showing surface temperature distribution at steady-state condition (time instant before the spraying step). Lines A′ D' indicate the paths along which the temperatures were monitored.

Fig. 9. Infrared thermography of the movable (left) and fixed (right) dies, showing surface temperature distribution at steady-state condition (time instant before the spraying step). Lines A ′ D' indicate the paths along which the temperatures were monitored.

The comparison between numerical predictions and experimental measurements confirmed the accuracy of the FE model, with an average temperature discrepancy of approximately 6∘C (average error of about 2% ).

The non-negligible thermal gradient, extremely visible from both the simulation and the experimental acquisition, led to differential thermal expansion, which was directly associated with dimensional instability, flash formation, and premature die degradation. Moreover, localized hot zones were identified in proximity to the central runner and injection area, with peak temperatures reaching up to approximately 350∘C. CFD results shown in Fig. 10 further showed that these regions are characterized by very high melt velocities, locally approaching 80 m/s, providing a clear explanation for the metallization phenomena observed on the die surfaces.

[picture]

Fig. 10 Simulation of die cavity filling with a particular focus on the velocity distribution in the central runner (the detailed view reports the distribution along the Plane_A cross section)

Fig. 10. Simulation of die cavity filling with a particular focus on the velocity distribution in the central runner (the detailed view reports the distribution along the Plane_A cross section)

Gating system redesign strategy. The thermal response of the die after the closure of the gating system in the upper part was analyzed by comparing the surface temperature distributions obtained before the spraying step at steady-state conditions. As shown by the contour plots in Fig. 11, the modification of the die cavities had a marked effect on the thermal behavior of the system, leading to a significantly more uniform temperature distribution compared to the baseline configuration.

[picture]

Fig. 11 Closing-casting configuration: Surface temperature distribution (before spraying the lubricant) over the movable (left) and fixed (right) die once reached the steady-state condition (after the 15th cycle). The temperatures were monitored along lines A"−B" and C"−D".

Fig. 11. Closing-casting configuration: Surface temperature distribution (before spraying the lubricant) over the movable (left) and fixed (right) die once reached the steady-state condition (after the 15th cycle). The temperatures were monitored along lines A " − B " and C " − D " .

In particular, the temperature fields on both the movable and fixed dies appear considerably homogenized, with a clear reduction of localized hot regions previously observed in proximity to the runner and injection area. This confirms that the closed gating configuration effectively redistributes the thermal load over the die surfaces.

In line with the analysis presented in Fig.8, temperature profiles were evaluated along the same four horizontal paths ( A′′ and B′′ on the movable die, C′′ and D′′ on the fixed die). The temperature distributions show a substantial reduction of the vertical thermal gradient. Quantitatively, the temperature difference between the upper and lower regions of the movable die was reduced to 12.6 ∘C, while a gradient of approximately 22∘C was measured on the fixed die.

These results demonstrate that the closure of the gating system provides a strong thermal stabilization of the die, reducing temperature gradients by more than 50% with respect to the original configuration. Such an improvement is expected to significantly mitigate differential thermal expansion effects, thereby contributing to enhanced dimensional stability and reduced susceptibility to thermally driven die degradation mechanisms. Nevertheless, the remarkable reduction of the thermal imbalance was not accompanied by an equally effective reduction of the bracket porosity, as
shown in Fig. 12: therefore, including the bottlenecks and optimizing their position/geometry was considered necessary to satisfy both the requirements (reduction of porosity and improvement of the material flow).

[picture]

Fig. 12 Total porosity distribution: a) current configuration; b) proposed geometry with the closing gating system

Fig. 12. Total porosity distribution: a) current configuration; b) proposed geometry with the closing gating system

Response surface. The trained metamodels are visually represented by the Response Surfaces in Fig. 13, from which interesting considerations could be drawn. In particular, regardless of the specific critical area, it is clear that if the height of the bottleneck is reduced close to the lower bound, its position has almost a negligible effect on the occurrence of the total porosity (see Fig. 13b and Fig. 13d). Moreover, all the reported response surfaces suggest that keeping the height of the bottleneck (H) in a range between 6 and 8 mm is extremely detrimental for the occurrence of porosity (red zones of the response surface). Eventually, it could be also said that the parameter H seems to have a more pronounced effect than the parameter L on the total porosity of both the critical areas.

[picture]

Fig. 13 Representation of the trained metamodels on the output variables: (a) and (b) AP_mc; (c) and (d) AP_row1

Fig. 13. Representation of the trained metamodels on the output variables: (a) and (b) AP_mc; (c) and (d) AP_row1

Optimization results. The main advantage of RS-based optimization is its virtuality: the metamodel is a reliable base from which the MOGA virtually extrapolates designs while trying to satisfy the objective functions. Therefore, the computational cost of the optimization is extremely limited and the number of successive generations can be largely increased: in the present work, 1000 generations were created and the algorithm evolved through 15000 designs. When dealing with a multi-response optimization, it is more likely to face a front of optimal solutions (rather than a unique optimum as it happens with the single objective optimization). Therefore, the Pareto front, defined as the group of non-dominated designs, could be easily extracted from the optimization results and analyzed using the parallel coordinate charts: in this type of charts, all the variables (input and response) are represented by equally spaced parallel vertical lines, whereas each design is represented as a polyline connecting vertices on the vertical axes. By filtering the results, i.e. by specifying a subrange of the variable values, two distinct optimal scenarios could be spotted.

[picture]

Fig. 14 Filtered parallel coordinate charts to identify optimal scenarios: (a) minimization of the AP_mc response and (b) minimization of the AP_row1 response

Fig. 14. Filtered parallel coordinate charts to identify optimal scenarios: (a) minimization of the AP_mc response and (b) minimization of the AP_row1 response

In fact, as shown in Fig. 14a, if privileging the minimization of the porosity in the middle branch (AP_mc), the bottleneck has to be located as close as possible to the brackets and its geometry characterized by a pronounced restriction in the width ( L close to 3 mm ) and moderate restriction along the height (H close to 1.5/1.6 mm, slightly higher than its lower bound). On the other hand (see Fig. 14b), the minimization of the porosity of the first row requires again an evident restriction along
the width, but in combination with a less sever reduction of the cross section along the height (H close to 2.2 mm ) while locating the bottleneck slightly farther from the brackets (pos close to 0.5 mm ).

These results clearly indicate that the optimal design parameters depend on which critical region is prioritized, confirming the presence of a trade-off between the two objectives. Consequently, more than one optimal condition can be identified along the Pareto front, rather than a single global optimum. The two best designs (each minimizing one objective function) were therefore compared with the reference condition (represented by the closed gating system in absence of bottlenecks) in terms of overall porosity, as listed in Table 3.

The optimization was deliberately formulated as a multi-objective problem, maintaining AP_mc and AP_row1 as independent response variables. This choice allows the trade-off between the two critical regions to be explicitly captured and provides a flexible decision-making framework, avoiding the information loss that would result from collapsing the problem into a single averaged objective function.

Table 3 Optimization results: optimal scenarios vs the reference condition

H [mm] | L [mm] | pos [-] | AP_mc | AP_row1
Reference | 9.15 | 12 | / | 4.25e-7 | 2.9e-7
Opt#1 | 2.7 | 3 | 0.5 | 2.46e-7 | 1.71e-7
Opt#2 | 1.5 | 3 | 0 | 2.37e-7 | 1.78e-7

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

In this work, a comprehensive numerical and optimization-based methodology was developed and applied to improve the quality and durability of High-Pressure Die Casting (HPDC) components, with specific reference to aluminum window brackets produced in an industrial environment. A validated multi-physics numerical model of the HPDC process was shown to be an effective and tool for identifying the root causes of premature die degradation and casting defects. The combined CFDthermal analysis revealed a pronounced thermal imbalance in the currently-adopted configuration, characterized by vertical temperature gradients of approximately 50∘C between the upper and lower regions of the dies. These gradients reflected directly in a differential thermal expansion, flash formation, impression pad crushing, and metallization phenomena, further exacerbated by localized hot spots (up to ~350∘C ) and high melt velocities (approaching 80 m/s ). A cost-effective and lowimpact Closed Casting strategy was numerically investigated as a first corrective action. This modification significantly improved the thermal behavior of the system, leading to a marked homogenization of the temperature field. In particular, the vertical temperature gradient was reduced to 12.6∘C in the movable die and approximately 22∘C in the fixed die, corresponding to an overall reduction exceeding 50%. Such thermal stabilization is expected to substantially mitigate thermally driven die wear mechanisms and improve dimensional stability. Nevertheless, the occurrence of porosity sensibly worsened. Therefore, the implementation of bottlenecks was optimized (in terms of positioning and geometry) by means of an RSM-based optimization. Porosity extracted from a set of simulations (arranged according to a CCD plan) were fitted by anisotropic Kriging metamodels showing the more pronounced effect of the restriction along the height rather than its length on the reduction of porosity. The multi-response optimization, driven by a multi-objective genetic algorithm, enabled the identification of two distinct possible optimal scenarios were identified, each targeting porosity reduction in the most critical regions of the casting. Compared to the reference configuration, the optimized solutions achieved porosity reductions of up to 42% in the middle branch and approximately 39−44% in the first row of brackets. Overall, the proposed methodology demonstrates that combining high-fidelity numerical modeling with DoE-driven, response surface-based optimization allows effective process improvement without costly hardware modifications or trial-and-error approaches. The approach is robust, scalable, and well suited for thin-walled HPDC components, offering a valuable tool for improving casting quality while extending die service life in industrial applications.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.33.pdf

Handbook of Metal Forming, edited byKurt Lange, Professor, University of Stuttgart (McGraw-Hill, 1985, $85.00; ISBN 0-07-036285-8). Reviewed by Serope Kalpakjian, Professor of Mechanical Engineering, Illinois Institute of Technology.

Dou K, Lordan E, Zhang YJ, et al (2020) A complete computer aided engineering (CAE) modelling and optimization of high pressure die casting (HPDC) process. J Manuf Process 60:435-446.

Podprocká R, Malik J, Bolibruchová D (2015) Defects in High Pressure Die Casting Process. Manufacturing Technology 15:1-1.

Campbell J (2015) Complete Casting Handbook: Metal Casting Processes, Metallurgy, Techniques and Design: Second Edition. 1-1028

Rocha SB, Zhiltsova T, Neto V, Oliveira MSA (2022) Optimization to Assist Design and Analysis of Temperature Control Strategies for Injection Molding-A Review. Materials 15:.

Chelladurai SJS, K. M, Ray AP, et al (2021) Optimization of process parameters using response surface methodology: A review. Mater Today Proc 37:1301-1304.

Han XW, Chen DP (2012) Numerical simulation life prediction of alu-alloy diecasting dies. Adv Mat Res 472-475:2296-2303.

Kotas P, Hattel J (2008) Optimization of die filling in high pressure die cast part using MAGMAsoft. 366-368

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/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 45-55

doi: 10.4028/p-olj0MC

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

Submitted:
2026-01-19

Revised:
2026-02-19

Accepted:
2026-02-22

Online:
2026-04-20

Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process

The original version of this paper is available on https://www.scientific.net/MSF.1188.45.pdf

Nagasivamuni Balasubramani 1,a* and Mohamed El Mansori 1, b

¹Materials, Surfaces, Mechanics and Processing Laboratoire, MSMP-EA7350, Arts et Métiers

ParisTech, 2 cours des Arts et Métiers, 13617 Aix-en-Provence, France

an  nagasivamuni.balasubramani@ensam.eu, b  mohamed.elmansori@ensam.eu,

Keywords: Numerical simulation, high-pressure die casting, solidification, filling velocity

Abstract

The effect of time-dependent interfacial heat transfer coefficient ( h ) within the shot sleeve and die cavity of high pressure die casting process (HPDC) has been simulated to systematically study the solidification occurring during filling. Two different h-profiles have been considered with peak values of 7 kW/m2 K and 12 kW/m2 K for the shot sleeve, and 18 kW/m2 K and 24 kW/m2 K for the runner, gate, and die cavity based on the values reported in the literature. In addition, two types of gate designs were considered for plate type castings to analyze their solidification behaviour and filling velocity. For the A356 alloy investigated, solidification typically occurs along the bottom wall of the shot sleeve, from the mid-region toward the mould-side region along the direction of pouring. At the end of filling, the solid fraction ( fs ) inside the shot sleeve increases from 10 to 18% with increasing peak value for h-profiles. Similarly, the progress of solidification around the gate regions increases rapidly above 0.4fs and reduces the channel width at the gate entry for h profile with higher peak values. Despite the lack of consensus on the selection of h value (peak value and range), this study highlights the influence h profiles and gating design on solidification during filling and discusses its implications on HPDC parts with large filling lengths.

Introduction

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Aluminium alloy castings for automotive applications are predominantly produced via High pressure die casting (HPDC) process due to their versatility to produce complex shapes, thin sections, long flow lengths with better dimensional accuracy, production rate, and surface finish [1−3]. Despite these advantages, understanding the role of process variables in achieving product consistency and reducing defective parts remains highly challenging [1]. In a cold chamber HPDC, the important stages are filling the shot sleeve, high velocity injection of molten metal into the die cavity ( 2−4 m/s ), and solidification under high pressure [3]. In general, defects formed in the HPDC process can be categorized into surface defects, subsurface defects, or internal and geometrical defects [1]. Filling, gas-related defects (air entrapment and H2 content), and oxides are the most common defects that significantly affect the as-cast tensile strength and elongation. Both morphology, growth, and size distribution of bubbles play an important role in determining the as-cast tensile properties of the HPDC alloys [4]. Higher elongation values in the as-cast condition >9% are achievable in HPDC when the size of the largest pore is controlled below 0.2 mm [2].

The second most influencing factor for porosity or related defects in HPDC is the formation of externally solidified crystals (ESCs) in the shot sleeve prior to the injection stage [5]. Nucleation of large, dendritic α−Al grains occurring inside the shot sleeve at relatively slower cooling rate could entrain into the die cavity causing shrinkage porosities due to restricted feeding or sometimes associated with defects (oxides). Despite constant operating parameters, occurrence and morphology of macro porosities varied significantly between trials [6], and it has been highlighted that the increase in feeding time by 0.25 s or more could affect the liquid volume by ±3.5%. These small variations arise from several uncertainty factors such as cold flakes in gate regions, spray variation, local metal/mould temperature and other inclusions. The temperature gradient during filling around the gate regions is crucial in feeding and the development of defects like flow lines, cold laps, gas, and shrinkage porosities. [3]. Higher injection pressure near the corners and edges prevents the extensive
formation of a chilled layer, resulting in a better flow [7]. Restricted filling due to significant solid formation around the gate or the presence of unfragmented, large dendrites could affect the last stage of feeding during the intensification stage leading to porosities. Although the role of intensification pressure on reducing porosity ( 26% for 0 MPa to <0.67% at 61 MPa ) has been well acknowledged, the degree of macrosegregation, porosity/cracks observed near the gate to grip region of the sample is noticeable when solid fraction exceeds 0.5-0.6% [4].

Numerical simulations using finite element methods (FE) have provided a better visualization of the complex mould filling process and facilitated defect prediction during HPDC solidification. Research works by Dou et al. [8,9] have shown the effect of shot profile, solidification inside the shot sleeve, and the evolution of solid fraction during fast and slow shot speeds using FE simulations. Although the simulations are incapable of predicting the entrainment effect of pre-solidified phases (ESCs), it is highlighted that predicting the solidification occurring along the casting, gate, and runner regions during fast-filling stages is crucial to avoid defects. One of the most important thermal boundary conditions that can affect the accuracy of solidification is the interfacial heat transfer coefficient ( h ) between the liquid metal and the mould [10]. Inverse methodology is the most commonly used method for determining h values using temperature measurements from inside the dies. [11]. During the HPDC process, h values are estimated during the filling of the shot sleeve, fast filling stage (in the runner and gate regions), and intensification stage during solidification [11]. For different levels of intensification pressure, fast-shot speed, mould, and pouring temperature investigated by Koru and Serce [12], peak values of h value range from 11.8 to 15.7 kW/m2 K. Increasing intensification pressure from 80 to 60 MPa and velocity from 1.5-2.5 m/s increases the h value by 150−200 W/m2 K. Similarly, increasing pouring temperature and vacuum level increases h value, while increasing mould temperature reduces h. However, Yu et al. [13] have shown that the peak values of h at the entry point near the gate vary from 15 kW/m2 K to as high as 64 kW/m2 K and 16 to 26 kW/m2 K near the overfill region. By correlating the measured ESC size from the gate region and various locations within the sample, Sharifi et al. [14] revealed that h values could be in the range of 13 to 14 kW/m2 K. Highest peak values for h have been reported by Dargusch et al. [10] which ranges between 80−140 kW/m2k for the intensification pressures of 32MPa,67MPa, and 90 MPa . Moreover, it has been highlighted that the heat transfer is predominantly influenced by gate velocity and die temperature rather than the intensification pressure [10]. This is due to the mechanical constraints imposed by the progressive solidification restricting the effect of applied pressure. Similar values have been reported for h during the initial stages of die filling that ranges from 104 to 105W/m2 K [5]. Under non-shooting conditions, where the liquid metal is left idle for 15 s after pouring into the shot sleeve (resembles the pouring of gravity casting), peak values of h are estimated to be in the range of values 4−6 kW/m2 K near the pouring zone, 10−14 kW/m2 K in the middle zone, and 8 12 kW/m2 K closer to the runner, respectively [15]. All these research work shows that there is a lack of agreement on the range and peak values of h during the HPDC process, which is affected by alloy type and processing conditions. Using a time-dependent h profile in FE simulations with a peak value of 10 kW/m2 K, Dou et al. [8] showed that nearly 11% of solid could form within 5 s idle time in the shot sleeve prior to the fast-filling stage. Though the second stage filling is expected to fragment these solid phases, the experimentally determined ESCs fraction is higher than the simulated solid fraction. This discrepancy could be due to the lower h values used in the simulations to predict the solid fraction during filling.

Therefore, the aim of this work is to simulate the h effect by assigning a time-dependent h profile to the shot sleeve and die cavity independently. The peak values for h-profile have been selected based on the literature (highest and lowest values reported in Al-Si alloys), and simulated for solid fractions along the shot sleeve, neck regions of the gate and drop in in-gate velocities as a function of time. The implications of having higher solid fractions around the gate and the importance of selecting a suitable h profile is briefly discussed.

Simulation Parameters and Procedures

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Fig 1(a) shows the symmetrical 3D model consisting of moulds, piston, shot sleeve, casting, and overfill. A lean type runner system adopted from [16] and two different gating configurations adopted from [4,16] have been used to investigate the effect of filling velocity and solidification near the gate regions (Fig. 1(b)). The thickness and width of the traditional and trapezoidal gates are shown in Fig. 1(c). The simulation of filling and solidification during the HPDC process is performed using the HPDC module within the ProCAST software. The meshed surfaces of the 3D model using ProCAST solver are shown in Fig. 1(d). According to the previous work by the authors, mesh convergence is achieved when the sizes are below 1.5 mm [11]. Therefore, gradual meshing is employed for casting and shot sleeve volume with finer sizes ( 1 mm ), and the remaining volumes are assigned as either 5 or 10 mm .

[picture]

Fig. 1(a) 3D symmetrical model used for simulation, (b) 2D view highlighting the dimensions of the shot sleeve and mould. (c) Detailed view of the gate showing the dimensions and gate numbers. (d) Meshed 3D volume in ProCAST for all the volumes marked in (a).

Fig. 1. (a) 3D symmetrical model used for simulation, (b) 2D view highlighting the dimensions of the shot sleeve and mould. (c) Detailed view of the gate showing the dimensions and gate numbers. (d) Meshed 3D volume in ProCAST for all the volumes marked in (a).

Two different h-profiles were selected as shown in Fig. 2(a and b) based on Ref.[15] where h-profiles are experimentally determined for fill volumes ranging from 22% to 45%. For the first profile, hss−1 (ss-shot sleeve) the curve shows one peak value at ~7 kW/m2 K and for hss−2, two peaks were imposed at the initial and final stages of filling as shown in Fig. 2(a). The filling and solidification within the shot sleeve are analysed without the piston displacement. Filling of the shot sleeve is performed at a very low filling velocity of 0.05 m/s to avoid significant turbulence. For the runner, gate, and mould regions two profiles (hrgm−1 and hrgm−2) were selected as shown in Fig. 2(b). A higher peak value is imposed within 0.5 s , since the whole filling completes within a second. The cavity filling simulations were performed with the pre-existing melt volume ( 35% fill) inside the shot sleeve, and a shot delay time of 1s was applied for piston profile.

[picture]

Fig. 2 Interface heat transfer ( h ) profiles used in the simulation for (a) shot sleeve and (b) runner, gate and mould cavity regions.

Fig. 2. Interface heat transfer ( h ) profiles used in the simulation for (a) shot sleeve and (b) runner, gate and mould cavity regions.

The simulation parameters and thermodynamic properties of the Al−7Si alloy are listed in Table 1. The energy, mass, and momentum equations for an incompressible fluid flow are given as

∇·v=0 (Mass conservation) (1)

ρ(∂v∂t+(v·∇)v)=∇·μ(∇v+(∇v)T)−∇P+ρg(2)

ρce(∂T∂t+v·∇ T)=∇·(k∇T)(3)

where v is the velocity vector, ρ is the density, μ is the dynamic viscosity of the liquid melt, P is pressure, T is temperature, g is the acceleration due to gravity, ce is the effective specific heat and k is the thermal conductivity. Additional details regarding the simulation parameters and the prediction of the heat transfer and solidification after pouring the liquid metal into the shot sleeve are detailed in the previous work of the authors [11]. The volume of fluid method is used to enable free surface fluid tracking and temperature dependent viscosity (Newtonian flow), density and enthalpy models are used for the filling simulation.

Table 1 List of fixed and variable processing parameters used in the simulation [11]

Variables/ conditions | Value/range/specifications
Alloy composition (wt.%) | Al-7Si-0.3Mg (A356/EN AC-42100)
Onset temperature of α -Al (°C) | 615.5
Solidus temperature (°C) | 554.5
Dendritic coherency temperature, 0.30% fscoh (°C) | 593-597
Pouring temperature, Tp (°C) | 700
Shot sleeve, piston and mould temperature, Tss (°C) | 200
Fill volume of metal inside shot sleeve, fv (%) | 35
Interface heat transfer coefficient between alloy volume and shot sleeve, h (kW/m²K) | hss-1 and hss-2
Interface heat transfer coefficient between casting volume and runner/gate/mould, h (kW/m²K) | hrgm-1 and hrgm-2
Interface heat transfer coefficient between mould walls h (kW/m²K) | 2
Piston displacement velocity (m/s) | 0.3/3.0 (slow/fast shot speeds)

To analyse the effect of h on the solidification, it is assumed that the defect formation is closely related to the shearing of partially solidified dendrites, coherency, and packing fraction of the solid proposed by Santos et al. [17]. Solid fractions below the dendritic coherency temperature (fscoh) is
assumed to behave similar to liquid and mass feeding is dominant ( ~0.30 for A356 alloy). After fscoh  until packing point where the resistance to flow is minimal, flow can occur in liquid films (also called shear bands) in these regions by either mass or interdendritic feeding. Above this packing range ( ~0.4 0.5), the application of force encounters a solid network which might lead to cracks or voids that are unfilled, where liquid segregations may occur.

Results

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Effect ofhss−1andhss−2on the solidification behaviour within the shot sleeve

The original version of this paper is available on https://www.scientific.net/MSF.1188.45.pdf

Fig. 3(a and b) shows the snapshots of solid fraction during filling for (a) hss−1 and (b) hss−2 profiles. At 1.5 s in Fig. 3(a and b), solidification occurs in the middle region of the shot sleeve, obstructing the flow initially due to the imposed peak value of h for both curves (Fig. 2(a)). The total percentage of solids increases rapidly from 0 to 10%, which is reduced to 9.3% by remelting due to the continuous flow of hot liquid from the pouring zone at 3.0 s (Fig. 3(a)). However, after 5s the solidified layers continue to develop along the bottom walls of the shot sleeve from the center region to the mould side along the longitudinal direction. Complete filling is achieved at 6s with nearly 14% solid fraction.

[picture]

Fig. 3 Sectional view of the shot sleeve showing solidification along the wall regions during filling for (a) hs−1 and (b) hs−2 profiles

Fig. 3. Sectional view of the shot sleeve showing solidification along the wall regions during filling for (a) h s − 1 and (b) h s − 2 profiles

Solidification in Fig. 2(b) occurs at a relatively faster rate immediately after pouring due to a higher peak value used in hss−2 as shown from 1.5 to 3.0 s . The solid fraction continues to increase and pose a significant resistance to the liquid flow. Complete filling in Fig. 3(b) is achieved at 7.5 s with nearly a 5% increase in the solid fraction compared to Fig. 3(a). The solidified layer near the pouring zone in Fig. 3(b) between 3 and 5s undergoes insignificant remelting due to the imposed second peak in the hss−2 profile (Fig. 2(a)).

Fig. 4(a) shows the locations of point analysis along the bottom walls of the shot sleeve. Based on the previous work of the authors [11], it has been found that the solidification is more rapid near the
walls, between 1 to 5 mm after employing a constant value for h from 5000 to 20,000 W/m2 K. Since the largest dendrites with sizes ranging over 0.5−1 mm solidify in these slow cooled regions, cooling curves are presented at 2 mm and 5 mm respectively in Fig. 4(b-e). For hss−1 profile, the cooling curves in Fig. 4(b) shows higher initial temperature above liquidus in the middle zone and solidifies slower compared to Fig. 4(c) in the locations closer to the mould side. For hss−2, a significant drop in the initial temperature below liquidus is noted in Fig. 4( d and e), indicating the formation of a higher volume fraction of ESCs during and after complete filling. In addition, a higher cooling rate and the time to reach solidus temperature is significantly reduced for hss−2 in Fig. 4(e).

[picture]

Fig. 4 (a) Sectional view of shot sleeve indicating the points at 2 mm and 5 mm from the bottom of the shot sleeve located at 190 mm(P1,P2) and 330 mm(P3,P4). Cooling curves at P1-P4 plotted for (b, c) hs−1 and (d, e) hs−2 profiles.

Fig. 4. (a) Sectional view of shot sleeve indicating the points at 2 mm and 5 mm from the bottom of the shot sleeve located at 190 m m ( P 1 , P 2 ) and 330 m m ( P 3 , P 4 ) . Cooling curves at P1-P4 plotted for (b, c) h s − 1 and (d, e) h s − 2 profiles.

Effect ofhgrm−1andhgrm−2on the solidification behaviour inside runner and gate regions

The original version of this paper is available on https://www.scientific.net/MSF.1188.45.pdf

One of the main limitations with the FE solver is that the solid formation within the shot sleeve is an indication of how much pre-solidification occurs before injecting the melt into the die cavity. Most of the solids formed within the shot sleeve stay in the chamber walls and only the remaining liquid is injected into the runner and die cavity [8,11]. Therefore, the effect of pre-solidification affecting further flow and filling of runner or gate regions could not be predicted effectively [8]. Nevertheless, the effect of imposed h-profiles could still be useful to understand the effect of solidification occurring on the runner and gate regions, and it can be assumed that any presence of unfragmented, long dendrites would increase the severity of filling defects. Because a smoothed particle hydrodynamics simulation has revealed that solid fraction up to 0.8 could be fragmented from the bottom regions of the walls during filling and can settle in the runner or block the liquid movement in the gate regions [18].

Fig. 5 shows the snapshots of filling velocity, temperature and solid fraction for hgrm−1 profile. To reduce the computational time, the mesh sizes for the fill volume in the shot sleeve and mould have been increased to 5 mm and 10 mm respectively, and 1 mm is applied to the runner, gate, and
casting volume. As shown in Fig. 5 (a), the filling starts initially at gates G3 and G4 and then proceeds to G1 and G2. The temperature and solid fraction curves indicate that there is no significant solid formation (Fig. 5( b,c) at this stage. Notably, the velocity at the onset of G1 and G2 in Fig. 5 (d) is relatively higher compared to G3 and G4. This is because the trapezoidal gate design contains additional narrower cross sections closer to the runner as shown in Fig. 1(c). The corresponding temperature and solid fraction snapshots indicate that there is no significant drop or solidification during filling ( <0.3fs ). The total filling time is around 1.6 s and the total solid percentage at complete filling is 31.1%.

[picture]

Fig. 5 (a, d) Fluid velocity, (b,c) temperature and (e, f) solid fraction at the onset of filling at the gates (a-c) G3, G4 and (d-f) G1, G2, respectively for hgrm−1 profile.

Fig. 5. (a, d) Fluid velocity, (b,c) temperature and (e, f) solid fraction at the onset of filling at the gates (a-c) G3, G4 and (d-f) G1, G2, respectively for h g r m − 1 profile.

To further understand the effect of fluid velocity and the development of solid fraction at the gate entry, point analysis from the middle region of each gate are plotted for hgrm−1 and hgrm−2 as shown in Fig. 6(a). Note that the snapshots shown in Fig. 5 are more representative of the solidification than point analysis, however, point analysis helps to correlate the fluid velocity and solid fraction as a function of time. In Fig. 6(b and c), the velocity profile in G3 and G4 increases progressively, while the increase in G1 and G2 is rapid at the onset of filling. In addition, G1 and G2 shows a two-stage curve where higher velocities are noted until 0.1−0.2fs and drops to less than 40 m/s after 0.2fs. Higher velocities during the initial stage of filling could break larger ESCs compared to a more gradual increase in filling velocity. In general, the solidification occurs relatively faster for hgrm−2, a higher fs is noticed during the velocity cutoff. It should be noted that these fs are formed during filling and did not account any ESCs. Any presence of large ESCs could reduce the filling velocity further.

[picture]

Fig. 6 (a) Cropped view of gate region of the casting highlighting the points located at gate entry analysed for filling velocity profile and solid fraction for profiles (b) hgrm−1 and (c) hgrm−2.

Fig. 6. (a) Cropped view of gate region of the casting highlighting the points located at gate entry analysed for filling velocity profile and solid fraction for profiles (b) h g r m − 1 and (c) h g r m − 2 .

[picture]

Fig. 7 Solid fraction snapshot at 1.15 s highlighting the progress in solidification at the gate region for (a) hgrm−1 and (b) hgrm−2 profiles.

Fig. 7. Solid fraction snapshot at 1.15 s highlighting the progress in solidification at the gate region for (a) h g r m − 1 and (b) h g r m − 2 profiles.

Fig. 7(a and b) shows the solid fraction snapshot at 1.15 s for hgrm−1 and hgrm−2 profiles, respectively. For G4 and G3, the solidification proceeds relatively slower compared to G1 and G2, due to the design of mould with thicker sections closer to G1. For the profile hgrm−2, the solidification occurring near the gate region reaches at 0.4-0.5 fs during the last stages of filling (97%). Further increase in fs above this range or the presence of ESCs in this region poses a risk for reducing the filling velocity and feeding related defects during the intensification stage.

Discussion

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Prediction of solidification within the shot sleeve is significantly influenced by the h profile. Since most of the solid formation occurs along the length of the sleeve, a higher percentage of solidification indicates more entrainment of ESCs into the die cavity. Therefore, a higher filling velocity at the gate during filling is critical to reduce the blocking effect of ESCs and feeding restriction during intensification stages [18]. Gunasegaram et al. [19] demonstrated that the pore volume fraction decreases when gate velocities are raised from 26 to 82 m/s, and high integrity castings are obtained at 82 m/s gate velocity. Although increasing gate velocity offers porosity reduction, finer dendritic sizes, and efficient feeding, higher gate velocities are not recommended due to mould erosion risk. Microstructure showed more refined grains at gate velocity of 82 m/s similar to the rheocasting process, while at 25 m/s the large unbroken dendrites (ESCs) could be seen. Similar observations have been reported by Niu et al. [20] where increasing intensification pressure along with gate velocity from 33 m/s to 61 m/s decreases the presence of larger ESCs near the gate. It has been reported that for Al−Si−Cu alloys solidification strength develops between 0.3fs and 0.6fs[21]. Higher shearing rates induced by HPDC filling is expected to enable the filling by lowering the apparent viscosity for the solid fraction as high as 0.6fs [22]. However, the effect of continuous cooling due to higher values of h and drop in velocity/pressure and gate dimensions could affect this observation and has not been reported systematically in simulation studies. An increase in the peak value of h from 18 to 24 kW/m2 shows a relatively higher solid fraction in the gate regions (Fig. 6) and reducing the gate width between 0.4 to 0.5fs (Fig. 7). It should be noted that the higher fluid velocity and lower viscosity could enable filling in these conditions for small flat castings demonstrated in the present work. However, for larger flow lengths and thin section castings, a systematic study on h is crucial to avoid defects. Secondly, it should also be highlighted that the initial solidification within the shot sleeve is not considered in the die filling. Generally, it has been agreed that the formation of solid fractions above 10% within the shot sleeve prior to filling can cause additional restrictions to both filling and feeding; defect band formation during the application of intensification pressure. Since the effect of h is greatly influenced by the combination of other processing variables such as fill volume and fill length, alloy composition, and gating system design, literature comparison becomes difficult. Further systematic works are necessary to address the effect of h on different longer filling lengths of castings and shot sleeves.

Conclusions

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Solidification within the shot sleeve and gate regions during filling was simulated for plate-type castings with two gate geometries and four interfacial heat transfer coefficient profiles ( h ). Within the shot sleeve, solidification predominantly occurs from the middle region to the mould side. The h profile with a peak value of approximately 7 kW/m2 K results in ~13%fs, which increases to ~18%fs for 13 kW/m2 K after complete filling. In the gate regions, increasing the peak values for h-profiles from 18 kW/m2 K to 24 kW/m2 K promotes local solidification between 0.4-0.5 fs, reducing the width of the channel and flow velocities. Compared to the conventional gate design, the trapezoidal gate exhibits a higher fluid velocity during filling until 0.4fs. For a given gate design, this study emphasizes that accurate prediction of fs based on h-profiles during shot sleeve filling and injection stages is necessary to ensure high-integrity HPDC components.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.45.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 57-73

doi: 10.4028/p-Wsp4qN

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

Submitted:
2026-01-19

Revised:
2026-02-22

Accepted:
2026-02-22

Online:
2026-04-20

Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

The original version of this paper is available on https://www.scientific.net/MSF.1188.57.pdf

Sri Harini RAJENDRAN 1,a*, Nagasivamuni BALASUBRAMANI 2, b and Mohamed EL MANSORI 1,c

¹Mechanics, Surfaces and Materials Processing Laboratory (MSMP), Arts et Métiers Institute of Technology, Châlons-en-Champagne F-51006, France.

a* sri-harini.rajendran@ensam.eu, b  nagasivamuni.balasubramani@ensam.eu, c Mohamed.elmansori@ensam.eu

Keywords: Additive manufacturing, solidification, microstructure, high-pressure die casting

Abstract

This article aims to review the recent advances in the laser powder bed fusion process (LPBF) of H13 tool steel for the dies used in the high pressure die casting (HPDC) applications. The effect of processing variables is briefly reviewed for the evolution of microstructure (phase transformations, as-built microstructure and carbides precipitation), mechanical properties, and defects. The second part of the review is focused on conformal cooling applications to HPDC dies, which is critical for die life and productivity. Achieving better microstructure without defects, understanding the role of processing variables in L-PBF and their interdependencies remains the key challenge for the as-built part, while the benefits of preheating and post-heat treatments are evident. Significant benefits are realized in the applications of die inserts favoring lower die surface temperature, reduced cycle time and lubrication, and thermo-mechanical stresses. In addition, L-PBF also plays a key role in die remanufacturing where significant benefits are achieved in terms of materials savings and improved performance compared to traditional repair technologies. Overall, LPBF offers a transformative pathway for high-performance HPDC dies; however, most investigations are trial-based. Long-term studies are needed for performance assessment and establishing failure mechanisms in production environments.

Introduction

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High-Pressure Die Casting (HPDC) is used to fabricate complex, high-volume, and high-precision metallic components for the automotive, aerospace, and electronics sectors. HPDC makes up roughly 60−70% of the cast alloy market, primarily for automotive applications. A significant growth for the automotive market and lightweight requirements is predicted due to the electrification of vehicles, where large capacity HPDC machines (mega/giga castings) are used in the production of integrated structural castings for front and rear end parts [1,2]. The quality of the produced components is linked to the performance of the dies used in die-casting, which are subjected to extremely severe service conditions, including high forming loads, repeated thermal cycling, intense mechanical stresses, erosion, and wear. Hot working tool steel, H13 (Cr-hot working tool steels) is characterized by excellent resistance to high temperature impact, loading and softening due to thermal fatigue, presence of medium C content and higher Cr with other carbide forming elements. The formation of fine and stable V- and Cr- containing carbides is key to the high temperature strength and hardenability [3]. Dies used in the HPDC process are generally designed for longer operating cycles (~100,000 to 300,000) to reduce repair and maintenance costs. However, most dies fail during service conditions due to heat checking, wear, plastic deformation, die soldering, and corrosion [4]. Cyclic heating and cooling during the casting process induce significant thermal gradient fluctuations, resulting in compression stresses at the surfaces of the mold and tensile stresses during cooling. A steady decrease in the surface hardness is noted after ~6500 cycles, and this softening causes cracks to appear on the surface of the dies, known as heat checks [5]. In addition to thermal loading, cyclic mechanical and tribological loadings lead to wear damage that affects the tool life and dimensions of the part [5]. Conventional dies for HPDC are typically manufactured by machining hot work tool
steels, which restricts the complex geometry of internal cooling channels and limits thermal management capabilities, ultimately shortening the die life. The severity of the die damage determines whether the die can be repaired by welding for further use or must be recycled [6]. Several approaches, such as surface treatments and improved thermal management during the HPDC process, are adopted to extend die life; however, achieving effective thermal control to improve die life remains a critical challenge. Thermal fatigue accounts for nearly 70% of the casting die failures and 30% of production costs [7].

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Fig. 1 (a) Overview of the review paper highlighting the important process variable and applications [8,9] (b) publications per year and (c) indexed keywords from the Scopus database for H13 tool steels.

Fig. 1. (a) Overview of the review paper highlighting the important process variable and applications [8,9] (b) publications per year and (c) indexed keywords from the Scopus database for H13 tool steels.

Additive manufacturing (AM), especially laser and electron beam powder bed fusion (L-PBF and E-PBF) processes, also referred to as Selective Laser Melting (SLM) or Laser Beam Melting (LBM), Direct Metal Laser Sintering (DMLS), and Electron Beam Melting (EBM) [10,11], offers greater potential. Originally developed for rapid prototyping and tooling, they have gained significant interest in recent years for promoting integrated and circular manufacturing solutions. For the HPDC industry, AM of tool steels provides significant advantages in the production of mold inserts, supporting repair and remanufacturing [6]. Notably, AM enables the fabrication of dies with conformal cooling channels, which are otherwise constrained by the limited design freedom of conventional processes [12]. Reducing the local hotspot regions within the mold, improving homogeneity or controlled cooling (directed solidification), reducing the propensity for defects and cycle time. Besides, die surface temperature reduction is critical to reduce the consumption of lubricants or die releasing agents [13,14]. AM also supports lattice reinforcements and multi-material gradients, offering localized tailoring of properties and reducing material costs [15]. Since AM facilitates seamless integration with simulation-based optimization, designers can develop and optimize applicationspecific tooling and integrated process-structure-property-performance (PSPP) modelling [16]. Although L-PBF processes enabled significant advances, such as improved part density, build rates, and the integration of conformal cooling, challenges such as residual stress, cracking, and the high surface roughness of PBF channels affect flow behaviour and heat transfer [8]. The most common
challenges are (i) achieving uniform microstructure, (ii) better mechanical properties, (iii) part density without defects (porosity, lack of fusion, cracks), and (iv) optimising heat treatment conditions [17].

Fig. 1a highlights an overview of the article focusing on the microstructure and applications of H13 tool steel (related tool steels used for mold inserts) fabricated by L-PBF applied to the HPDC process. A broad keyword search using Scopus is performed for the keywords "H13 and additive manufacturing," which yielded 293 results from 2004-2025 as shown in Fig. 1b. This unfiltered keyword search is further plotted for the number of appearances over years as shown in Fig. 1c. The keyword search mainly highlights the number of keyword appearances over the years that are mainly limited to processing methods, microstructure, and properties. These documents were manually assessed and found that there are continued interests to examine the effects of process parameters (not limited to deposition strategy, energy input, scan speed, hatch spacing), defects, heat treatments, preheating, surface modification, powder characteristics, alloying elements, mechanical and fatigue properties, mold repair and remanufacturing. Since recent reviews have extensively focused on investigating the role of process variables in detail for H13 tool steel, this review is intended to highlight only the main observations of the current status. Therefore, this review is divided into two main parts, where the first section is aimed at summarizing the key findings for the process parameters and highlighting the recent research works on the microstructure, defects, mechanical properties, and the role of heat treatment, since several research articles recently covered these topics extensively [4,11,15,17]. The second part of the review is aimed at reviewing the application of additively manufactured H13 tool steels in casting dies, which is relatively less explored and addresses the current progress, potential opportunities, and outstanding challenges, including the scope for remanufacturing processes.

Additive manufacturing of tool steels

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The factors that directly influence the robustness of additive manufactured H13 parts can be broadly classified as process parameters, powder metallurgy considerations, and post-processing treatments, each governing the microstructure evolution, residual stress development, and defect formation, which together determine the thermal and mechanical properties [17]. A review by Wu et al. [11] classified these parameters into material-dependent and material-independent parameters as shown in Fig. 2. The first group includes powder quality, laser/machine settings, and post-heat treatments. Part geometry (CAD model, slicing) and process preparation (operational, environmental, and other variables) are the main components of the second group.

[picture]

Fig. 2 Factors influencing the part quality in the L-PBF process [11].

Fig. 2. Factors influencing the part quality in the L-PBF process [11].

In this section, the focus is placed on two key aspects of AM: the role of process parameters, effects of heat treatment, and residual stress. Optimizing these material-dependent variables is essential, as they directly influence the performance, reliability, and service life of tool steel dies.

Role of process parameters on microstructure and defects

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Fabricating of H13 parts in AM without defects is challenging due to the complex interactions between the processing parameters and the resultant microstructure. In AM components, defects and microstructure are closely interconnected as both evolve under the same thermal cycles, energy input, and melt pool dynamics inherent to the layer-by-layer fabrication process.

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Fig. 3 (a) Isopleth phase diagram of H13 tool steel [20], (b) phase transformations during L-PBF of tool steel [21], (c) bidirectional scan strategy with different rotational angles ( 0,45,90,67∘ ) showing macrostructure homogeneity [22], (d) lack of fusion and solidification cracking [11], and (e) porosity map as a function of energy density [23].

Fig. 3. (a) Isopleth phase diagram of H13 tool steel [20], (b) phase transformations during L-PBF of tool steel [21], (c) bidirectional scan strategy with different rotational angles ( 0 , 45 , 90 , 67 ∘ ) showing macrostructure homogeneity [22], (d) lack of fusion and solidification cracking [11], and (e) porosity map as a function of energy density [23].

Porosity, lack of fusion, incomplete melting, hot cracks, and solidification cracks are the common defects. Porosities with irregular morphologies primarily form because of incomplete fusion, while spherical porosities could be due to gas entrapment, both of which can act as stress concentration sites, facilitating crack propagation and lowering fatigue strength [8]. The build rate is one key factor that significantly influences the manufacturing cost and is strongly dependent on process parameters such as laser power, scan strategy, scan speed, and layer thickness. These parameters control the thermal gradient of the melt pool and play a critical role in defect formation during L-PBF processing [18,19].

Fig. 3a shows the isopleth equilibrium diagram of tool steel. Austenite is more stable above 840∘C, which transforms into ferrite and carbide precipitates during cooling. Due to the rapid thermal cycling of the laser processing ( 105−107 K s−1 ), the as-built structure promotes martensite, with retained austenite within the martensite matrix ( ~10−15% ). Formation of Cr -rich carbide occurs below 800∘C without a distinct temperature range, while Mo−M6C carbides rich in Mo form between 500−600∘C [20]. Investigations by Zhang et al. [19] varied the scan speed and scan pattern to impose different thermal gradients in H13 steel, showing that these gradients govern the types and severity of defects that develop, and consequently the resulting fatigue behaviour. Specimens produced under the steepest thermal gradients exhibited microscopic solidification cracks caused by elemental segregation, along with centerline hot cracks, leading to the lowest matrix strength and fatigue performance. Although hot crack and solidification crack types are generally smaller than lack-offusion defects, they remain detrimental to mechanical performance. The specimens fabricated with the lowest thermal gradients showed moderate defect sizes, primarily spherical pores and vertical or horizontal cracks, and displayed the highest fatigue strength.

Fonseca et al. [21] examined the influence of laser power and scan speed on the microstructure along the interlayer and porosity formation. Among different test conditions, the microstructure remained largely consistent and was dominated by cellular/dendritic martensite with a significant fraction of retained austenite (16.5-29.7%). Fig. 3b shows the simplified schematic of the microstructure formation based on the temperature cycles. Temperatures above Ac3 result in the complete transformation to austenite and become martensite when cooling, with retained discontinuous austenite grains between martensite laths. Layer-by-layer thermal cycling induces intrinsic tempering, producing alternating fusion and heat-affected zones. At a higher solidification rate, the last to solidify layers do not contain massive carbide precipitates and are dominated by untempered martensite displaying higher hardness ( 552−614HV ). The bottom regions of the fusion zone showed the presence of precipitation with a dendritic/cellular structure. Heat-affected zones exhibited a degenerated or partially dissolved microstructure with more precipitates, resulting in lower hardness. The processing parameters in this scenario had a limited influence as the microstructure is strongly affecting consolidation quality.

Kisraoui et al. [22] investigated the influence of two printing strategies, bidirectional and chess scanning, combined with four-layer rotation angles ( 0∘,45∘,67∘, and 90∘ ), on the mechanical properties, hardness, and porosity of L-PBF processed H13 tool steel. The bidirectional strategy consistently outperformed the chess strategy, producing samples with lower porosity, higher relative density, and superior mechanical properties, whereas the chess strategy resulted in more severe defects, including extensive cracking and void formation, particularly at 45∘ and 67∘ rotations. As shown in Fig. 3c, a better homogeneity in the microstructure is achieved by consistent melt pool depth, macrostructure uniformity, and stable thermal profile. Higher elongation values ranging between 5.5−9% with better repeatability are noted for 67∘ rotational angle. The segmented scanning path and rapid thermal cycling inherent to the chess pattern mainly promoted elongated melt pool boundaries, intensifying thermal stresses, which are prone to brittle failure under tensile loading. Significant cracking is noted during printing due to residual stresses, which propagated along the melt pool boundaries and within the heat-affected zone. Fig. 3d shows the most common defects due to lack of fusion between the melt pools and solidification cracking formed during solidification across the melt pool [11]. Lack of fusion has been noted in samples with low-volumetric energy density (VED) where the neighbouring tracks did not melt, leading to a triangular void. Besides, gas pores
were noted in high VED, which is attributed to the vaporization effects because of their spherical morphology. The ratio of depth-to-width in L-PBF is influenced by power and speed, where lower speeds with higher power output can increase the depth of penetration. Higher scan speeds for a given power output could affect the penetration depth, and the optimal range for achieving good metallurgical bonding (2-3 layers) ranges from 0.3-0.6. Studies by Omidi et al. [23] showed that both size and number of porosities (keyhole and gas) increase for VED up to 57 J/mm3, and lack of fusion was eliminated at this threshold. At 59 J/mm3, keyhole porosities were absent and gas pores were evenly distributed, which is referred to as the "safe zone". However, when VED is increased above 61 J/mm3, large keyholes form and lead to the reduction in the relative density of the part. The effect of VED on the porosity formation is shown in Fig. 3e. A higher volumetric energy density promotes the formation of a dense microstructure in L-PBF H13 steel, which is essential for achieving high thermal conductivity properties.

Effects of heat treatment and substrate preheating

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As-built L-PBF H13 steels typically show poor ductility, prompting efforts to improve their mechanical performance through post-processing heat treatment. Heat treatments generally transform the retained austenite into martensite. While comparing post-treatment protocols, a single hardening followed by double-tempering treatment resulted in recrystallized grains, lath-type martensite, and the precipitation of fine spherical carbides along the boundaries of martensite laths. A doubletempering treatment at 550∘C promotes precipitation of fine spherical carbides along the boundaries with the highest tensile strength of 2100 MPa , however, increasing the double-tempering treatment temperature to 650∘C increases the sub-grain size and continuous network of carbide layers along with primary austenitic grain boundaries, promoting the ductility [20].

Table 1 Heat treatments on the microstructure and mechanical properties of H13 tool steels.

Heat treatment | Tensile strength (MPa) | Elongation (%) | Micro hardness (HV) | Comments on the microstructure observation | Ref
H13 AB | 1900 | 9.7% | 593 | α-martensite matrix with 18.6% retained austenite (RA) | [20]
DT at 550°C | 2100 | 7.6% | 665 | Promotes fully tempered martensite formation and precipitation of fine carbides |
DT at 650°C | 1410 | 10.2% | 485 | Leads to carbide coarsening, recovery, recrystallization and stress relaxation |
SH at 1040°C + DT at 550°C | 1450 | 9.2% | 532 | Fully tempered martensite formation and fine carbides distributed along the martensite lath boundaries and the prior austenite grain boundaries. |
H13 AB | 2035 | 12.5 | 569 | α-martensite matrix with 14.1% RA along with the precipitation of coarse VC (70.7 nm), and Fe₃C carbides | [24]
DT at 550°C | 2265 | 7.8 | 652 | Promotes fully tempered martensite formation, α-martensite grain coarsening, disappearance of Fe₃C and refinement of VC (23.8 nm) |

Note: AB- As-built, DT-double tempering, SH- solution hardening
In the as-built state, the microstructure of H13 consists of lath-type martensite and retained austenite within the martensitic regions. The percentage of retained austenite is higher ( 17% ) for low energy density ( 75 J/mm3 ) and reduces to nearly 6% when the energy density is increased to 85 J/mm3, exhibiting exceptionally high strength of 2 GPa and hardness of 540−600HV, along with moderate ductility ( 8.4−10% ). Following post-build tempering at 650∘C, the microstructure transforms into tempered martensite with finely dispersed carbides. Although this heat treatment leads to a reduction in strength, it significantly enhances ductility ( 15−16% ), yielding mechanical performance comparable to that of conventionally processed H13 [19]. Quenching followed by double-tempering has been shown to increase thermal conductivity by up to 12%, as re-austenitization
eliminates the melt-pool structure and its associated boundaries, thereby reducing thermal resistance. This improvement, however, comes at the cost of reduced mechanical strength [28]. Yan et al. [29] demonstrated that optimising the tempering temperature can further improve strength-ductility combinations. As-printed H13 exhibited a yield strength of 818 MPa and an elongation of 2.42%, reflecting a brittle microstructure. Tempering at 600∘C and 700∘C improved their ductility to 5.8% and 10.9%, respectively. However, in terms of microstructure, 600∘C tempering treatment exhibited a tempered martensitic matrix and dense precipitation of fine V-rich carbides, improving yield strength to 1483 MPa . Meanwhile, the microstructure of 700∘C tempered H13 coarsened the carbide precipitates, thus relatively lowering the yield strength to 877 MPa . Table 1 summarizes some recent notable works on the effects of heat treatments with microstructural observations.

Preheating of the substrate has been identified as an effective strategy for mitigating residual stresses in AM components. Elevated residual stresses, together with a higher carbide fraction, can increase susceptibility to crack initiation and propagation under thermal loading conditions [18]. Preheating temperatures below 200∘C promote horizontal cracking, while temperatures between 200 and 600∘C are identified as the crack-free processing range [11]. Zong et al. [30,31] investigated the effect of preheating temperature at 200∘C and 500∘C during L-PBF processed H13 steel. Higher preheating temperature affects the molten pool geometry by increasing the average depth and width, promoting epitaxial growth, carbide precipitation in the cell boundaries, and reducing the number of pores along the build direction. Bainitic transformation is promoted for preheating at 500∘C, while the low preheating samples are still dominated by martensite microstructure ( 200∘C ) [30]. When fabricated at a preheating temperature of 200∘C, variations in build orientation (horizontal (H13200H) and vertical (H13−200 V) ) did not result in significant differences in tensile strength and elongation (Fig. 4a). In contrast, a noticeable variation in these mechanical properties is observed for H13 specimens produced using a strip scan strategy with a 61∘ rotation angle, even under the same preheating conditions. This indicates that differences in scan strategy and processing parameters can have a more pronounced effect on mechanical performance than build orientation and preheating temperature. Fractographic analysis (Fig. 4b) further reveals that the improved properties of H13200 V specimens are associated with reduced defect populations and fracture behaviour governed by martensitic lath structures [26]. Although porosities are present in the samples, a mixed fracture mode can be seen where the fracture is controlled by microstructure rather than the defects.

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Fig. 4 (a) Mechanical property comparison of conventional and LPBF-processed H13 steel and (b) fracture surface of H13 vertically built on preheated substrate at 200∘C [31].

Fig. 4. (a) Mechanical property comparison of conventional and LPBF-processed H13 steel and (b) fracture surface of H13 vertically built on preheated substrate at 200 ∘ C [31].

For AM processing, it is critical to understand the role of the preheating and post-heat treatment process, which generally produces secondary carbide precipitation, shifting the secondary hardening peak, and converting tensile stresses into beneficial compressive stress, improving the overall ductility for tooling applications [32].

AM inserts for conformal cooling of dies in HPDC applications

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Direct metal additive process is highly attractive for tool building process due to rapid response time to market, enabling tooling functionalization and higher performance, although the process chain involves standard processes including machining, thermal treatments, grinding, and polishing. A much-valued advantage is the fabrication of conformal cooling channels for molds used in both plastic injection and metallic materials as an alternative to the deep drilled straight channel cooling system. Conformal cooling channels match the design of the mold cavity (equidistant from the mold surfaces) mostly with variable and non-circular cross sections with additional design features such as fins, helical, or lattice structures [13]. The efficiency of the cooling channels is determined by several factors including the type of channels (circular, ellipsoid or others), layout (spiral, mesh, vascularized), placement, location of adjacent channels, and other modular aspects considered either in design or in construction (lattice structures) [13].

[picture]

Fig. 5 (a) Different designs of conformal cooling inserts [34], conformal cooling HPDC inserts for (b, c) gear box housings [35,36], (d) core pin [32], and (e) metal diffuser at the gating system [37].

Fig. 5. (a) Different designs of conformal cooling inserts [34], conformal cooling HPDC inserts for (b, c) gear box housings [35,36], (d) core pin [32], and (e) metal diffuser at the gating system [37].

Modelling results suggest that enhanced thermal management can extend die life from approximately 80,000 to at least 120,000 cycles, a 50% improvement that offers clear benefits for high-volume manufacturing. Improved thermal control also helps eliminate hot spots and reduce
shrinkage porosity, leading to better part quality and a potential reduction in scrap rate from 5% to 3%. Collectively, these gains can lower unit production costs by up to 10%, representing a meaningful advancement toward both economic and sustainability benefits [33]. Different geometries of cooling channels with a single loop, a single loop with a larger diameter, a single spiral loop, and a bidirectional spiral loop are investigated for the cooling performance, pressure drop, stress, and failure life by He et al. [34] (Fig. 5a). Overall, single-spiral and bi-directional spiral loops showed the lowest thermal gradient and reduced risk of vapour bubble formation using simulations; however, the propensity of fabrication defects is not discussed for all designs.

Fig. 5b shows the application of a conformal cooling insert to the MQ 200 GA gearbox housing reported by Andronov et al. [36]. Pressure test revealed that a significant number of parts failed due to the defect in the START/STOP feeder area. Simulation of solidification has shown that the last to solidify metal is located directly inside the feeder (with a shrinkage volume of 0.186 cm3 ) in which either the continuous feeding is restricted or no heat dissipation is in place. To address this issue, a conformal cooling insert (MS1 maraging steel) was designed and printed using the L-PBF process as shown in Fig. 5b. During the test series of 8000 casting cycles, the pressure test indicated that the acceptable number of parts increased to 92%, shrinkage pore volume reduced by 6%, and produced nearly 43000 castings before showing signs of wear. This study also demonstrated that the simulation tools are more reliable in predicting the temperature field verified by thermal camera imaging.

Fig. 5c addresses the issue of section thickness affecting the cooling rate in the HPDC process using conformal cooling of molds. It has been shown that the highest temperature detected after 5 cycles for conformally cooled die insert is ~104∘C lower than the traditional die. In addition, the duration of positioning and blowing is reduced, die cooling time and solidification time were faster, contributing to the overall cycle time improvement by 11%. The AM fabricated die tested for different sprinkling oil temperatures for 15000 cycles showed that highly loaded areas of the casting had slightly fewer defects compared to the standard die [35]. For an engine crank case produced by HPDC process, Karakoc et al. [38] reported the effect of conformal cooling on porosity reduction, die surface temperature, and microstructure. Simulation results indicated that the peak die surface temperature without cooling is in the range of 350−370∘C, which is reduced to 310−340∘C with conformal cooling (before lubrication). This reduces the lubrication time from 4 s for the traditional mold to 2 s after employing conformal cooling. Dendritic size refinement, porosity size reduction (251 to 156 mm3 and 108 to 91 mm3 ) were noted in two locations of the casting after conformal cooling, although the porosity could not be eliminated completely.

Similarly, a Zamak alloy fabricated using a conformally cooled die in the HPDC process offers a 50% reduction in surface defects for the same level of spray lubrication, decrease in cycle time, and shrinkage porosity volume. The redesigned insert was used in the production cycles for nearly 200 cycles without any damage due to wear [39]. Fig. 5d shows a case study of HPDC core pin that demonstrated the practical benefits of AM fabricated die parts. The SLM built pin, incorporating a 2.5 mm internal cooling channel, achieved a 15-20% reduction in surface temperature compared to the conventional, non-cooled design. This improved thermal management shortened the casting cycle time by 2 seconds and reduced surface porosity in the cast parts, leading to fewer rejections. Fig. 5e demonstrates the application of conformal cooling to the metal diffuser at the gating system that typically experiences a pressure of 70 MPa and metal flow speeds of 30−40 m/s. A stress threshold is adopted (400MPa at 400∘C and 600 MPa at .600∘C) to analyse the variation of cyclic stresses of the designs numerically. Design No 3 offered better performance of all; interestingly, design No 2 has large fluctuation of stresses from 300 to 700 MPa , while the stresses in traditional design 1 are stable at 500 MPa . It has been highlighted that often a cooling channel designed closer to mold surfaces could enhance cooling; however, large gradients over a short distance could lead to earlier failure [37]. All these results demonstrate that the industrial readiness of conformally cooled AM tooling for HPDC applications [32]. It should be highlighted that the cooling enables faster cycle time, whereas more analysis is required to assess the component thickness, filling speed required, distance from the insert surface, and numerical simulations to verify adequate feeding. For instance, semi-solid HPDC
provided similar benefits to that of the conformal cooling; however, the use of conformal cooling with semi-solid metals exhibits difficulties to fill and porosities in the casting [14].

Geometrical constraints and surface roughness of the cooling channels are very important for better performance over a longer period. Typically, large overhangs and build angles less than 45∘ require support structures. It has been reported that the cooling channels fabricated above 10 mm led to failure by collapsing where support structures are essential (Fig. 6a). Cooling channel with supporting structures inside facilitates the manufacturability of cooling channels with diameters greater than 10 mm until 20 mm ; however, for reduced internal struts, thickness below 4 mm causes fracturing due to poor fusion and residual stresses. A 20% reduction in cooling time has been observed for a 13 mm strut design compared to an 8 mm cooling channel without any supporting structures [40].

Fig. 6b shows the corrosion and crack formation of the single loop cooling channel. Cracks formed from inside the conformal cooling channel and areas undergoing corrosion are identified by the presence of grooves and strips. Extended exposure to coolant and vapour formation is the main reason for the strip formation. The presence of line distribution of pores during the fabrication stage due to gas or vaporisation of powders could act as weak sites for crack initiation and propagation. In addition, pores present in molten pool boundaries can also act as weak sites for crack propagation and coolant leakage [34]. Failure of an HPDC insert fabricated using 18 Ni 300 maraging steel is shown in Fig. 6c. The insert operated with water at 150∘C and 5.25 bar and failed within ~15000 cycles, which is very short compared to a typical HPDC service life. Detailed analysis indicated that the cracks preferentially initially from the channel edges, where a combination of thermo-mechanical stresses during operation and defects/residual introduced during the fabrication process are also stated as possible sources. Since most of the cracks propagated in a straight path, the probable mechanism could be stress-induced corrosion due to cyclic gradients rather than melt pool geometries or other process-induced factors [41]. Another important characteristic feature of AM processed cooling channels is the higher surface roughness, which on the one hand could promote heat transfer, while exhibiting inferior flowability ( <10 mm ) especially when the flow becomes turbulent, leading to failures [42].

[picture]

Fig. 6(a) Manufacturability analysis of AM fabricated cooling channel indicates the collapsing defect [40]. (b) Major cracks originating from the inner side of the cooling channels (1), crack propagation (2), and porosity areas (3) [34] and (c) cracking of a die HPDC insert (18Ni300 maraging steel) [41].

Fig. 6. (a) Manufacturability analysis of AM fabricated cooling channel indicates the collapsing defect [40]. (b) Major cracks originating from the inner side of the cooling channels (1), crack propagation (2), and porosity areas (3) [34] and (c) cracking of a die HPDC insert (18Ni300 maraging steel) [41].

AM also enables die design concepts in bimetallic structures that are not achievable with conventional machining, allowing HPDC dies to be optimized for thermal management, durability, and functional integration. Copper is widely used for heat dissipation due to its high thermal conductivity of 402 W/mK. Incorporating a copper lining inside the cooling channels of the steel mold has been shown to improve the cooling rate by approximately 12% [43]. Three-dimensional
(3D) lattice composite structures can significantly broaden the spectrum of achievable thermal properties, creating new opportunities for designing multifunctional tool dies. For HPDC dies, which are subjected to extreme thermal loads, H13 lattice structures infiltrated with copper offer a promising route to produce dies that dissipate heat more efficiently, thereby enhancing both die performance and casting quality. Osman et al. [44] developed a two-step approach in which L-PBF was first used to fabricate functionally graded H13 lattice structures, followed by copper infiltration and quenching with double-tempering. L-PBF H13 structures featured an octet truss unit cell designed for load capacity and efficient heat transfer. The H13 volume fraction was graded from 40% at the top to 100% at the bottom. The as-built H13 exhibited a lath martensite microstructure with 16% retained austenite. Copper infiltration produced a nearly fully dense composite with excellent bonding between the steel and copper phases, while preserving the tempered H13 microstructure. The resulting composite exhibited highly tuneable thermal conductivity, ranging from 34 to 145 W/mK depending on the H13 volume fraction, which is significantly higher than the thermal conductivity typically reported for L-PBF fabricated H13 (22-24 W/m K [28,45]).

Asnafi [9] evaluated the use of laser-based AM, specifically L-PBF for manufacturing, surface treatment, and repair of tooling across cold-working, hot-working, and injection-molding applications. Compared to the mechanical properties of wrought H13 (yield strength of 1290-1570 MPa , and tensile strength of 1500−1600MPa in hardened condition), AM exhibits a lot of variability due to the challenges in optimising process conditions for defect reduction and microstructure uniformity. This is also reflected in fatigue testing and other performance studies shown in Fig. 5 and Fig. 6 for cooling channels [15]. Functional enhancements to the die surface can enhance the performance; for instance, Ni-based cladding applied to an L-PBF maraging steel subs strate reduced friction by 25% and improved wear resistance by a factor of 45 . Other ceramic reinforcements (carbides, borides, and oxides) or hard-facing with Co-based and Ni-based alloys can enhance resistance to high temperature, wear, and corrosion; however, the increased processing cost limits their broader applications [9].

Role of AM in die remanufacturing

The original version of this paper is available on https://www.scientific.net/MSF.1188.57.pdf

Remanufacturing is a process of reworking the end-of-life product to be usable with its original or better performance as a newly manufactured product with warranty [9]. This involves a series of processing stages inducing disassembly, sorting and cleaning, inspection, reconditioning, reassembly and testing. This requires higher skills than the design of the original product as it involves multiple steps in dismantling, restoring, replacing, and testing individual and whole (assembled) components to validate the functionality of the remanufactured part. The key benefits of the remanufacturing process include effective resource management, reduction in the usage of materials and resources, lower emissions and energy usage, and provides significant opportunities for the development of skilled jobs and economic growth.

[picture]

Fig. 7 Common failure modes of HPDC dies and a summary of traditional methods employed in traditional repair works[6].

Fig. 7. Common failure modes of HPDC dies and a summary of traditional methods employed in traditional repair works[6].

Fig. 7 shows the comparison of the most used welding technologies in die repair, where the selection of a particular process depends on various attributes such as cost, portability, set-up time, quality of the weld, and other considerations such as post-weld treatments. Laser-based techniques show superior metallurgical quality of the weld; however, as discussed in the above sections, the role of processing variables, residual stress, and martensite formation needs to be addressed [6].

Compared to original manufacturing, remanufacturing requires more skills and competency to determine the worthiness of the component. This includes accessibility of the damage, volume, shape, and machinability considerations after remanufacturing. Generally, the process of remanufacturing starts with pre-machining of damaged parts, where the worn-out surfaces or heat checks and corroded surfaces are removed prior to AM. Secondly, based on the type of defects (surface impact defect or superficial cracking), a reverse engineering approach can be used to generate the damaged surface from which the undamaged surface will be reconstructed [46]. Remanufacturing of casting dies holds significant value retention, boosts productivity, and enables circular manufacturing in foundries; however, establishing a remanufacturing pathway is more complex as it relies on operator skills.

Summary and Prospects

The original version of this paper is available on https://www.scientific.net/MSF.1188.57.pdf

Extending die life is crucial for the productivity of the die casting industry, especially in HPDC and ultra-large castings, as it boosts productivity and supports circular manufacturing practices in foundries. Laser powder bed fusion process provides several key benefits in terms of manufacturability, microstructure, and mechanical properties of H13 dies used in HPDC process. In particular, conformal cooling die-inserts gained significant industry interest for increased die life, which otherwise seems difficult to achieve with conventional manufacturing processes. Reduction in the size of shrinkage porosities, lubricant usage, core temperature, cycle time, and stabilizing thermal fields are key benefits of die inserts. Best practices for producing AM parts with reproducible properties require a complete understanding of processing variables (powder quality, laser variable, and machine variables) to achieve high density as-built parts, followed by post-processing heat treatment to improve the microstructure homogeneity, residual stress reduction, and precipitate formation. Preheating treatment has also been shown to reduce residual stresses, a fraction of retained austenite, and solidification cracking. Integration of AM into die remanufacturing offers significant cost benefits, along with additional opportunities for functional improvements (such as conformal cooling and laser surface treatments), compared to complete die replacement in case of failure. Nevertheless, significant challenges lie ahead to achieve successful integration of AM in die applications for longer service life.
(i) The primary challenge lies within the fabrication step where the role of processing variables and factors causing variability within the process needs to be established. The importance of preheating to control microstructure and cracking, and post-processing heat treatment to reduce residual stresses is evident. Understanding the effect of machine variables, strategies of printing and controlling their interdependencies could reduce variability in the results due to porosities (lack of fusion or keyhole pores), cracking, and as-built microstructure. Data reported in the literature are often incomparable due to the inconsistency in methodologies and procedure.
(ii) The second level of challenge lies in the testing of AM parts produced in the actual production environment, for both insert and remanufactured parts. Numerical simulation studies for topology optimization, testing under controlled conditions reveal promising results for die inserts (reducing the cycle time, lubrication time, peak temperatures and stresses); however, longer testing duration and follow-up studies on the failure mechanisms are essential. While this review is limited by the data available in such studies, only a few studies have stated that failure of conformal cooling channels before the expected lifetime of the die. In some cases, it has been stated that there is no conclusive evidence on whether processing defects or operational defects caused failure. Therefore, more standardization of tests and research work is needed in this regard.

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The original version of this paper is available on https://www.scientific.net/MSF.1188.57.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 75-83

doi: 10.4028/p-5uWMrS

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-22

Online:
2026-04-20

Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

The original version of this paper is available on https://www.scientific.net/MSF.1188.75.pdf

KTARI Ahmed 1,a, LU Davis 2, b, AINSWORTH Griffin 2,c and EL MANSORI Mohamed 1, d *

1 Laboratoire MSMP-EA7350, Arts et Métiers ParisTech, 2 cours des Arts et Métiers, 13617 Aix-en-Provence, France

2 Texas A&M Engineering Experiment Station (TEES), Texas A&M university, College Station, TX, 77843, USA

a  ahmed.ktari@ensam.eu, b  magmagon@tamu.edu, c  griffinainsworth@tamu.edu, dmohamed.elmansori@ensam.eu

Keywords: Remanufacturing by casting, Reverse engineering, 3D scanning, Sand-mold printing, 3D surface deviation

Abstract

Remanufacturing by casting of end-of-life (EoL) components can be challenging as it requires specific molds/cores, machinery and tooling. The use of 3D scanning technology and rapid casting processes can produce high-quality, efficient components. However, error propagation during the manufacturing process can significantly affect the dimensional accuracy of the final product. In this study, the dimensions of a case study part were evaluated at the main stages of the rapid hybrid low-pressure sand casting (LPSC) process, from the initial CAD model to the final casting, to identify the main causes of final 3D surface deviation. The casting design was optimized using coupled thermal and fluid-flow FE computations for two suitable casting orientations: horizontal (H) and vertical (V). After the 3D sand-mold printing process, an optical 3D scanner was used to extract surface data from each printed mold part. 3D surface deviations caused by the printing process were evaluated by comparing the individual mold components to their original CAD models using GOM Inspect Pro ® software. The final castings were also compared to the initial CAD models for both orientations to quantify the overall 3D surface deviations resulting from the rapid LPSC process chain, including 3D printing, liquid metal shrinkage and contraction during solidification and cooling. The results provide a foundation for improving dimensional accuracy of one-off replacement components produced by the hybrid LPSC process.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1188.75.pdf

Remanufacturing is a key strategy in the circular economy, designed to extend product lifespans and reduce resource consumption. Its primary goal is to restore worn components or assemblies to their original or improved functional condition [1]. In recent years, remanufacturing by casting has emerged as a new branch for producing large metallic components with intricate geometries. This approach is used when direct additive manufacturing (AM) processes, typically preferred for rapid repair of localized damage, are impractical due to technical, economic, or material constraints, and when original spare parts are unavailable [2]. It enables the production of one-off replacement parts for obsolete or damaged components [3], thereby extending the service life of existing systems and supporting sustainability and resource efficiency principles.

The development of precise 3D scanning technologies, additive manufacturing, and low-pressure casting (LPC) has improved the production of one-off replacement components for complex light metal castings. The rapid LPC process reduces costs and lead times while providing better mechanical properties than traditional gravity casting [4]. However, integrating rapid LPC technology in realworld applications requires characterizing the dimensional deviations of the final casting compared to its nominal CAD. Castings without sufficient machining or heat-treating allowances are unusable, while those requiring excessive material removal by machining or grinding increase lead time and
cost. It is essential to understand how dimensional errors propagate throughout the manufacturing process, from 3D scanning to final casting.

Unlike traditional sand mold casting, which requires skilled labor and time-consuming manual preparation, the AM processes accelerate the 3D sand mold production, reducing labor costs and significantly shortening production times. However, the quality of the sand mold is controlled by a number of printing parameters, including the sand grain size, the binder content, the volume of binder droplet, the recoater speed and the curing time which not only affect the mechanical properties [5], density [6] and permeability [7] of the mold but also its surface roughness [8,9] and dimensions [1012].

Prior studies consistently show that dimensional accuracy in 3D printed sand molds (3DPSM) is influenced by both material and printing process factors. Increasing binder content or droplet diffusivity reduces dimensional accuracy due to binder bleeding and axis-dependent spreading effects, with deviations varying across the x,y, and z directions [13,14]. Build orientation also plays a significant role: vertically or 45∘ oriented parts exhibit better geometric fidelity than horizontally printed ones due to reduced powder compressibility [15]. This effect can be mitigated by placing parts at the bottom of the job box to improve support [12]. Additional parameters further influence shrinkage-driven deviations: finer sand grains, unfavorable grain shape, and longer curing times all increase volumetric contraction and dimensional error [16].

The use of 3D scanning technology and rapid casting processes can help produce high-quality near-net shape castings, despite the dimensional deviations that may arise from the 3D printing process of the sand mold described above. However, the dimensional deviation does not end after mold printing; it continues during the subsequent casting phase in the remanufacturing process, which can significantly affect the dimensional accuracy of the final product. This deviation can also be influenced by the casting process, as additional dimensional errors may be introduced, including mold and core expansion [17], metal shrinkage [18], core displacement [19], warping [17], and heat accumulation [20]. All these factors must be considered during the mold design stage to reduce the dimensional deviation of the remanufactured cast.

The objective of this study is to identify and analyze the key factors contributing to dimensional errors in the rapid hybrid LPSC process and to assess their impact on the dimensional deviation of parts during digital remanufacturing, from the initial CAD design to the final casting through a case study.

Materials and Methodology

The original version of this paper is available on https://www.scientific.net/MSF.1188.75.pdf

In this paper, the failed part made from aluminum alloy (AlSi13Mg0.3) is scanned using Zeiss ATOS Q optical scanning technology and then processed into a 3D CAD model using Zeiss Reverse Engineering Software ®. This CAD model, shown in Fig 1, serves as the basis for designing a sand mold.

[picture]

Fig. 1. The original CAD part with key characteristic dimensions

Fig. 1. The original CAD part with key characteristic dimensions

The sand mold is designed and printed according to the parameters shown in Table 1. After curing, the sand mold is brushed and cleaned to remove all loose sand particles. The 3DPSM parts were scanned and their dimensional errors compared to their original CAD were determined using Zeiss GOM Inspect Pro ®. The final steps include performing the LPSC process, followed by knocking out and trimming to obtain the finished casting. The casting can then be scanned to assess the dimensional errors between the final casting and the original CAD part.

Table 1 3D printing parameters of the sand-mold

Average grain size: | 140±25 μm
Resin binder content: | 1.6 %
Activator content: | 0.18%
Recoating speed (Rs): | 0.182 m/s (14%)
X-resolution (Xr): | 0.11 mm
Y-resolution (Yr): | 0.102 mm
Z-resolution (layer thickness): | 0.280 mm
Print head voltage: | 75 V

In addition, the dimensions of casting parts were evaluated at all stages of the rapid hybrid lowpressure sand casting (LPSC) process, from the initial CAD model to the final casting, to identify the main causes of the final 3D surface deviation. Before physical casting, each printed mold part was scanned using an optical 3D scanner. The 3D surface deviations caused by printing were determined and evaluated. The proposed methodology for evaluating the dimensional error generated in the remanufacturing process using rapid LPSC is summarized in Fig. 2.

[picture]

Fig. 2. Evaluation of dimensional error propagation in remanufacturing by rapid LPSC process

Fig. 2. Evaluation of dimensional error propagation in remanufacturing by rapid LPSC process

During remanufacturing using rapid LPSC process, dimensional errors can arise at three stages: i ) 3D scanning of the EoL part and CAD preparation, which includes 3D scanning, CAD model reconstruction and repair, and adding casting allowances; ii) 3D sand mold printing and assembly; and iii) LPSC process. In this study, dimensional errors that occur between the original EoL part and
the digitized CAD model were not considered. This decision stems from the need to apply various casting geometry repairs to the scanned EoL part to compensate for in-service phenomena such as wear, fatigue, oxidation and viscoplastic deformation. In addition, various casting allowances must be determined based on the casting material, casting process and functional surfaces. Given that the main objective of this work is tracking error propagation from the initial CAD to the final casting, only errors generated by the mold printing process and the LPSC process were investigated.

Result and Discussion

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FE simulation-based casting design validation

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After reverse engineering to determine the digital model of the part, remanufacturing by rapid LPSC begins with FE-simulation to identify hot spot positions, followed by the addition of the filling and risering systems according to design rules. For the present case study, two casting configuration are considered: horizontal (H) and vertical (V). Fig. 3 shows the evolution of solid fraction during the cooling phase for both orientations. After thermal optimization, thermal-fluid flow simulations were performed to optimize process conditions (temperature and filling sequence) to avoid volume defects related to the filling and cooling phases such as misrun and shrinkage. All the thermalphysical properties for the alloy and mold, used in the FE simulation are summarized in Table 2.

[picture]

Fig. 3 FE-simulation of LPSC for V (left)- and H (right)- configuration

Fig. 3. FE-simulation of LPSC for V (left)- and H (right)- configuration

Table 2 Thermal-physical properties of the cast and 3DPSM

| T [°C] | Density [kg.m⁻³] | Enthalpy [kJ.kg⁻¹.°C⁻¹] | Conductivity [W.m⁻¹.°C⁻¹] | Fraction solid | Viscosity [cP]
Alloy (AlSi7Mg03) | 25 | 2675 |  | 154.2 | 1 | -
548 (Tsol) | 2565 | 542 | 175.0 | 1 | -
570 | 2522 | 564 | 125.0 | 0.49 | 1.746
590 | 2511 |  | 101.2 | 0.35 | 1.607
613 (Tliq) | 2473 | 1010 | 80.0 | 0 | 1.481
720 | 2452 | 1246 | 80.0 | 0 | 1.153
Sand mold (Furan resin- bonded silica) | 20 |  | 670 | 0.71 | - | -
300 | 1590 | 883 | 0.6
900 |  | 1006 | 0.73

Surface deviation characteristics of the 3DPSM

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In this work, all the mold parts for both H - and V -orientation are nested within the job box as shown in Fig.4a. Then the printing process was started until its completely finishing. It is important to note here that the printing resolution along X,Y and Z are defined as 0.11 mm,0.102 mm and 0.28 respectively. The Z resolution is defined indirectly since it is equal to twice the sand grain diameter. The X and Y resolutions are determined by the center-to-center spacing of adjacent inkjet nozzles within the printhead, which defines the minimum binder droplet deposition interval in the horizontal (in-plane) directions.

[picture]

Fig. 4 Nesting of mold parts, 3D printing, and surface deviation inspection using 3D scanner

Fig. 4. Nesting of mold parts, 3D printing, and surface deviation inspection using 3D scanner

After printing, all parts were de-powdered and scanned with a 3D scanner to digitize their surfaces (Fig. 4b). The resulting 3D digital molds were compared to the original CAD models to assess dimensional deviations under the studied conditions. Fig. 4c shows an example of the 3D surface deviation map for mold parts V3 and H2, where the surface deviation value can be positive or negative compared to the original CAD model. The deviations are color coded for clarity with red indicating the upper limit of deviation, and blue representing the lower bound. For V3, printing accuracy decreases as angles increase in curved regions, a trend also confirmed by H 2 . Additionally, the top surfaces defining the cavities exhibit banding with deviations of approximately -0.1 to -0.4 mm , known as the staircase effect. Due to the resolution limits of sand grains, highly accurate angled or curved surfaces, especially at small angles relative to the horizontal plane (OXY), cannot be achieved. To estimate the average surface deviation caused by the printing process, a quantitative analysis was conducted on the scan data of all 3DPSM parts. Fig. 5a shows the legend associated with each scanned mold, where the maximum and minimum limits are defined using a ±3σ criterion.

[picture]

Fig. 5 Dimensional deviation metrics on the 3DPSM parts compared to their CAD reference

Fig. 5. Dimensional deviation metrics on the 3DPSM parts compared to their CAD reference

Fig. 5a also shows the dispersal of the point cloud, which forms the generated mesh from the 3D scan, within this 3σ range. According to the color scale, points (i.e., regions) shown in yellow mostly correspond to the highest dimensional accuracy, indicating minimal deviation magnitude. For further understanding, the point cloud distribution obtained from all 3D scanned mold parts was postprocessed, and the maximum, minimum, and average deviation values are shown in Fig. 5b. It is clear that the surface deviation is mostly negative, with an average value of -0.311 mm . This result indicates that the printed sand mold parts have dimensions smaller than their initial CAD models.

3D surface deviation analysis of remanufactured as-cast

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After mold assembly and completion of the LPSC process, the as-cast parts from both configurations underwent knockout and trimming, followed by 3D scanning to evaluate surface deviations, as shown in Fig. 6. Similar surface topologies are observed on the cast components, notably the staircase effect, which reflects the geometry of the mold surfaces. The measured surface deviations range from approximately -1.034 mm to +0.941 mm for the H configuration and from 0.931 mm to +0.837 mm for the V configuration. Regions shown in green indicate deviations close to zero. The red regions observed in the H configuration, corresponding to deviations exceeding +0.9 mm near the internal core surface, are likely due to core shift during mold assembly or the influence of molten metal flow within the mold cavity.

[picture]

Fig. 6 Castings from the LPSC process to 3D scan for both H- and V- configurations

Fig. 6. Castings from the LPSC process to 3D scan for both H- and V- configurations

To better understand the effect of the surface deviation on dimensional accuracy, a simplification method is followed in this work. The six characteristic cast dimensions presented in Fig. 7 were calculated based on the 3D scanned casts and then compared to the initial CAD model to access the total dimensional deviation of the rapid LPSC process. Fig.7a shows an example of the followed procedure performed using Zeiss inspect® on the basis of the casting point cloud to find the value of the cylinder diameter. In this study, different fitting algorithms, such as Gaussian, Chebytshev and Point median were applied for each cylinder, surface, etc. to find the best fitted geometry.

[picture]

Fig. 7. Dimensional deviation procedure performed on the characteristic casting dimensions

Fig. 7. Dimensional deviation procedure performed on the characteristic casting dimensions

Fig. 7b shows the average dimensional deviation for both H and V casting orientations. The overall casting dimensions are very close to the target CAD dimensions. The average dimensional deviation does not exceed +0.41 mm and -0.21 mm for the C and F dimensions, respectively. Fig. 7b also shows
that the deviation is mainly positive for the external casting surface, with an average value of 0.325 mm (i.e., twice the grain sand diameter), and negative for the inner surface, with an average value of -0.143 mm (i.e., the grain sand diameter). The positive deviation in the external cast dimensions can be explained by the negative surface deviation of the printed mold parts. However, its negative deviations are mainly attributed to the mold constraint on casting shrinkage during the cooling phase [20].

Conclusion

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This study demonstrates that rapid LPSC, combined with reverse engineering, 3D printing, and FE-based process optimization, can produce castings with high dimensional accuracy. Surface deviations in 3DPSM, primarily attributed to printing resolution determined by sand grain size, result in a negative surface deviation with an average value of -0.311 mm compared to the original CAD model. A simplification method was used to evaluate the dimensional accuracy of rapid LPSC castings by comparing six characteristic dimensions from 3D scanned parts to the original CAD model. Various fitting algorithms, including Gaussian, Chebyshev, and point-median methods, were used to determine the best-fit geometry. The results show that overall casting dimensions closely match the CAD model, with average deviations ranging from -0.21 mm to +0.41 mm . External surfaces exhibited slight positive deviations (approximately 0.325 mm ) while internal surfaces showed minor negative deviations (approximately -0.143 mm ) primarily caused by shrinkage constraints during cooling. The findings confirm that using the rapid LPSC process for manufacturing one-off replacement components can effectively minimize defects and ensure castings closely match the CAD design.

Furthermore, the proposed dimensional assessment methodology provides a practical means to evaluate the overall impact of the rapid LPSC process on part accuracy. Given the complex and varied sources of dimensional deviation, this approach can be extended to develop new mold design guidelines that consider not only literature-based expansion coefficients but also the cumulative effects of all process steps, thereby improving dimensional accuracy.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.75.pdf

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The original version of this eBook is available on https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 85-94

doi: 10.4028/p-xelC2G

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

Submitted:
2026-01-20

Revised:
2026-02-21

Accepted:
2026-02-22

Online:
2026-04-20

Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

The original version of this paper is available on https://www.scientific.net/MSF.1188.85.pdf

Evangelos Pischinas 1,a, Emmanouil Stathatos 1, b and George-Christopher Vosniakos 1,c*

¹National Technical University of Athens, School of Mechanical Engineering, Manufacturing

Technology Laboratory, Heroon Polytehniou 9, 15773 Athens, Greece

a  vaggelispischinas@mail.ntua.gr, b  mstatha@mail.ntua.gr c  vosniak@central.ntua.gr

Keywords: metal casting; neural networks; process simulation; digital twins.

Abstract

Dedicated simulation software is used to create a dataset which for a particular sand-cast part and various casting parameter values provide casting quality metrics based on temperature and solidification evolution. Based on simulation data, ANNs are trained to predict the successful or failed filling of the mold (a classification problem), as well as the quality of the part through solidification time, maximum microporosity, maximum von Mises residual stress, maximum displacement of any point in the casting and total volumetric shrinkage (a regression problem). Such ANNs can provide augmented information much faster than the simulation model to the process planner. A third category of ANNs (of the regression type, too) determine the temperature evolution with time at an inaccessible point, where no thermocouple can be placed, from the measurement history at two other thermocouples close to it. This data comes from real-time monitoring during casting. Such ANNs can aid the process supervisor in a 'digital shadow' context. The issues associated with generalizing these predictors to become independent of specific part geometry are also discussed.

Introduction

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Selection of appropriate process parameters in metal casting remains a demanding task, owing to the strong interdependence between molten metal flow, heat transfer, solidification, and defect formation. Numerical simulation of casting processes has been widely adopted as a "virtual casting" tool, enabling the prediction of filling behaviour, temperature evolution, shrinkage, porosity, and residual stresses prior to production. Nevertheless, credible simulation results are contingent upon careful problem formulation, accurate input parameters, validation against experimental results and extensive computational effort [1].

In recent years, the concepts of Digital Twin (DT) [2] and Digital Shadow (DS) [3] have been introduced as an evolution of simulation-based approaches, aiming at the continuous replication of the physical process through the integration of process data, sensor measurements, and computational models. In metal casting, as in most manufacturing processes, DTs promise enhanced process planning, monitoring, and decision support. However, their deployment is hindered by limitations in data availability, uncertainties in model validation, and the computational burden associated with high-fidelity simulations, which restrict their use in real-time or iterative planning contexts [4].

An alternative is offered by Artificial Neural Networks (ANNs). When trained on simulation or experimental datasets, ANNs can provide rapid predictions of critical process outcomes, effectively acting as surrogate models [5]. Such models are particularly attractive in a DS framework, where fast, augmented information is required to support the process planner or supervisor without replacing the underlying physical process.

The present work investigates the use of ANNs for the creation of DTs and DSs in sand casting. Emphasis is placed on predicting mold filling completion, key quality indicators, and internal temperature evolution, with the objective of complementing conventional simulation tools and enabling faster, data-driven support for casting process planning and monitoring.

Literature Review

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ANNs become central in DT/DS when they serve as fast surrogates for physics-based simulation or as soft sensors that infer unmeasured states. Recent casting-focused work demonstrates deeplearning surrogates for thermal fields. Kang et al. [6] propose deep learning to emulate heat-transfer simulation for casting, while Zhao et al. [7] predict spatiotemporal temperature fields during solidification using a physics-augmented learning approach. In continuous casting, Lu et al. [8] report real-time prediction of 3D temperature fields, enabling adaptive adjustment at millisecond scales. These studies support a DT pattern in which the NN surrogate replaces the slow solver during planning (design-of-experiments, parameter sweeps) and during operations (real-time forecasting, what-if control actions), while periodic synchronization with the physical process corrects drift.

Planning and monitoring also rely on quality prediction and defect detection. For X-ray based inspection of aluminum castings, CNN approaches have been demonstrated for defect recognition and classification [9,10] and for improved defect detection using attention-guided augmentation [11]. Such models can be embedded into a DS layer to provide rapid, structured quality signals (defect likelihood, type, location), which then feed back into DT analytics for root-cause exploration (e.g., linking porosity to melt treatment, venting, or thermal gradients) and for planning mitigations (parameter constraints, design changes). Process-condition ML can also predict quality outcomes from sensor streams, as shown for defect prediction in casting-related contexts such as slag inclusion prediction using condition data [12]. A "smart" DT for stabilizing return sand temperature illustrates closed-loop recommendations in foundry sand systems [13]. At a production-system level, a datadriven DT for foundry operations integrates operational models with predictive components to evaluate best-practice scenarios [14].

In summary, several directions are traceable in the research literature so far: (a) standardization and structured exploitation of casting data and real-time process context within a DS / DT layer to enable downstream analytics and decision support [15,16] (b) training neural surrogates and soft sensors for important hidden states (e.g., thermal history, solidification-related metrics) and quality outcomes [17,18] (c) coupling these data-driven models with physics constraints and selective highfidelity simulation to improve extrapolation beyond the training set and to enable computationally efficient parameter-space exploration [18] (d) and embedding the resulting DT capabilities into planning and monitoring, ranging from design and parameter optimization to monitoring and adaptive recommendations [19-21].

The part and mold

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The part studied is a pulley of outer diameter 96 mm , hole diameter 25 mm , thickness 14 mm and hub length 30 mm , see Fig. 1(a). The part is made of aluminum alloy EN AC-44000, containing 1011.8%Si, with liquidus temperature 590∘C solidus temperature 560∘C and recommended casting temperature for gravity cating 640−740∘C [21].

[picture]

Fig. 1 (a) The part (b) The mold showing cavity and open riser (green), cores (beige), feeding system (red) (c) Feeding system detail.

Fig. 1. (a) The part (b) The mold showing cavity and open riser (green), cores (beige), feeding system (red) (c) Feeding system detail.

The mold is made of green sand consisting of 85% silica sand, 4% water and 11% clay and bentonite. It mold includes two sand cores, corresponding to the pulley rim slot and the central hole, an open riser and the feeding system, see Fig. 1(b). The feeding system consists of the pouring basin,
the sprue and the ingate, see Fig. 1(c). The open riser is of conical shape with bottom/top diameter 10/18 mm respectively and height 50 mm . All feeding and risering system calculations were made according to [22].

Simulation-based ANNs

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Casting simulation. Casting simulation was performed using Altair Inspire Cast TM. Following a mesh sensitivity analysis, the mesh size was set to 2.377 mm achieving both accuracy and speed. All three casting phases were modelled, i.e. filling, solidification and demolding. The interfacial heat transfer coefficient between the mold and the aluminum melt was set to the default recommended value of the simulation software used, i.e. 1500 W/m2· K. Thermomechanical simulation was enabled.

Three process parameters were investigated, namely: melt temperature, mold/core temperature (i.e. investigating also preheating as implemented through immersion of the mold in a furnace), inlet melt speed, see Table 1. These values were compatible with the recommendations of the simulation software. In addition, the gate area was varied as a key parameter pertaining to the mold design and determining volumetric flow rate when multiplied by ingate speed, see Table 1.

Table 1. Process parameters and values investigated.

Process parameter | Value 1 | Value 2 | Value 3 | Value 4 | Value 5 | Value 6
Melt temperature (°C) | 670 | 690 | 720 | 740 |  |
Mold/core temperature (°C) | 20 | 30 | 100 | 200 | 300 | 400
Inlet melt speed (m/sec) | 0.63 | 0.79 | 0.99 |  |  |
Gate area (mm²) | 12.57 | 28.27 | 50.27 | 78.54 |  |

Simulation outputs included temperature evolution at indicated points, solidification time for the whole part, microporosity (% volume), maximum displacement (for any point compared to its initial position), shrinkage (% volume comprising both internal microporosity and surface or 'pipe' shrinkage) and minimum and maximum normal and shear stresses ( 12 values altogether) as well as the maximum von Mises stress at demolding at room temperature. Fig. 2 presents indicative results.

[picture]

Fig. 2 Indicative simulation results for melt temperature: 670∘C, mold temperature: 200∘C, inlet melt speed: 0.79 m/sec, gate area: 28.27 mm2 :(a) Temperature (b) Microporosity (c) Shrinkage (d) Von Mises stress.

Fig. 2. Indicative simulation results for melt temperature: 670 ∘ C , mold temperature: 200 ∘ C , inlet melt speed: 0.79 m / s e c , gate area: 28.27 m m 2 :(a) Temperature (b) Microporosity (c) Shrinkage (d) Von Mises stress.

All possible combinations of the parameter values of Table 1 amount to ( 4 X 6 X 3 X 4= ) 288, but only 234 were successful, the rest corresponding to combinations that did not achieve complete filling of the mold. This was due mainly to early solidification of the melt at particular points owing e.g. to low flow rate in combination with low melt temperature etc. Yet, even unsuccessful cases were retained and added to the dataset.

ANN predicting filling completion. This binary classification ANN is a shallow one following the feedforward-backpropagation paradigm. The inputs of this model are exactly those of the simulation, i.e. melt temperature, mold temperature, ingate speed and gate area. The output is completeness of mold filling including the open riser, thus prediction of one of two possible states: successful or incomplete filling.
The activation function was selected to be 'softmax'. Given an initial output vector of the ANN z = (z1,z2), the softmax function produces a new output vector σ(z)=(σ1,σ2) where each component is defined as: σi(z)=ezi/∑j=12ezj,i=1,2. This function is suitable for classification problems transforming linear outputs to probabilities, the sum of which is equal to 1 , exponentiation emphasizing differences. The 'cross entropy' function was used to quantify training performance, expressing how well the probability distribution predicted by softmax matches the target distribution.
One hidden layer was explored in the architecture of this ANN, varying the number of its neurons from 1 to 10 and selecting the case giving the best results. To do so, 70% of the dataset was used for training, whilst 15% was used for validation and another 15% for testing of its generalization ability. Maximum number of training epochs was set to 10000 . Training was performed on Matlab TM.

The best architecture used 8 neurons in the hidden layer. Its training took only 100 epochs, see Fig. 3(a) achieving 100% prediction accuracy for all training, validation and testing cases, see Fig. 3(b).

[picture]

Fig. 3 Mold filling predictor ANN (a) evolution of training (b) confusion matrix results

Fig. 3. Mold filling predictor ANN (a) evolution of training (b) confusion matrix results

ANN predicting casting quality. Five ANNs were developed independently in this case. The inputs of all of them are exactly those of the previous one, whilst the outputs are solidification time, maximum microporosity, maximum displacement, maximum von Mises stress, and total shrinkage, respectively. Inputs and outputs are normalized in the [0,1] interval.
The activation function was selected to be 'logsig' f(x)=1/(1+e−x) outputting values in the range [0,1], whilst for the output layer the 'purelin' function f(x)=x was used to maintain the normalization range which is necessary for a regression problem.
For each predictor ANN one hidden layer was used for which 1 to 20 neurons were tried, and the best case was selected, see Table 2.

Table 2. Architecture and performance details of the casting quality predictor ANNs (HLN: hidden layer neurons, MSE: Means Squared Error, MAE: Mean Absolute Error, MRA: Mean Relative Error, R-train / R-valid / R-test: correlation coefficient of the training / validation / testing data subset.

Output | HLN | Epochs | MSE | MAE | MRE(%) | R-train | R-valid | R-test
Solidification time (sec) | 6 | 7 | 299 | 8.9 | 4.4 | 0.9977 | 0.9957 | 0.9977
Max displace-ment (mm) | 16 | 11 | 0.007 | 0.054 | 6.88 | 0.9803 | 0.9103 | 0.9077
Max von Mises stress (MPa) | 15 | 7 | 189.4 | 10.2 | 5.2 | 0.9547 | 0.9288 | 0.8811
Total shrinkage (mm³) | 17 | 12 | 22211 | 72.3 | 3.3 | 0.9935 | 0.8573 | 0.9965
Max micro-porosity (%) | 0 | 3 | 0.112 | 0.228 | 4.7 | 0.3549 | 0.5927 | 0.5372

The correlation coefficients shown in Table 2, broken down into training, validation and testing, are generally acceptable except for those of the max microporosity which are clearly problematic. Therefore, the corresponding ANN is not trustworthy. The training curves are shown in Fig. 4.

[picture]

Fig. 4 Training curves of predictor ANNs for (a) Solidification time (b) Max displacement(c) Max von Mises stress (d) Total shrinkage (e) Max microporosity

Fig. 4. Training curves of predictor ANNs for (a) Solidification time (b) Max displacement(c) Max von Mises stress (d) Total shrinkage (e) Max microporosity

Experiment-based ANNs

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Casting Experiment. The model including the part cavity, cores and feeding system, was 3D printed on PLA and was used to create the mold with manual action by an experienced technician, see Fig. 5(a). A physical experiment was conducted under analogous conditions as in the simulations: melt temperature: 720∘C, mold temperature: 30∘C, ingate melt speed: 0.89 m/sec and gate area: 50.27 mm2. Pouring lasted 4.87 sec .

[picture]

Fig. 5 (a) Mold preparation (b) Thermocouple positions (c) Casting.

Fig. 5. (a) Mold preparation (b) Thermocouple positions (c) Casting.

Real-time temperature data was acquired using three K-type thermocouples with a range -180 to 1350 ∘C embedded in the mold, at distances 2,5 , and 10 mm respectively from the wall of the cavity, see Fig. 5(b). There is inherent difficulty in measuring these distances with accuracy, which renders validation of the simulation challenging, especially in view of the sharp decrease of temperature of the sand mold with the distance from the cavity wall [23]. Thermocouples were connected to a Advantech TM USB 4761 A/D converter feeding signals to LABVIEW TM software on a laptop. The casting obtained is shown in Fig. 5(c). The thermocouple measurements are shown in Fig. 6.

[picture]

Fig. 6 Thermocouple measurements (sampling rate: 1/sec).

Fig. 6. Thermocouple measurements (sampling rate: 1/sec).

ANN predicting temperature evolution. This ANN predicts temperature at one of the three thermocouple points using the other two thermocouple measurements. This is a preamble to the capability to estimate the temperature of a point which is inaccessible, just based on the temperature of accessible points. Equally importantly, this is achieved in real time. All three combinations were tested, i.e. estimating temperature at A/B/C based on measurements of B,C/A,C/A,B respectively. The input to this ANN is the temperature curve corresponding to the last 10 measurements of both known thermocouples (20 input neurons altogether) and the output is the current reading of the third thermocouple (one output neuron). Therefore, the prediction is based not on the current temperature of the other two thermocouples but on their recent history.
This is clearly a regression problem for which normalization in the interval [0,1] applies. Activation function 'logsig' was used for the hidden layer and 'purelin' for the output layer, exactly as in the previous group of ANNs of regression nature.
For each predictor ANN one hidden layer was used for which 1 to 30 neurons were tried, and the best case was selected, see Table 3. All metrics indicate an excellent prediction ability of all three ANNs. The results for thermocouple B were particularly accurate, surpassing those of thermocouples A and C . This is attributed to the fact that thermocouple B is placed between thermocouples A and C , hence its measurements are naturally 'interpolated' between those of those of the surrounding thermocouples.

Table 3. Architecture / performance of temperature evolution predictor ANNs (legend as in Table 2).

Thermocouple | HLN | Epochs | MSE | MAE | MRE (%) | R-train | R-valid | R-test
C | 26 | 20 | 15.97 | 1.04 | 0.326 | 0.9981 | 0.9998 | 0.9997
A | 22 | 7 | 40.93 | 1.63 | 0.861 | 0.9985 | 0.9978 | 0.9997
B | 22 | 40 | 0.67 | 0.18 | 0.048 | 0.9999 | 0.9999 | 0.9999

The training curves are shown in Fig. 7.

[picture]

Fig. 7 Training curves of predictor ANNs for temperature evolution at thermocouples (a) A based on B and C (b) B based on A and C and (c) C based on A and B.

Fig. 7. Training curves of predictor ANNs for temperature evolution at thermocouples (a) A based on B and C (b) B based on A and C and (c) C based on A and B.

The predicted temperature evolution curves are also presented in comparison to the corresponding actual recorded curves from the experiment, see Fig. 8. Any differences are restricted to the very first points where the transient nature of heat transfer is more pronounced. Some differences are also spotted at sample No 450 , where thermocouple C exhibits a sudden jump that is possibly due to hardware inconsistency, local collapse of the cavity wall or other anomaly, see Fig. 6. In fact, this jump is filtered out in Fig. 8(c) since the input based on which this is computed do not exhibit such a jump; this is a desired behavior of the ANN, given that only smooth temperature evolution curves are naturally expected in sand casting. However, when predicting temperature evolution at point B , for which thermocouple C provides an input, a spike is predicted, without being present in reality. Yet, this is not the case when predicting temperature evolution at point A despite thermocouple C providing input there, too.
Each diagram in Fig. 8 also depicts the corresponding temperature evolution curves resulting from the simulation, using input data identical to the experiment for comparison purposes. Although the simulation curves follow the general shape of the experimental data, they underestimate the latter by a significant offset of 100 to 150∘C. This is expected to some extent, as no attempt was made to calibrate the simulation by adjusting the heat transfer coefficient. This is commonly done given (a) its strong dependency on various casting parameters, of which predominant is the geometry of the casting [24], and (b) its variation during solidification following alternative functions reported in research literature, rather than being fixed as in the simulation software's recommended default. However, the primary source of deviation is the inability to determine the exact location of thermocouples once the drag and cope have been assembled. This likely results in comparing simulation curves with experimental curves corresponding to displaced locations, i.e. not the true counterparts in the mold.

[picture]

Fig. 8 Predicted vs measured temperature evolution at thermocouple positions (a) A, (b) B (c) C.

Fig. 8. Predicted vs measured temperature evolution at thermocouple positions (a) A, (b) B (c) C.

Discussion

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Using simulation-trained neural networks without direct experimental validation. Training neural networks on simulation data, even in the absence of direct experimental validation, can be justified by several practical and methodological considerations. High-fidelity casting simulations embed decades of validated physical modelling, including heat transfer, phase change, and fluid flow,
and are already trusted for process design and optimization. Neural networks trained on such simulations can therefore be viewed as surrogate models that inherit this embedded physics indirectly. Simulation-trained models can therefore serve as decision-support tools rather than absolute predictors, guiding process adjustments and narrowing the experimental search space even before direct validation is feasible.

Generalization beyond the specific part geometry. The ANNs developed in this work are clearly part-specific. To make such networks useful for predicting properties of entirely different cast parts, new inputs must be added. Geometry-dependent descriptors-such as local section thickness, distance from feeders, surface-to-volume ratio, or local cooling conditions-would need to be incorporated. Alternatively, mesh- or field-based learning approaches [25] could be employed that predict spatially and temporally distributed physical fields, such as temperature or stress, rather than global scalar quantities, thereby enabling geometry-aware generalization and soft sensing. For instance, Convolutional Neural Networks, Graph Neural Networks, Neural operators (Fourier, DeepONet, etc.) and Physics-informed neural networks (PINNs) are pertinent examples. Another promising direction is transfer learning, where an ANN trained on one part serves as a starting point and is subsequently fine-tuned using a limited number of simulations or experiments for a new geometry, e.g. [26]. The present study therefore represents a foundational step toward more general digital twin architectures rather than a fully geometry-agnostic solution.

Number of thermocouples required for temperature field inference. The experiment-based ANN results provide insight into the minimum sensing requirements for inferring temperature evolution at unmeasured locations. Thus, thermocouples should be placed strategically to capture the dominant thermal gradients and time constants within the mold. In practice, a small number of sensors, on the order of two to three per critical region, may suffice to enable soft-sensor functionality, provided that their locations are chosen based on simulation-guided sensitivity analysis. The implication for industrial practice is significant: reliable temperature field estimation may be achievable without excessive instrumentation, which is impractical in sand casting production environments.

Integration of simulation-trained neural networks into a digital twin. ANNs trained on simulation data can play a central role in a casting digital twin, acting as fast predictive engines embedded within a real-time monitoring framework. In such a system, sensor data from the actual process (e.g., thermocouples, flow sensors, infrared measurements) continuously update the state of the digital twin. The ANN can be used to:

Predict unmeasured variables (e.g. internal temperature fields or solidification progress),

Forecast downstream quality indicators (e.g. hot tearing risk or porosity formation),

Perform rapid "what-if" analyses in response to detected deviations.

Mitigation actions when a digital twin predicts an out-of-spec part. If a digital twin predicts that a cast part is likely to be out-of-spec, several corrective actions may be possible, depending on the process stage and defect type. During casting or early solidification, active process interventions may include adjusting cooling intensity, modifying mold insulation, or altering secondary cooling conditions. If the issue is detected later, downstream mitigation strategies such as controlled heat treatment, localized rework, or selective machining allowances may still salvage the part. In some cases, the prediction may inform process learning rather than immediate correction, allowing future cycles to be adjusted while accepting the current part as scrap. Very importantly, early and reliable prediction shifts quality control from a reactive to a preventive paradigm, reducing waste and enabling data-driven continuous improvement.

Conclusions and Future Work

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The mold-filling classification ANN achieved perfect prediction accuracy. Thus, it can provide immediate feedback during process planning, identifying infeasible casting process parameter combinations.

The regression-based ANNs predicting casting quality metrics showed mixed results. Solidification time and total shrinkage were predicted with high correlation and low relative errors. Predictions of maximum displacement and von Mises stress were also acceptable, though mild overfitting was observed. In contrast, the ANN trained to predict maximum microporosity performed poorly, indicating that additional inputs related to feeding or thermal gradients may be necessary.
The experiment-based ANN predicting temperature evolution represents an interesting contribution toward real-time digital shadow functionality. Using only recent temperature histories from two thermocouples, the models accurately reconstructed the temperature at a third location with very high accuracy.
Notably, temperature evolution curves obtained from the experiment and used for ANN training were substantially offset from their simulation counterparts, due to uncertainties in thermocouple placement as well as uncalibrated heat transfer coefficients used in the simulation.
The work performed confirmed that ANNs should not be viewed as replacements for physics-based models or experiments, but as complementary tools. Their true value lies in accelerating prediction, enhancing observability, and enabling proactive control in complex, data-scarce manufacturing environments. ANNs can complement casting simulation tools in the context of digital twins and digital shadows for sand casting, provided that their scope and limitations are clearly understood. However, their validity remains tightly coupled to the part geometry, parameter ranges, and data sources used for training.
Future work should focus on improving model generalization beyond the specific part and mold geometry investigated, enabling ANN predictors to be transferable across different casting designs and alloys. This may be achieved through larger, more diverse training datasets, the integration of physics-informed or hybrid models that embed fundamental heat transfer and solidification constraints or, alternatively, transfer learning. An additional interesting direction would be the merging of simulation and experimental data in datasets.

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The original version of this paper is available on https://www.scientific.net/MSF.1188.85.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 95-105

doi: 10.4028/p-tH3TT1

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

Submitted:
2026-02-02

Revised:
2026-02-17

Accepted:
2026-02-22

Online:
2026-04-20

Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

Tharmalingam Sivarupan 1,a*, Arul Mozhi Varman Jayaraman Palanivel 1, b, Konstantinos Georgarakis 1,c, John Forde 2, d, Ben Shaw 3,e, Konstantinos Salonitis 1,f, Mark Jolly 1, g

1 Sustainable Manufacturing Systems Centre, Faculty of Engineering and Applied Sciences, Cranfield University, United Kingdom,

2 JF Advanced Technology Solutions Ltd., Warwickshire, United Kingdom,

3 Sylatech Ltd., York, United Kingdom

Corresponding Author: a  siva.sivarupan@cranfield.ac.uk, b  a.jayaramanpalanivel@cranfield.ac.uk,

c  k.georgarakis@cranfield.ac.uk, djohn.forde@jfatechsolutions.com, èbenshaw@sylatech.com, f  k.salonitis@cranfield.ac.uk, g  m.r.jolly@cranfield.ac.uk

Keywords: Aluminium alloys, impurity elements, baffle, sedimentation, simulation, sustainability, aerospace castings, HPC.

Abstract

Cleaner melt transfer is critical to the broader use of recycled aluminium alloys in highend structural casting applications, where oxide bifilms and intermetallic inclusions, such as Fecontaining intermetallics, can significantly affect the casting's mechanical properties. In countergravity low- and high-pressure casting, the launder system must not only promote the sedimentation of inclusions but also deliver a stable, cleaner melt to the crucible. Prior research showed that 15∘ double baffles in the mid-section of the sedimentation launder at a flow rate of 100 kg· h−1 provide high efficiency. The present work investigates the influence of baffle design at the launder-crucible interface, where the melt enters the crucible before casting. Fluid dynamic simulations were carried out at a 100 kg· h−1 flow rate for three inlet configurations: (i) full baffle; (ii) lifted baffle; and (iii) split baffle. Inclusions of various densities and diameters were tracked. Results indicate that the full baffle, while beneficial as a benchmark and efficient, is impractical because it generates fresh oxide surfaces. The lifted baffle provided the most effective reduction in inclusions, like the full baffle setup, enhancing sedimentation and suppressing entrainment, while the split baffle showed intermediate behaviour. Moreover, the lifted configuration promoted centrifugal flow (at lower velocities, it still made a partial contribution) within the crucible, directing inclusions towards the crucible wall and the stagnation-velocity zone, and enabling the crucible itself to act as a final sedimentation stage before the counter-gravity pump extracts the melt. These results demonstrate that combining mid-launder optimisation with crucible inlet baffle design enables cleaner, more automated melt delivery, thereby strengthening the use of recycled aluminium alloys in structural casting applications.

Introduction

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The adoption of secondary (recycled) aluminium alloys for structural and high-integrity cast components, such as those utilised in aerospace applications, is an environmentally friendlier option. A key barrier is melt cleanness; oxide bifilms, often referred to as double-oxide "bifilms", and intermetallic inclusions introduced during melting and transfer can severely reduce fatigue life and increase tolerance levels in mechanical properties. Secondary contamination occurs when melt-flow disruptions expose fresh metal surfaces to air, leading to the formation of an oxide bifilm and reentrainment. This underscores the importance of maintaining stable flow and designing baffles appropriately for the casting melt delivery system. Campbell's bifilm framework links entrained surface oxides to crack-like internal defects that govern fracture and fatigue performance of the casting[1]. Quantification approaches, such as the 'bifilm index' proposed by Dispinar and Campbell, have provided practical melt-quality metrics that correlate with mechanical properties [2].

Experimental studies of bifilm behaviour in Al−Si−Mg alloys further confirm that bifilms form during turbulent handling and are the dominant defect-controlling factor [3]. Therefore, delivering a cleaner melt to the mould cavity for structural casting is a prime choice.

Flow-conditioning devices, such as baffles, dams, and weirs, in hydraulic systems are widely used in metallurgical and civil engineering applications to tailor the residence time distribution (RTD), suppress turbulence, and create low- or stagnation-velocity zones favourable for inclusion filtration. Although a substantial portion of this literature arises from tundish metallurgy (steelmaking), the governing fluid mechanics of particle transport, RTD control, and turbulence suppression provide transferable design principles. For example, Zhang et al. demonstrated that flow control devices can significantly alter the tundish flow structure and inclusion-filtration performance [4]. More recent numerical optimisation studies show that combinations of baffles and turbulence inhibitors can increase residence time and improve inclusion-filtration efficiency across a range of particle sizes [5]. He et al., report that hydrodynamic stability improves when baffles and inhibitors are arranged to reduce short-circuiting and stagnation zones [6].

Industrial melt-cleaning routes typically combine degassing, fluxing, and filtration. However, passive separation techniques based on residence time and controlled hydrodynamics are attractive for continuous melt delivery because they can reduce reliance on consumables and avoid secondary contamination [7]. Sedimentation-based launders (or 'settling' launders) are intended to control fluid velocity and provide prolonged residence time to promote inclusion settling (for phases denser than the melt) or flotation (for phases less dense than the melt). The effectiveness of this approach depends strongly on the local flow field, particularly near interfaces and transition zones where recirculation, jets, or surface disturbances can cause re-entrainment. In civil engineering applications [8], at room temperature, the design of baffles or exposure of the fluid to the atmosphere may have a positive effect, as nitrogen gas adsorption can help agglomerate inclusions and is used in water treatment applications; whereas in metallurgical applications, at elevated temperature, the melt surface can react with atmospheric constituents and re-entrain, e.g., metal oxides, that is a loss of metal. Therefore, the baffle design has limited options for promoting melt flow below the surface.

In counter-gravity casting processes, the launder's role extends beyond inclusion filtration. It must deliver a stable, clean melt to the crucible, from which the alloy is continuously extracted. Flow disturbances or poor interface design at the launder-crucible boundary can re-entrain settled inclusions or generate new oxide films. Although prior studies have shown that inclined double baffles [9], within the sedimentation launder, significantly enhancing inclusion filtration, the influence of baffle geometry at the crucible inlet has received limited or no attention.

Experimental investigation of inclusion trajectories in liquid metals is often challenging, costly and environmentally unsustainable; therefore, validation methods, e.g, positron emission particle tracking (PEPT), which was developed at Birmingham University, are valuable for comparing experimental and simulation results. Griffiths et al. [10] demonstrated the use of PEPT to track entrained inclusions in shape-casting environments, thereby providing a pathway to validate inclusion-transport models. Burnard's thesis [11] further developed PEPT-based analysis and compared particle pathways with simulations in industrially relevant launder flows that included a baffle in the melt path. Such studies reinforce the importance of reliably capturing flow features that control particle residence and sedimentation.

In parallel, controlled filling routes, such as counter-gravity casting, have been promoted to reduce turbulence and improve melt cleanness by enabling more quiescent mould filling and by avoiding/minimising the formation of new oxide surfaces. The CRIMSON (Constrained Rapid Induction Melting Single Shot Up-Casting) concept [12], for instance, has been assessed for energy and environmental benefits and emphasises controlled, computer-guided counter-gravity filling.

Counter-gravity methods can reduce oxide entrainment at the mould, but overall cleanness still depends on stable melt transfer into the crucible and effective inclusion management before pumping/pouring.

Within Ultra Clean Cast Digital Liquid Metal Manufacturing project (UltraCleanCAST DLMM), the authors' prior research [9,13] demonstrated that inclined double baffles in the mid-section of a sedimentation launder can significantly improve inclusion retention at low flow rates, identifying 15∘ double baffles at a melt flow rate of 100 kg· h−1 as a strong baseline configuration. However, the launder-crucible interface remains a critical region where flow disturbances may re-entrain settled inclusions or generate new oxide surfaces. This motivates the present study, which evaluates alternative baffle configurations at the launder-crucible interface, analogous to overflow-, undershot, and slotted-weir concepts, to control crucible inlet hydrodynamics and promote final-stage sedimentation before counter-gravity extraction. This necessitates designing and assessing alternative baffle configurations at the launder-crucible interface, using high-fidelity numerical simulations (a finite-volume method) to quantify their influence on flow behaviour, inclusion transport, and sedimentation performance. The simulation is considered high-fidelity due to (not limited to) full 3D transient finite-volume modelling, Lagrangian tracking of inclusions with density and size variation, long physical simulation time ( ~22600 s ), inclusion force modelling (gravity, buoyancy, centrifugal), realistic boundary conditions and flow rate, and particle counting using flux surfaces. Hence, a binary baffle setup with a double baffle at the centre of the sediment tank and the other at the launder-crucible interface has been evaluated.

Numerical Methodology

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Geometry, System Description, and Simulation

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The simulated system consists of a sedimentation launder connected to a crucible in a counter-gravity casting setup. The mid-section of the launder incorporates previously optimised 15∘ double baffles (Figure 1), while the interface region features interchangeable baffle designs (Figure 2) evaluated in this study. The alloy melt, at 650∘C, flows from left to right toward the crucible, which then pumps the melt at the same rate as the inflow, 0.028 kg· s−1(~100 kg· h−1). At the inlet region and just before the primary baffles, two particle generators generate inclusions of various densities ( 3348 kg· m−3 and 3990 kg· m−3 ) and diameters ( 25μ m,50μ m,100μ m, and 1000μ m ) at a rate of 30 s−1 in total and in equal portions at random. The inclusions discussed here represent the density of, e.g., Ti-/Fecontaining intermetallic inclusions (predendritic Fe-intermetallics or Ti-containing intermetallics formed due to the master alloy 1 addition) with a density of around 3348 kg· m−3 and alumina (bulk, but the density of aluminium oxide bifilm should be less than this density due to its porous nature) with a density of 3990 kg· m−3. These inclusions flow with the melt and encounter the baffles at the middle of the launder, the primary baffles at 15∘ angle from the vertical in the anticlockwise direction, and then the vertical baffle at the launder-crucible interface, the secondary baffle, Figure 2. The baffle setup was designed to influence the flow patterns of the melt and these inclusions; hence, the inclusions could be sedimented in the middle of the launder at the sediment tank or within the crucible and not escape the system, thereby keeping the melt supplied by the system cleaner for countergravity casting.

Although the geometry is symmetric, a full geometry simulation was necessary, despite its computational intensity, because inclusion transport is inherently asymmetric due to stochastic particle motion, recirculation, and secondary flow structures that break symmetry. Centrifugal effects within the crucible also require full-domain resolution; using symmetry would artificially constrain particle dynamics and could misrepresent inclusion trajectories.

[picture]

Fig. 1. The launder-crucible assembly with interchangeable baffle configurations at the interface. Due to proprietary design, only a limited side view is shown. Melt flows from left to right at 1AlTiB. Inclusions of various sizes and densities form at the indicated position. The primary double baffles, set at Al3Ti to vertical and inclined against flow, are in the sediment tank, while secondary baffles are at the launder-crucible interface, as in Figure 2.

Fig. 1. The launder-crucible assembly with interchangeable baffle configurations at the interface. Due to proprietary design, only a limited side view is shown. Melt flows from left to right at 1 A l T i B . Inclusions of various sizes and densities form at the indicated position. The primary double baffles, set at A l 3 T i to vertical and inclined against flow, are in the sediment tank, while secondary baffles are at the launder-crucible interface, as in Figure 2.

[picture]

Fig. 2. Schematic illustration of the type baffles at the launder-crucible interface and their dimensions.

Fig. 2. Schematic illustration of the type baffles at the launder-crucible interface and their dimensions.

The influence of the baffle's setup on the melt flow behaviour and the sedimentation efficiency of inclusions is compared. Three secondary baffle configurations at the Launder-crucible interface were examined, Figure 2:

Full-baffle (Overflow-type): A full-height baffle that forces the melt to flow over the top edge.

Lifted-baffle (or Undershot-type) baffle: a raised baffle that allows melt to flow beneath through a defined clearance.

Split-baffle (or Slotted-): two partial-height baffles separated by a central slot.

Simulations were performed using a commercial computational fluid dynamics solver with Lagrangian particle tracking (Flow3D, Flow Science USA) at the Delta 2 high-performance computing facility at Cranfield University. Flow3D uses a finite-volume method for its simulations. The key parameters are summarised in Table 1. The results are then compared to determine which inclusions pass through the baffles or exit the system. Two particle counters were also added to the simulation setup: one at the launder-crucible interface and the other just before the melt enters the counter-gravity casting pump. Counting inclusions and comparing them will provide the sedimentation efficiency for each particle type, e.g., density and diameter. This way, it is possible to
determine the baffle setup that results in cleaner melt exiting the system, following some of Campbell's [14] rules for good casting: a cleaner melt at a streamline flow to fill the mould cavity. That is, minimal entrained oxide bifilm or any other inclusion in the melt, and ensuring that none (optimal) form during the mould filling process, for better casting in structural high-end applications using secondary-sourced aluminium alloys. The Reynolds number, Table 1, indicates non-turbulent flow up to 4000 (a reasonable approximation accepted by the fluid dynamics research community) or 2300 for laminar flow. In this case, the value is approximately 160, which is significantly below the threshold required for stable streamline flow in the launder system at a flow rate of 100 kg· h−1 and a minimum cross-section of 15∘; the fluid velocity should be approximately 100 kg· h−1 at this mass flow rate through the smallest cross-section of the launder. The Reynolds number, even at a velocity of 100×100 mm2, would be around 800 . This suggests that the flow could be increased to approximately 1 mm· s−1 before turbulence occurs (a safe estimate).

Table 1. Summary of simulation parameters and operating conditions.

Description | Values
Alloy | aluminium-silicon alloy (A356 alloy). The simulation was carried out for liquid flow; therefore, there is no significant effect from minor chemical variations in the secondary-sourced alloy.
Liquidus | 617.6 °C
Melt temperature | 650 °C
Flow rate | 0.028 kg·s⁻¹ (~100 kg·h⁻¹)
Inclusion density | 3348 kg·m⁻³, and 3990 kg·m⁻³
Inclusion diameter | 25 μm, 50 μm, 100 μm, and 1000 μm
Particle generation rate | 30 s⁻¹ (continuous/random)
External insulated wall temperature | 200 °C (measured/fixed boundary condition)
Approximate dimensions of the launder crucible setup (proprietary design) | 4×1×1 m³, (crucible 1×1×1 m³)
Mesh size | ~ 6 mm (average)
High-performance computing wall time | ~ 5 days per 3000 s simulation
Simulation time | 22600 s (physical time)
Reynold number, ρVL/μ (ρ-density, V-velocity, L-characteristic length of the cross section and is the side length of the square cross section, μ-dynamic viscosity of the fluid) | Around 160, at a melt flow rate of 100 kg·h⁻¹ through the smallest section of the launder, 100×100 mm² square cross section.

Results

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

[picture]

Fig. 3. Top view of inclusion distribution at 22600 s for (a) full-/ (b) lifted-/ (c) split- baffle, respectively. Due to the proprietary nature of the launder design, only a portion of the launder (hidden) view is presented. Only blue 5 mm· s−1 and brown-coloured 5000 kg· h−1 inclusions can be seen in the crucible. The larger blue-coloured (25μ m) and red-coloured (50μ m) inclusions have already sedimented in the sediment tank.

Fig. 3. Top view of inclusion distribution at 22600 s for (a) full-/ (b) lifted-/ (c) split- baffle, respectively. Due to the proprietary nature of the launder design, only a portion of the launder (hidden) view is presented. Only blue 5 m m · s − 1 and brown-coloured 5000 k g · h − 1 inclusions can be seen in the crucible. The larger blue-coloured ( 25 μ m ) and red-coloured ( 50 μ m ) inclusions have already sedimented in the sediment tank.

[picture]

Fig. 4. Side views of inclusion distribution at 22600 s for the (a) full-/ (b) lifted-/ (c) split- baffle configurations, respectively. Due to the proprietary nature of the launder design, only a portion of the launder (hidden) view is presented. Only blue (25) and brown (50) coloured inclusions can be seen in the crucible. The larger blue-coloured (100) and red-coloured (1000) inclusions have already sedimented in the sediment tank.

Fig. 4. Side views of inclusion distribution at 22600 s for the (a) full-/ (b) lifted-/ (c) split- baffle configurations, respectively. Due to the proprietary nature of the launder design, only a portion of the launder (hidden) view is presented. Only blue (25) and brown (50) coloured inclusions can be seen in the crucible. The larger blue-coloured (100) and red-coloured (1000) inclusions have already sedimented in the sediment tank.

A vast data set was collected from various simulations, and a detailed quantification study was considered 'not necessary'. However, a qualitative analysis of the time stage of the inclusions was deemed reasonably sufficient for comparison and was therefore evaluated and concluded accordingly. Although some quantification of the results is presented in Figure 5 to strengthen the conclusion. The simulation was run for approximately (100μ m), which was assumed sufficient for the saturation duration in a practical casting trial. At the end of the 22600 s time, the positions of the inclusions are visualised and compared in Figures 3 (Top view) and Figure 4 (side view) of the crucible side of the simulation setup. The inclusions were enlarged for comparison. The red-coloured inclusions were (1000μ m) in diameter, blue were 22600 s(~6.3 h), green were 1000μ m, and the 100μ m were brown in colour.

The full-baffle, Figure 3 (a) and Figure 4 (a), produces strong upstream deceleration and large recirculation zones. Denser and larger inclusions tend to stagnate upstream of the baffle, whereas smaller inclusions may pass into the crucible. The lifted-baffle configuration, Figure 3 (b) and 4 (b), promotes subsurface inflow and a less turbulent free surface. Inclusion trajectories follow smooth curved paths beneath the baffle, resulting in improved sedimentation and reduced re-entrainment. The split-baffle, Figure 3 (c) and Figure 4 (c), configuration produces a combined jet-recirculation flow. While flow uniformity is improved, a fraction of the minor inclusions bypasses the sedimentation region through the slot or the split section of the baffle. All baffle setups appear effective at controlling larger inclusions ( 50μ m ). Although the lifted-/full- baffles seem to control even smaller inclusions, a small portion of 25μ m inclusions is still visible in the crucible. When comparing the effectiveness
of these two baffle setups for sedimenting inclusions, the split baffle is less effective, as evidenced by the inclusions' positions later in the simulation and quantitative analysis, Figure 5. Quantifying the inclusion escape into the counter-gravity casting pump would be a future study, although it is not necessary to conclude that the lifted baffle was both technically and practically an effective solution for optimising the sedimentation of fine-scale inclusions as small as >50μ m in diameter. It is important to note that inclusion counting was performed only from 22000 s to 22900 s to compare the sedimentation efficiency of these baffles; it was not necessary to count from 0 s to 22900 s . This was considered a reasonably saturated time to minimise the computation time. A diverging plot does not necessarily mean that saturation was not achieved, as these plots were used for comparison only to show the total number of inclusions in each category at the end of the time period. The shape of the launder may have caused a pattern of inclusion passing through the flux surfaces. Based on the inclusion distribution in the crucible, this helps explain that the lifted baffle setup trapped finer inclusions more effectively than the split baffle.

[picture]

Fig. 5. The 25μ m diameter inclusions with densities of (left) 25μ m and (right) 25μ m passed through flux surfaces at (solid lines) the inlet and (dotted lines) outlet for the full-, lifted-, and split-baffle configurations. These inclusions moved through the inlet and outlet over time from 22000 s to 22900 s . The outlet flux surface captures more inclusions than the inlet because the suspended particles are in the launder. Note: the negative 3338 kg· m−3-axis shows inclusions exiting the flux surface, and the background indicates the baffle setup at the launder interface for clarity.

Fig. 5. The 25 μ m diameter inclusions with densities of (left) 25 μ m and (right) 25 μ m passed through flux surfaces at (solid lines) the inlet and (dotted lines) outlet for the full-, lifted-, and split-baffle configurations. These inclusions moved through the inlet and outlet over time from 22000 s to 22900 s . The outlet flux surface captures more inclusions than the inlet because the suspended particles are in the launder. Note: the negative 3338 k g · m − 3 -axis shows inclusions exiting the flux surface, and the background indicates the baffle setup at the launder interface for clarity.

Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

Comparative Sedimentation Performance

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

The three baffle configurations alter the local hydrodynamics at the launder-crucible interface in distinct ways. Full (overflow) baffles maximise upstream trapping but risk oxide generation, although they act as a baseline for comparison. Lifted (undershot) baffles provide a balanced combination of flow stability and sedimentation efficiency. Split (slotted) baffles offer moderate performance, with improved flow symmetry but reduced retention of 3990 kg· m−3 or y inclusions.

Centrifugal Flow Effects in the Crucible

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

The results indicated that inclusions of 25μ m and 50μ m mostly escaped, depending on the secondary baffle used. To explain the reasons and identify possible causes, it is vital to analyse the forces acting on these inclusions at a fundamental level. Each inclusion in the crucible experiences gravitational, buoyant, and centrifugal forces. Figure 6 shows the calculated minimum (brown line) and maximum (pink line) of the resultant vertical forces (the sum of the gravitational and buoyant forces, the horizontal lines). The plots (inclined lines) compare the centrifugal forces as a function of the
inclusion velocity for various diameters and densities. The inclusion diameter (as marked, 25μ m, 50μ m ) has a significant effect on the forces, while the density (red/blue/green lines) has a minor effect. The lifted (undershot) baffle configuration induces a mild tangential velocity component within the crucible, generating centrifugal forces that guide inclusions toward the crucible wall. While smaller than gravity or buoyancy (Figure 6), these forces are directionally important and enhance the residence time of inclusions near low- and stagnation-velocity wall regions. The gravitational (/buoyancy) force/s on these inclusions are significantly larger than the centrifugal force that acts on inclusions of this study. They assist in moving inclusions towards stagnation (melt; inclusions still move due to gravity/sedimentation) velocity zones; hence, gravitational force could influence the sedimentation of these inclusions. Figure 6 compares the centrifugal forces on these inclusions as a function of diameter and velocity, along with the gravitational forces. At a certain threshold velocity, e.g., 1000μ m, the 100μ m,50μ m,25μ m inclusions, the centrifugal force becomes comparable to gravitational and buoyant forces, and their influence is considered important. Because fine inclusions are difficult to sediment solely by gravity, the assistance of centrifugal forces brings them into the stagnation velocity zone; hence, gravity and prolonged residence time of these inclusions facilitate sedimentation. The critical velocity for which the centrifugal forces become comparable to the gravitational forces would be ~1 mm· s−1 for 100μ m inclusions, as well as 2.5 mm· s−1 for the finer 50μ m inclusions. A velocity of 5 mm· s−1 is not considered turbulent in casting conditions, which may not cause significant issues; however, it is only required in a portion of the crucible to generate the necessary centrifugal force to move the finer inclusions towards the stagnation zone, thereby optimising the sedimentation process. The effect of centrifugal force is therefore the reason the lifted baffle achieves higher efficiency in sedimenting inclusions. This brings the melt beneath the surface and circulates it to achieve a critical velocity that exceeds those of other baffle setups.

[picture]

Fig. 6. Calculated centrifugal force, F , using the fundamental equation (25μ m) (on a particle with mass, m , velocity, v , and the radius, r ), acting on inclusions of different sizes and densities as a function of melt velocity (with random values within the domain of simulated inclusion velocity, 5 mm· s−1, and F=mv2/r ). The colour codes are red for high-density, blue for medium-density, and green for low-density inclusions. The crucible background shows forces acting on a particle and their directions.

Fig. 6. Calculated centrifugal force, F , using the fundamental equation ( 25 μ m ) (on a particle with mass, m , velocity, v , and the radius, r ), acting on inclusions of different sizes and densities as a function of melt velocity (with random values within the domain of simulated inclusion velocity, 5 m m · s − 1 , and F = m v 2 / r ). The colour codes are red for high-density, blue for medium-density, and green for low-density inclusions. The crucible background shows forces acting on a particle and their directions.

Numerical and Computational Considerations

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

Long physical simulation times and large computational domains impose significant constraints even with a high-performance computer (HPC). The current setup represents a compromise among spatial resolution, simulation duration, and post-processing feasibility, as specified in Table 1. Although the mesh size 1,2.5 or 6 mm5 mm· s−1 considered in this study may slightly misplace the inclusion positions, this ultimately alters the predicted positions of these inclusions. The inclusions had diameters of 25, (6000μ m, and ). These are 50μ m,100μ m, and 6 times smaller than the mesh size; while the mesh size was comparable for the 1000μ m and 240,120,60 inclusions, it was not for the 100μ m and 1000μ m inclusions, which may significantly affect the position error for these inclusions. Balancing the mesh size comparable to the inclusion size is vital. In practice, it is a viable solution to find a Pareto front to determine a reasonable mesh size for computational and post-processing efficiency. Measurement and computational errors are inevitable; therefore, this Pareto front is a viable solution.

The Flow3D simulation employs a point-mass assumption for particles; therefore, inclusions are treated as non-interacting Lagrangian points, without inclusion-inclusion (particle-particle) collisions or agglomeration. Physical clustering is not possible in the present model. Any apparent grouping of inclusions observed in the visualisations is a visual illusion caused by the projection of inclusions travelling along similar flow paths and residence-time patterns. In addition, the particles were intentionally enlarged in the post-processing visualisation for clarity, which may further enhance the perception of clustering. This effect is purely graphical and does not represent physical agglomeration.

Practical Implications

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

The simulation assumes isothermal conditions (the measured external temperature of 25μ m ) only at the external boundary, because the launder is insulated and operates in a controlled casting environment with heating elements surrounding it (top surface and some near the sediment tank). The low Reynolds number (laminar flow) minimises viscous heating. In industrial launders, thermal gradients are usually small relative to hydrodynamic effects. Although thermal losses near the walls can slightly increase viscosity and affect local flow, their impact on inclusion distribution is secondary to factors such as residence time and gravity-driven sedimentation. From an industrial perspective, the lifted (undershot-type) baffle is the most feasible option. It minimises surface disturbance, limits oxide formation, and provides effective inclusion filtration, while remaining compatible with continuous counter-gravity casting systems. The split baffle may also pose a practical challenge during melt delivery, whereas the lifted baffle may be easier to place in the launder system and may not significantly cause oxide reformation issues. Baffle optimisation through different geometries (split/lifted) and positioning, control inclusion path lines and accumulation zones. The sedimentation performance trend was as follows:

Full baffle 50μ m Lifted baffle 200∘C Split baffle, for smaller and lighter inclusions
Split baffle still seems sufficient for denser ( ~ ) and larger ( > ) inclusions
In practical situations, adjustable baffle height and inclination can provide tunable melt-cleaning performance that adapts to varying inclusion types and melt flow rates. Inclusion/particle trapping at the low flow rate of 3990 kg· m−3> is significantly better for inclusions of various sizes and densities within the launder. The slower flow gives inclusions more residence time to settle or float. At a higher flow rate (e.g., from a previous study, 100μ m> ), smaller inclusions remain suspended longer and are more likely to escape, while larger, denser inclusions still settle but less uniformly. On the other hand, this low flow rate may be suitable for aerospace casting, whereas automotive casting requires a much higher flow rate to optimise production. In automotive production, the inclusion size tolerance is less stringent than in aerospace. The presence of primary/secondary baffles, or their angles, significantly enhances inclusion sedimentation. As
demonstrated in another study [9], inclined baffles at 0.028 kg· s−1(~100 kg· h−1) or 180 kg· h−1 improve the filtration of small 15∘ ) or low-density inclusions, whereas larger ( 30∘ ) and high-density inclusions settle completely within the launder. This study reveals that further refinement of these fine inclusions can be achieved by adding a secondary baffle at the launder-crucible interface, with an appropriate opening for melt flow. Therefore, a correlation between flow rate, launder/crucible/baffle designs and the particle properties should be identified. A unified solution may be impractical, or further research is required. However, suitable primary, secondary, and tertiary baffle designs can increase inclusion residence time to achieve effective sedimentation and provide a cleaner and smoother melt supply for casting structural components with high tolerances for physical and mechanical properties.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

Baffle design at the launder-crucible interface strongly influences inclusion transport and sedimentation efficiency.

The undershot baffle provides the best balance between melt stability and inclusion filtration.

Mild centrifugal flow within the crucible enhances final-stage sedimentation in the crucible.

Integrated optimisation of the launder and crucible setup with suitable baffles enables cleaner, more automated melt delivery for structural aluminium castings.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

The authors gratefully acknowledge the valuable and continuous support for the UltraCleanCAST DLMM project from Prof. John Campbell. The financial support from UK Research and Innovation through the UltraCleanCAST DLMM project is also gratefully acknowledged. Appreciation is extended to the Delta2 High Performance Computer team at Cranfield University and to Flow Science for providing a discounted academic research licence for Flow 3D products. Their collaboration and resources have been crucial to the progress of the UltracleanCAST DLMM project.

References

The original version of this paper is available on https://www.scientific.net/MSF.1188.95.pdf

Campbell J. The consolidation of metals: the origin of bifilms. Journal of Materials Science 2016;51. https://doi.org/10.1007/s10853-015-9399-9.

Dispinar D, Campbell J. Use of bifilm index as an assessment of liquid metal quality. International Journal of Cast Metals Research 2006; 19. 136404606225023300.

El-Sayed MA. The behaviour of bifilm defects in cast Al-7Si-Mg alloy. PLoS One 2016;11. .

Zhang L, Taniguchi S, Cai K. Fluid flow and inclusion removal in continuous casting tundish. Metallurgica and Materials Transactions B 2000;31:253-66. .

Fang Q, Zhang H, Luo R, Liu C, Wang Y, Ni H. Optimisation of flow, heat transfer and inclusion removal behaviours in an odd multistrand bloom casting tundish. Journal of Materials Research and Technology 2020;9. .

He Z, Zhou K, Liu S, Xiong W, Li B. Numerical modelling of the fluid flow in continuous casting tundish with different control devices. Abstract and Applied Analysis 2013;2013. .

Liu G, Ren Y, Ma W, Morita K, Lei Y, Zhan S, et al. Recent advances and future trends of aluminium alloy melt purification: A review. Journal of Materials Research and Technology 2024;28:4647-62. .

TU Delf Open Courseware. Sedimentation; WATER TREATMENT. n.d.

T Sivarupan et al. Baffle-Based Sedimentation Strategies for Inclusions in Launders of Aerospace Aluminium Casting: A Sustainable Approach. 2026 TMS Annual Meeting & Exhibition, 2026.

Griffiths WD, Beshay Y, Parker DJ, Fan X, Hausard M. The application of positron emission particle tracking (PEPT) to study the movement of inclusions in shape castings. Shape Casting: 3rd International Symposium 2009.

Burnard DJ. Positron Emission Particle tracking of inclusions in cast liquid metals. 2015.

Salonitis K, Jolly MR, Zeng B, Mehrabi H. Improvements in energy consumption and environmental impact by novel single-shot melting process for casting. Journal of Cleaner Production 2016;137:1532-42. .

Sivarupan T, Palanivel AMVJ, Georgarakis K, Forde J, Rawding J, Shaw B, et al. Sedimentation of Inclusions in Melts of Aerospace Structural Aluminium Alloy Castings: A Sustainable Approach. In: Edwards L, editor. Light Metals 2025, Cham: Springer Nature Switzerland; 2025, p. 1172-9.

Campbell J. Complete Casting Handbook: Metal Casting Processes, Metallurgy, Techniques and Design: Second Edition. 2015. .

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.

(~25μm master alloy is added to the melt to form >50μ m particles on a pre-existing alumina bifilm.

The original version of this eBook is available on https://www.scientific.net/book/advanced-metal-casting/978-3-0364-1982-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1188, pp 107-116

doi: 10.4028/p-u66D1M

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

Submitted:
2026-02-13

Revised:
2026-02-19

Accepted:
2026-02-22

Online:
2026-04-20

Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

Alessandro MORRI 1, a , Barbara REGGIANI 2,3, b , Rosario SQUATRITO 2,c Manel da Silva 4, d, Giulia ZANIBONI 2,e*, Lorenzo DONATI 1,f

1 Department of Industrial Engineering, University of Bologna, Viale Risorgimento 2, 40133

Bologna, Italy

2 Department of Science and Method for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy

3 INTERMECH, University of Modena and Reggio Emilia, Via Amendola 2, 42122, Italy

4 Eurecat, Centre Tecnològic de Catalunya, Unit of Metallic and Ceramic Materials, Av. Universitat Autònoma 23, 08290 Cerdanyola del Vallès, Barcelona, Spain

a alessandro.morri4@unibo.it, b barbara.reggiani@unimore.it, c rosario.squatrito@unimore.it, d  manel.dasilva@eurecat.org, e  giulia.zaniboni@unimore.it*, f  I.donati@unibo.it

Keywords: Microstructural prediction, High-pressure die casting (HPDC), step casting mould

Abstract

The increasing demand for lightweight and energy-efficient components has strengthened the use of high-pressure die casting (HPDC) for thin-walled aluminium parts, often produced from recycled alloys. However, HPDC components are still affected by microstructural heterogeneity and defect formation, such as shrinkage porosity and gas entrapment, which are closely related to melt flow and solidification conditions. In this study, a dedicated step-casting geometry was developed to reproduce, within a single casting, solidification conditions representative of industrial HPDC components with varying thickness. The design was supported by numerical simulations to control filling and thermal evolution. Experimental HPDC trials were performed under industrial conditions, followed by microstructural characterization in terms of porosity, Secondary Dendrite Arm Spacing (SDAS) and skin layer thickness. Comparison with a complex industrial demonstrator component confirmed that the step casting reliably reproduces both average microstructural features and their variability. The proposed numerical-experimental approach provides a robust framework for process HPDC design and optimization for lightweight cast components.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

The increasing industrial focus on energy efficiency and environmental sustainability has made the production of lightweight metal components a strategic priority across several application fields, particularly in the transportation and automotive sectors [1]. In this context, aluminium alloys, often derived from recycled feedstock, offer an attractive combination of low density, good mechanical performance and reduced environmental footprint.

High-pressure die casting (HPDC) is widely adopted for the manufacturing of lightweight aluminium components due to its capability to produce complex geometries with thin walls, high dimensional accuracy and short cycle times [2], [3], [4]. Despite its technological maturity and extensive industrial use, HPDC still poses significant challenges in terms of controlling microstructural evolution and defect formation during solidification [5]. The extreme process conditions characterizing HPDC, like high filling velocities and compacting pressures, rapid heat extraction and complex thermal gradients, make the final microstructure highly sensitive to local process variations.

Small changes in melt temperature, cooling rate or metal flow behaviour can significantly alter key microstructural features such as porosity distribution, dendritic morphology and skin layer development, ultimately affecting the mechanical strength, ductility and fatigue resistance of the final component [6], [7]. In particular, turbulence during cavity filling plays a critical role in promoting
gas entrapment and oxide formation, while insufficient feeding during solidification leads to shrinkage porosity, especially in thicker sections. These phenomena are often strongly coupled and difficult to mitigate simultaneously in industrial-scale components.

Numerical simulations have therefore become an essential tool for the analysis and optimization of HPDC processes. Modern simulation frameworks allow the investigation of mould filling dynamics, temperature evolution, cooling rates and solidification times prior to experimental trials, significantly reducing development time and costs. However, simulation alone is not sufficient to fully describe the complex thermo-fluid and metallurgical phenomena occurring during HPDC [8]. The predictive capability of numerical models strongly depends on reliable experimental validation, particularly with respect to microstructural features and defect formation mechanisms.

Industrial HPDC components are typically characterized by large thickness variations, local hot spots and intricate internal cavities, which make it difficult to isolate the specific process parameters governing microstructural evolution. As a result, there is growing interest in the development of controlled casting geometries that can reproduce, in a simplified and repeatable manner, the thermal and solidification conditions encountered in real components, while remaining sufficiently simple to allow systematic investigation.

Among the proposed solutions, step casting geometries represent an effective experimental approach, as they enable the generation of distinct solidification regimes within a single casting by varying wall thickness and local heat extraction conditions. When combined with validated numerical simulations, step castings provide a robust platform for studying the relationships between process parameters, microstructural features, such as Secondary Dendrite Arm Spacing (SDAS), porosity content and skin layer thickness, and the resulting mechanical performance [9].

In this work, a dedicated step casting was designed and optimized through numerical simulations with the objective of minimizing turbulence-induced defects and shrinkage porosity while reproducing microstructural conditions representative of industrial HPDC components. After the optimization, the casting was produced under controlled industrial HPDC conditions and extensively characterized from a microstructural standpoint. A direct comparison with a complex industrial demonstrator component was carried out to validate the representativeness of the step casting, establishing a solid foundation for future process-microstructure-property correlations and datadriven modelling strategies for lightweight aluminium HPDC components.

Step Casting Design and Optimization

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

The mould used for the experimental production of the components was created following an assessment using numerical simulations, which made it possible to identify the critical points of the geometry and optimize it.

The base mould (M0) of the step component was available at the Eurecat industrial facilities (Cerdanyola del Vallès, Barcelona). The step casting component obtained from the M0 had a stepshaped geometry thought reproduce, within a single HPDC component, a significant range of solidification and cooling conditions representative of industrial lightweight parts. The specimen consisted of a plate 170 mm in width and 180 mm in height, composed of six steps with decreasing thickness, ranging from 15 mm to 1 mm . Each step had a height of 29 mm , except for the thickest one, which was 23 mm . The ingate was dimensioned as 67 mm in width and 4 mm in height, for a total feeding area of 267 mm2, and positioned in correspondence with the thickest step to promote efficient feeding. Vents were placed near the thinnest sections to facilitate air evacuation during filling (Figure 1a).

For the numerical simulations, ProCAST 2025 was employed using a simplified representation of the die in order to reduce computational time. Since the primary objective of the simulations was the comparative evaluation of the step casting geometry, all analyses were performed using the aluminium alloy EN AC43500 selected from the ProCAST material database. As illustrated in the Figure 1b, the die casting system was simplified by explicitly modelling only the mould components directly in contact with the molten alloy of the component, namely the cavity insert and the gating system. In addition, the mould holder was included in the numerical model to account for the global
thermal inertia of the die assembly. This modelling strategy allows a significant reduction in computational cost while preserving the predictive capability of the simulation, particularly with respect to the thermal behaviour of the mould and its influence on filling dynamics, solidification and shrinkage porosity formation.

[picture]

Fig. 1. a) Base step casting geometry, b) solid model used to perform the simulation

Fig. 1. a) Base step casting geometry, b) solid model used to perform the simulation

The simulation parameters were set in accordance with previous experimental data obtained at Eurecat [10], ensuring consistency with industrial HPDC operating conditions. In particular, the most relevant parameters for the numerical modelling of the HPDC process in ProCAST included the thermophysical properties of the alloy (density, thermal conductivity, specific heat and latent heat of solidification), as provided by the EN AC-43500 material database.

To ensure a correct evaluation, the properties of the alloy were calculated using the ProCAST tool Computherm. Starting from the alloy's chemical composition, this module extracts the phase transformation temperatures, specific heat capacity, thermal conductivity, and other thermo-physical properties crucial for simulating the casting process and predicting the final material properties

Boundary conditions at the metal-die interface were defined through heat transfer coefficients (HTC), which were assigned as a function of local contact conditions and process stage, with representative values ranging between 4000 and 10000 W· m−2· K−1. The evolution of HTC during the cycle was used to account for the transition from initial metal-die contact to solidification and die opening. The injection conditions were defined by imposing the piston velocity profile at the inlet, including the slow-shot and fast-shot phases, with characteristic velocities of 0.4 m· s−1 and 4 m· s−1. The melt temperature at injection and the initial die temperature were set to 680∘C and 100∘C, respectively, in agreement with the monitored industrial process conditions.

Additional modelling parameters included the filling fraction of the shot sleeve ( 33% ), the metal fraction at the end of filling, and the criteria for shrinkage porosity prediction, based on local solidification time and feeding capability. This parameter set provided a physically consistent representation of the HPDC process and ensured reliable prediction of filling behaviour, thermal evolution and defect formation. Then, a total of 3,443,126 elements were used for the finite element mesh, consisting of hexahedral and tetrahedral elements, carefully refined in regions of high thermal gradients and complex geometry to ensure numerical accuracy. The time step for the transient simulation was set to vary over time from 0.05 to 0.1 s resulting in a total simulation duration of 81.7 s, sufficient to capture the complete filling, solidification, and ejection phases of the HPDC cycle. The analyses performed were filling and solidification, activating Thermal and Flow modules. Mesh convergence studies were performed to verify that further refinement did not significantly affect temperature, flow, or porosity predictions, thus avoiding numerical instabilities. The solver settings in ProCAST included 5 iterations per time step, with automatic stabilization routines activated to maintain convergence throughout the rapid filling phase. This setup allowed for a robust and computationally efficient simulation of the industrial HPDC process.

Concerning the achieved results, filling simulations revealed significant fluid recirculation, particularly in the thicker sections of the casting, as shown in Figure 2a. This recirculation promoted turbulent flow, increasing the risk of gas entrapment and of unwanted oxides and leading to inadequate feeding during solidification. Consistently, shrinkage porosity predictions, conducted by using the standard APM module, indicated a high concentration of defects in the thickest steps highlighting the need for geometric and process optimization (Figure 2b).

[picture]

Fig. 2. Defects of the original mould design a) temperature during filling, b) total shrinkage porosity

Fig. 2. Defects of the original mould design a) temperature during filling, b) total shrinkage porosity

To mitigate these issues, a multi-step redesign strategy was implemented. First, the lateral width of the steps was reduced from 170 mm to 120 mm to promote more uniform cooling and limit recirculation during filling (Figure 3a). Temperature field during the filling phase (Figure 3b) and the shrinkage porosity (Figure 3c) show a clear reduction compared to the original configuration, although some porosity remains localized in the thicker steps.

[picture]

Fig. 3. a) First mould redesign, b) Temperature and c) Shrinkage porosity

Fig. 3. a) First mould redesign, b) Temperature and c) Shrinkage porosity

This modification resulted in a clear reduction of predicted porosity, although localized shrinkage defects persisted in the thickest sections. Subsequently, further geometric refinements were introduced by increasing the ingate width to 77 mm and thickness to 6 mm to promote efficient feeding. Finally, the opposite section of the casting was chamfered with angles of 45∘C (Figure 4a). The simulations of this optimized configuration show an improved feeding of the thicker step during the filling phase (Figure 4b), and the total shrinkage porosity is further reduced and significantly lower than in the original design, though not completely eliminated (Figure 4c).

[picture]

Fig. 4. a) Second mould redesign, b) Temperature and c) Shrinkage porosity, d) final Step-shaped HPDC specimen

Fig. 4. a) Second mould redesign, b) Temperature and c) Shrinkage porosity, d) final Step-shaped HPDC specimen

Based on these results, this final optimized geometry was selected for casting, as it provides a good compromise between improved feeding, reduced solidification time in the thicker sections, and minimized shrinkage porosity (Figure 4d).

Step casting production

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

Once optimized the geometry, the step samples were casted using the aluminium alloy EN AC-43500, which nominal composition is reported in Table 1.

Table 1. Alloy composition

EN 1706:2020 / EN AC-43500 | Si | Fe | Mn | Mg | Zn | Ti | Cu | Cr | Sr | Al
9.0–11.5 | <0.25 | 0.4–0.8 | 0.10–0.6 | <0.07 | <0.20 | <0.05 | <0.05 | - | Bal

The step casting production was performed at the Eurecat industrial facilities (Cerdanyola del Vallès, Barcelona) using a Bühler cold chamber HPDC machine with a maximum locking force of 5250 kN , a plunger with a diameter of 60 mm and a stroke of 455 mm (Figure 5a). The plunger velocity at the filling phase was reduced from 4 m/s to 2 m/s in order to ensure a good filling of the whole part, including the section of 1 mm . The metal velocity at the ingate section was 12 m/s. The aluminium alloy was melted and held in a temperature-controlled furnace at 705∘C to ensure stable thermal conditions prior to injection. Temperature control was continuously maintained throughout the production campaign in order to guarantee process stability and repeatability. The key process parameters used both in simulation and in experimental are reported in Table2.

Table 2. Process parameters for numerical simulation and for experimental trials

Parameters | Unit | Simulation value | Experimental value
Piston velocity | m·s⁻¹ | 0.4 (first phase) 4 (second phase) | 0.4 (first phase) 2 (second phase)
Melt temperature | °C | 680 | 705
Die temperature | °C | 100 | 100
Total cycle time | S | 81.7 | 81.7

Molten metal was injected into the die cavity following a predefined piston velocity profile, reported in Figure 5c. The injection curve describes the evolution of piston displacement and velocity during the shot cycle. A first low-speed phase was applied to enable a controlled transfer of molten metal along the shot sleeve, followed by a high-speed phase to achieve complete cavity filling before the onset of solidification. The final stage corresponds to pressure build-up and intensification, which is required to compensate for solidification shrinkage and to enhance casting soundness. The selected
injection profile is consistent with standard industrial HPDC practice for aluminium alloys and was designed to limit air entrapment while ensuring adequate filling performance.

The thermal state of the die was monitored by means of infrared temperature mapping before and after the spraying phase, as shown in Figure 5d. Three reference points were selected to track temperature evolution. The thermal images illustrate the surface temperature distribution of the die prior to lubricant application and the corresponding temperature reduction following spraying. This monitoring procedure was essential to confirm that the die operated within the desired thermal window, promoting stable solidification conditions and reducing the risk of defects related to local overheating or insufficient cooling.

The castings produced during the industrial trials consisted of the step casting components integrated with the complete feeding system. In details, the manufactured part included the step casting geometry together with the runner, runner extension, and riser (Figure 5b).

[picture]

Fig. 5. a) Injection unit control interface displaying the shot sleeve configuration and filling conditions, b) final Step-shaped HPDC specimen, c) Piston injection profile (velocity over the displacement during the shot cycle) and d) Thermocamera acquisitions.

Fig. 5. a) Injection unit control interface displaying the shot sleeve configuration and filling conditions, b) final Step-shaped HPDC specimen, c) Piston injection profile (velocity over the displacement during the shot cycle) and d) Thermocamera acquisitions.

Step Casting Microstructure Investigation

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

Radiographic inspection and micrographs of the step castings were performed, revealing the presence of significant porosity only in the step with a thickness of 15 mm (Figure 6a). Microstructural analyses were therefore carried out in the central region of three step castings (Figure 6b), focusing on the assessment of the porosity percentage, SDAS and the thickness of the skin layer. For each step thickness the microstructural characterization was performed over the entire cross-sectional area.

[picture]

Fig. 6. (a) Radiographic inspection and microstructural analysis of a step casting, highlighting the presence of defects in selected sections; (b) Area of the step casting that underwent microstructural characterization.

Fig. 6. (a) Radiographic inspection and microstructural analysis of a step casting, highlighting the presence of defects in selected sections; (b) Area of the step casting that underwent microstructural characterization.

As an example, Figure 7 shows the microstructures of the 2 mm and 6 mm step thicknesses. Despite the substantial variation in section thickness, only limited differences were observed in terms of porosity content and secondary dendrite arm spacing (SDAS) among the different steps.
The average porosity, assessed by metallographic analysis, ranged from 1.1% (SD 0.3% ) in the 2 mm step to 1.4% (SD 0.1%) in the 6 mm step. Similarly, the SDAS varied within a narrow range, from 13μ m (SD 1.7μ m ) in the 1 mm step to 14.5μ m (SD 1.0μ m ) in the 15 mm step. This slight increase can reasonably be attributed to the reduction in cooling rate associated with increasing section thickness.
It should be noted, however, that the microstructure of the high-pressure die castings consists of both externally solidified crystals (ESCs), formed during the injection stage, and dendrites that developed within the die cavity. The coexistence of these distinct solidification features introduces significant heterogeneity and complicates the reliable determination of SDAS.
The skin layer thickness ranged from 35μ m (SD 19μ m ) in the 1 mm step to 130μ m (SD 23μ m ) in the 6 mm step. The relatively large data dispersion is mainly attributable to the discontinuous nature of the skin layer, particularly in the thinner steps, where it was not uniformly present along the entire cross-section

[picture]

Fig. 7. Microstructure of (a) the 2 mm thickness step and (b) the 6 mm thickness step.

Fig. 7. Microstructure of (a) the 2 mm thickness step and (b) the 6 mm thickness step.

Industrial Component Microstructure Investigation

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

The selected industrial component was an engine mount manufactured by a sports car producer using the same EN AC-43500 alloy adopted for the step casting specimens and produced by a high-pressure die casting (HPDC) process assisted by squeeze pins. As in the case of the step casting samples, the microstructural analysis focused on the evaluation of porosity content, secondary dendrite arm spacing (SDAS), and skin layer thickness.
Metallographic specimens extracted from different regions of the component are shown in Figure 8a, while representative micrographs illustrating the typical microstructural features are reported in

Figures 8 b and 8 c . The microstructural parameters were quantified over the entire cross-sectional area of each analysed sample.
The total porosity area fraction exhibited significant variability among the different locations: a porosity level of 0.8% was measured in sample A,1.3% in sample B , and up to 3% in sample C . Across all analysed specimens, the SDAS ranged from 14μ m (SD 2μ m ) in sample A to 18μ m (SD 2μ m ).
Finally, the skin layer thickness (Figure 8c) varied between a minimum of 125μ m (SD 24μ m ) in sample C and a maximum of 225μ m (SD 37μ m ) in sample A .

[picture]

Fig. 8. a) Image of the industrial component, with confidential features intentionally obscured for confidentiality reasons, c)Typical microstructure of the component, b) Skin layer in sections A and C .

Fig. 8. a) Image of the industrial component, with confidential features intentionally obscured for confidentiality reasons, c)Typical microstructure of the component, b) Skin layer in sections A and C .

Comparison of the Step Casting vs Industrial Component Microstructure

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

Microstructural analyses were performed on both the step casting specimens and the industrial component with the objective of assessing whether the step casting approach is able to reproduce microstructural features representative of a real, complex HPDC part. The comparison between the industrial component (orange, right-most side bars) and the step casting samples corresponding to the different section thicknesses (blue bars) is summarized in Figure 9 in terms of porosity percentage, SDAS and skin layer thickness. In all graphs, the thin error bars indicate the intrinsic variability of the measured quantities.

[picture]

Fig. 9. Comparison of step casting vs industrial component: a) porosity percentage, b) SDAS, c) Skin layer

Fig. 9. Comparison of step casting vs industrial component: a) porosity percentage, b) SDAS, c) Skin layer

The porosity results do not show a significant dependence on section thickness, as the measurements were performed at the centre of the steps. Across all investigated sections, including the thicker ones, porosity levels remain low and comparable. This observation is consistent with both ProCAST simulations and X-ray analyses, which indicate that in thicker steps porosity is predominantly localized in the lateral regions rather than in the central area, as a consequence of feeding limitations and local solidification behaviour. The industrial component exhibits a porosity level comparable to the upper range of the step casting values, indicating that the step casting successfully reproduces the defect severity observed in real HPDC parts. The overlap between the variability ranges further supports the representativeness of the step casting geometry. The relatively high scatter observed in the measured porosity is primarily associated with the presence of more massive sections within the industrial component, which exhibit a higher concentration of defects.

However, these regions correspond to low-stress areas of the component and can therefore tolerate such porosity levels without detrimental effects on structural performance. SDAS, instead, slightly increases with increasing section thickness in the step casting, reflecting the reduction in cooling rate. The SDAS measured in the industrial component falls within the range defined by the step casting samples, confirming that the solidification conditions reproduced in the step geometry are consistent with those experienced in the industrial component. This agreement is particularly significant given the complex thermal history and geometry of the real part.

Regarding the skin layer thickness, the step casting results reveal a non-monotonic dependence on section thickness. The skin layer thickness increases with section thickness up to the 6 mm step, beyond which a decrease is observed for thicker sections. This behaviour can be attributed to the combined effect of local cooling rates at the die-metal interface and melt flow conditions during cavity filling. In particular, increased flow turbulence promotes the formation of non-uniform and locally discontinuous skin layers. The industrial component exhibits a skin layer thickness comparable to that measured in the 6 mm step casting section and substantially remains within the overall variability envelope defined by the step casting dataset.

Overall, the results demonstrate that the proposed step casting is capable of reproducing not only the average microstructural characteristics of a real HPDC component, but also their inherent spatial variability. This confirms the suitability of the step casting as a reliable experimental benchmark for studying process-microstructure relationships in HPDC and for supporting the validation of numerical simulations and future data-driven modelling approaches.

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

By integrating numerical modelling with experimental validation, this study provides a practical and reliable framework for linking HPDC process conditions to resulting microstructural properties. The results not only demonstrate the effectiveness of the step casting as a benchmark tool, but also highlight the potential for refining simulation parameters to enhance predictive accuracy. Overall, this work contributes to the broader field of materials and manufacturing science by supporting the development of more robust methodologies, including future analytical and data-driven models, that enable designers to predict the behaviour of lightweight cast components with greater confidence and efficiency.

Fundings

The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

This work was supported by the European Union's Horizon Europe research and innovation programme, Zero Emission electric Vehicles enabled by haRmonised circularity, under No. 101138034.

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The original version of this paper is available on https://www.scientific.net/MSF.1188.107.pdf

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Keywords Index

#

3D Scanning Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

3D Surface Deviation Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

A

Additive Manufacturing Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Aerospace Castings Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Air Entrainment RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Aluminium Alloys Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Aluminum Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

B

Baffle Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

C

Cast Solidification Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

F

Fe Simulation Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

Filling Velocity Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process

H

High-pressure Die Casting Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process; Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

High-pressure Die Casting (hpdc) Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Hpc Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Hpdc RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Hydrogen Substitution Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

I

Impurity Elements Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

L

Low-pressure Casting Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

M

Machine Learning Prediction Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

Mechanical Properties Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Metal Casting; Neural Networks; Process Simulation; Digital Twins Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

Microstructural Prediction Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Microstructure Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys; Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Molten Aluminium Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

N

Numerical Simulation Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process

O

Optimization RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

R

Remanufacturing By Casting Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

Response Surface Methodology RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Reverse Engineering Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

S

Sand-mold Printing Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

Secondary Aluminium Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Sedimentation Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Simulation Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Solidification Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process; Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Step Casting Mould Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Sustainability Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production; Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

T

Twin-roll Casting Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Authors Index

A

Ainsworth, G. Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

B

Bailly, D. Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Balasubramani, N. Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process; Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Bourgeois, J. Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

C

Campanella, M. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Cusanno, A. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Czempas, D. Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

D

Donati, L. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Duraccio, M. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

F

Forde, J. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

G

Georgarakis, K. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Giese, A. Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Guglielmi, P. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

H

Hackert-Oschätzchen, M. Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Housni, S. Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting

Häner, S. Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

J

Jolly, M. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

K

Koslowski, E. Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Ktari, A. Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting; Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

L

Lembo, F. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Lian, J. Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Lu, D. Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

M

Mansori, M. Neural Network-Based Machine Learning Model for Spatiotemporal Prediction of Temperature and Fraction Solid in Low-Pressure Sand Casting; Effect of Interfacial Heat Transfer on the Solidification Behaviour of Numerically Simulated High-Pressure Die Casting Process; Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes; Digital Manufacturing of One-Off Replacement Components Using Reverse Engineering and Rapid Low-Pressure Sand Casting: Dimensional Evaluation and Key Challenges

Michaelis, C. Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Morri, A. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

P

Palanivel, A. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Palumbo, G. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Piccininni, A. RSM-Based Approach to Optimize the Gating System in High Pressure Die Casting

Pischinas, E. Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

R

Rajendran, S. Additive Manufacturing for Advanced and Functional Tooling of Dies Used in High-Pressure Die Casting Processes

Reggiani, B. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

S

Salonitis, K. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Scharifi, E. Potential and Advantages of Vertical Strip Casting for Production of High-Strength Aluminum-Magnesium Alloys

Schwarz, C. Assessment of Natural Gas-Hydrogen Fuel Blends for Industrial Melting Furnaces in Secondary Aluminium Production

Shaw, B. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Silva, M. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Sivarupan, T. Cleaner Melt Transfer of Recycled Aluminium Alloys: Simulation-Guided Launder Baffle Design for Aerospace-Grade Structural Castings

Squatrito, R. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component

Stathatos, E. Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

V

Vosniakos, G. Exploiting Artificial Neural Networks for Digital Twins in Sand Casting

Z

Zaniboni, G. Numerical and Experimental Design of a Step HPDC Mould for Process-Microstructure Correlation in Lightweight Component