Defect and Diffusion Forum Vol. 450

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

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

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

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

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

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

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

Abstract: Sectors such as energy, aerospace, and heavy machinery increasingly rely on the machining of large components, where boring bars can easily exceed 200 mm in diameter and reach length-to-diameter ratios of up to 14. In these operations, chatter remains the dominant limitation due to the inherently low dynamic stiffness of such long tools. While Tuned Mass Dampers (TMDs) are widely applied in small and medium-sized boring bars, but transferring this technology to large-scale tools introduces significant challenges, particularly in the selection and tuning of damper components and the difficulty of evaluating performance prior to manufacturing. Because producing large boring bars is costly, a structured and predictive design strategy is essential to avoid trial-and-error iterations. This work introduces a scaling methodology that adapts TMD-integrated boring bar designs to large dimensions, providing a systematic approach to predict dynamic behavior across different tool sizes. The methodology is demonstrated through a case study involving Ø200 mm boring bar with length of 14 times the diameter. Experimental validation with the manufactured prototype confirms that the proposed scaling strategy enables effective chatter suppression and offers a practical path for extending TMD technology to large-scale boring applications.

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