Nanocomposite coatings like TiAlCrSiN, synthesized by physical vapor deposition, present promising potential to increase tool performance during challenging cutting applications. This study focuses on wear prognosis of cemented carbide inserts with TiAlCrSiN coatings for turning quenched and tempered 42CrMo4 steel. The purpose is to understand and predict the effect of coating architecture and thickness on tool wear. Two coating architectures, monolayer TiAlCrSiN and bilayer TiAlCrSiN/TiAlCrSiON with thickness s = ~2.0 µm and s = ~4.0 µm, are considered. Turning trials were carried out with cutting speed vc = 120 m/min, feed f = 0.25 mm and depth of cut ap = 1.5 mm. Cutting forces and flank wear VB were measured over cutting length lc, while tool damage was characterized by scanning electron microscopy analysis. Moreover, a machine learning (ML) approach combining process data, damage analysis and coating characteristics for wear prediction was developed. Higher coating thickness extends the tool life by delaying the transition between linear increase and progressive zones of tool wear curve. Oxygen incorporation reduces indentation hardness HIT and abrasive wear resistance for bilayer variant. However, workpiece material adhesion decreases with oxynitride top layer. This contributes to comparable tool performance for bilayer and monolayer variants until the transition to progressive wear zone. The ML based prediction framework shows promising potential to learn correlations between process data and tool wear until the transition to progressive wear zone. The prediction ability in terms of coating influence on the transition to progressive tool wear remains limited. The study provides promising basis for data-based understanding and prediction of coated tool wear behavior.

Tool wear is a decisive factor in gear hobbing for process reliability, component quality, and cost-effectiveness. Conventional wear detection methods are often based on visual inspections of the tool or indirect monitoring based on workpiece quality at fixed intervals and are therefore either inaccurate or delay the manufacturing process. In this work, a combination of physical models and data-based methods for tool wear monitoring was systematically investigated to enable early and reliable detection of wear conditions.The objective was to determine the current tool condition and enable a prediction of the remaining tool life. For this purpose, various process signals were recorded during the gear hobbing process. The raw data was preprocessed and converted into characteristic features. These characteristic features were then used to develop black-box models that monitor the wear status of the tool. The investigation included both classical approaches like threshold methods and artificial intelligence models such as random forests and neural networks. In addition, the potential of a grey-box model was analyzed. The grey-box model combines the physical fundamentals of the gear hobbing process such as cutting forces with data-based black-box approaches.The use of data-driven models, especially in combination with physical models, has shown that it is possible to distinguish between different degrees of wear with high accuracy. In addition, the use of self-learning algorithms offers the possibility of adapting the system to different tools, materials, and process conditions. The study highlights the potential of data-based approaches for wear monitoring in gear hobbing and provides a solid foundation for further development and industrial application.

In this paper, a methodical approach to evaluate the potential of quantum computing for manufacturing simulation, using the example of multi-axis milling of thin-walled aerospace components, is discussed. A developed approach for identifying bottlenecks in manufacturing simulations, for which the application of quantum computing potentially provides a speed-up or increase in accuracy, is presented. Moreover, indicators of quantum computing suitability and feasibility are defined with the main objective of identifying whether a manufacturing simulation bottleneck is suitable for quantum computing applications. First results of testing a hybrid routine as an application approach for the milling dynamics simulation on quantum machines are presented.

Inconel 718 is difficult to machine due to its high-temperature strength, low thermal conductivity, strong work hardening tendency, all of which contribute to increased cutting forces and rapid tool wear. Advanced tool materials such as ceramics and cubic boron nitride (CBN) offer high hardness, thermal stability, and chemical inertness, making them promising materials for improving tool life in the machining of Inconel 718. However, their differing thermal conductivities can cause variations in surface temperature during cutting, potentially affecting surface integrity. Despite this, few studies have systematically investigated how tool material influences surface integrity characteristics. This study addresses this gap by conducting a comprehensive experimental investigation on the face turning of Inconel 718 using ceramic and CBN tools under varying cutting parameters. Surface integrity indicators, including residual stresses and microstructural alterations, were analysed in detail. The results highlight the influence of tool material on the workpiece rim zone properties and provide valuable insights for tool selection in industrial applications.

Additive manufacturing (AM) enables rapid, near-net-shape fabrication with high material efficiency, but the resulting components often exhibit surface roughness, microstructural heterogeneity, and tensile residual stresses that reduce their performance. Machine hammer peening (MHP) is a mechanical surface treatment capable of modifying the surface integrity of AM components. It can be applied either as a conventional post-processing step after fabrication or as a hybrid interlayer treatment integrated into the build process. In this study, the effects of MHP process parameters and treatment strategies on wire-based laser metal deposition (LMD-w) Inconel 718 components were investigated, including the implementation of hybrid interlayer MHP. Surface topography, hardness, microstructure, and residual stresses were examined experimentally, while numerical simulations were developed to support the measurements and to characterize local contact conditions and plastic strain evolution during peening. The results show that MHP significantly reduces surface waviness and roughness, increases near-surface hardness, refines the microstructure, and introduces deep compressive residual stresses. Furthermore, hybrid interlayer MHP enhances the depth and uniformity of the modified layer by influencing the evolving microstructure during deposition. Standard forged Inconel 718 samples were also treated with MHP as a reference, showing comparable characteristics between the forged and AM components. These findings demonstrate that MHP is a versatile and effective modification technique for improving the performance and reliability of AM components, particularly when implemented as a hybrid interlayer treatment during the AM process.

Manufacturing companies require large numbers of high-performance cutting tools, which are often made from cemented carbides due to their high hardness and toughness. Cobalt (Co) is typically used as a binder because of its favorable mechanical properties and the high solubility of tungsten carbide (WC) in Co. However, Co is considered a critical raw material, as it is both economically important and subject to high supply risks due to political instabilities in its mining regions. In addition, Co mining and handling pose environmental and health risks to workers. These concerns motivate the search for alternative binder materials in cemented carbides. Nickel (Ni), iron (Fe), and a nickel–chromium-cobalt mixed binder (NiCrCo) are investigated in this work as potential binders containing less or no Co. The grinding behavior of cemented carbides has so far mainly been studied for Co-bonded systems. For cemented carbides with alternative binders, the grinding behavior in dependency of the material characteristics and grinding parameters remains unclear. This work characterizes these cemented carbides using hardness and critical fracture toughness measurements at various temperatures, and mesoscale structure analysis. Furthermore, analogy surface grinding experiments on the mentioned materials are carried out. Combining the material characterization and grinding analysis, conclusions on the material removal behavior are derived. The study shows that alternative binders lead to comparable, but not equal material properties and grinding behavior. These results contribute to a knowledge-based grinding of cemented carbides with alternative binders, supporting more economical, ecological, and human-friendly production of cemented carbide cutting tools.

In fineblanking, a mass production process for often safety-critical components, the objective is to achieve and maintain high-quality sheared surfaces without defects such as tears across as many processing cycles as possible. Fineblanking relies on modeling techniques that assume static process conditions. Consequently, these models struggle to capture the stochastic variations observed in real-world measurements from in-situ sensor systems, which typically involve substantial uncertain components, subsumed as process noise. This work introduces a systematic framework to decompose process noise into explainable and potentially unexplainable components, utilizing a comprehensive dataset, consisting of fineblanking process and quality data. Three complementary analytical approaches are employed: quality evolution analysis to track tear development over time, explainable predictive modeling to link force signal variations to tear formation, and deep latent variable modeling to uncover underlying drivers of process variations. The findings provide novel insights into the shearing dynamics under industrial conditions. For instance, the analysis reveals that tool wear drives distinct tear patterns across part geometries, linking systematic process variations to quality outcomes. This work offers a blueprint for decomposing process noise in sheet-metal forming processes.

The aviation industry is committed to reducing its impact on the climate to meet political targets (Paris Climate Agreement, Green Deal) and enhance social acceptance. In particular, the ambitious climate protection targets of Flightpath 2050 require disruptive and revolutionary propulsion concepts. The use of fuel cell systems is one of these propulsion concepts and enables near carbon-neutral operation. The fuel cell acts as an energy converter and supplies the primary electric drives so that there is potential for emission-free flying, particularly in the short and medium range. However, successful integration of the fuel cell into the aircraft's power train requires a significant increase in gravimetric power density compared to the status quo. In addition, the fuel cell and the entire process chain for its production must fulfill the high safety requirements of aviation. This paper briefly describes the process chain to produce a fuel cell stack, focusing in particular on the key component, the bipolar plate (BPP), and highlighting the requirements and quality characteristics specific to each process step. Based on this, the fields of action of the production technology for the achievement of a high gravimetric power density and a quality assurance that fulfills the standards of the aviation industry are derived. Examples for the realization of the fields of action are given based on the process step of forming.

Studies show that manufacturing companies that invest in automation and artificial intelligence can achieve productivity gains of up to 20

In aerospace engineering, safety-critical engine components operate under severe thermal and mechanical loads, requiring high surface integrity to ensure functional reliability and long-term durability. These components are commonly manufactured from nickel-based superalloys such as DA718, which possess superior strength and thermal resistance. However, their low thermal conductivity and strong work-hardening behavior impair machinability and cause extensive tool wear. Tool wear strongly impacts the thermomechanical loads during machining and induces modifications within the rim zone of the workpiece. In this study, the influence of tool wear in face turning of DA718 using ceramic cutting tools was investigated. Cutting forces and workpiece surface temperatures were experimentally recorded, and metallographic analyses were performed to examine machining-induced modifications in the rim zone. To improve the understanding of these modifications, the residual stress depth profiles were correlated with the depth of plastic deformations. The results demonstrate that progressing tool wear intensifies thermomechanical loads, leading to increased force components and elevated surface temperatures. Furthermore, both the magnitude and distribution of residual stresses, as well as the depth of slip lines and plastic deformation, were significantly affected. Using statistical methods, significant correlations between cutting-induced rim zone modifications were identified, and regression models were derived for predicting deformation depths based on residual stress parameters.

The microgeometry of gears enables the optimization of the acoustic excitation behavior and power density of gearboxes and is applied in the form of standardized or topological tooth flank modifications in profile and flank direction. The application possibilities and limitations of manufacturing topological tooth flank modifications using gear finish hobbing have not yet been systematically investigated. The aim of this study is therefore the simulative and experimental investigation of the manufacturability of topological modifications using adapted process kinematics for gear finish hobbing. The value and the shape of the tooth flank modifications were varied. The surface topographies, profile, and flank lines of the simulated gears were validated against experimental results, revealing a high degree of correlation in both the shape and progression of the applied modifications. The maximum deviation observed between simulated and manufactured modifications was approximately 10-15%, demonstrating a high level of agreement between the simulation model and experimental results within the investigated parameter range.

In precise sheet-metal forming operations such as fine blanking, the closed tool design precludes direct observation during production, which makes indirect monitoring of punch wear necessary. Previous work has shown that the sheared surfaces of the scrap web can be used to infer the punch wear through spatial correlation analysis of areal roughness parameters. However, these correlations have so far each explained only a limited portion of the variance and have only been investigated for a single punch geometry, leaving their robustness and generalizability open to question. In this work, multivariate regression approaches and feature importance analysis are used to combine complementary areal roughness parameters and generate robust indicators of punch wear. The plausibility of these indicators is validated by linking the correlated scrap web sheared surface features to physically interpretable wear mechanisms, such as worn surface area or punch breakage. Furthermore, the approach is extended to multiple punch geometries to examine the extent to which the identified correlation patterns can be transferred to different tool designs and process conditions. The results demonstrate the generalizability of spatial correlation-based indicators across different geometries and process conditions.

Over the past decades, global air traffic has increased continuously, with passenger kilometers roughly doubling every fifteen to twenty years, and this trend is estimated to continue, with some adjustments due to COVID-19 impact. In response to the resulting environmental challenges, the European initiatives Flightpath 2050 and Clean Sky serve as central drivers of technological development aimed at achieving ambitious sustainability goals. Flightpath 2050 targets, relative to a reference engine from the year 2000, include a 75% reduction in CO2 emissions per passenger kilometer, a 90% reduction in NOx emissions, and a 65% reduction in noise emissions. These objectives highlight the urgent need for emission reduction strategies across all manufacturing domains, including turbine component production. This study evaluates the environmental impacts of the preturning and roughing operations employed in turbine disk production. The analysis focuses on these specific processes rather than the entire product, as the approach of process-level Life Cycle Assessments (LCA) are more universally applicable across different products, and their systematic combination can ultimately form a comprehensive product-level LCA. Operational data, such as energy usage, cooling lubricants, and compressed air, were gathered and processed from the equipment involved in manufacturing. The collected data were analyzed and modeled in Spheras life cycle assessment software LCA for Experts (version 10.9.0.20) to quantify the environmental effects of each process. The findings of the current research emphasize notable patterns of resource utilization and their respective environmental impacts. Furthermore, the Industrial Digital Twin Association (IDTA) Product Carbon Footprint (PCF) template was utilized to present the findings in a standardized manner, enabling effective data transfer between stakeholders. The results demonstrate the critical need to leverage machine data for sustainability analysis, providing inputs for industry practice enhancement and progress toward better environmental performance.

Application fields and requirements for roll-cladded cooling plates are continuously rising. Especially as part of the thermal management systems in battery electric vehicles (BEV), the share of roll-cladded cooling plates is growing. A deeper understanding of the deformation regime in the roll bite is needed to completely fulfill the high quality, performance and cost requirements of the automotive industry Whereas most cause-effect relationships in the roll-cladding process have been scientifically evaluated, the influence of separating agents on the deformation regime in partial roll-cladding has not yet been investigated. To examine this relationship, an experimental set up is created and trials are conducted on a laboratory size roll-cladding mill. Two different aluminum alloy blanks are joined together under temperature by roll-cladding without the application of strip tensions and with different separating agent patterns. The results show: Firstly, there is a correlation between the materials’ relative flow stress difference and their relative deformation. Secondly, the separating agents’ areal share over the blank width significantly impacts the deformation regime in the roll bite. Thirdly, in areas with separating agent there is a correlation between the surface elongation of the bottom blank and the elongation of the contact interface between the blanks, which governs the later cooling channel tolerances. To use the results in the industrial application, the impact of so far neglected parameters such as strip tensions have to be considered in future research.

Gear systems operate under high mechanical and tribological loads, making their surfaces vulnerable to wear and fatigue. Improving surface durability requires finishing processes that improve near-surface properties and extend service life. Since machine hammer peening (MHP) offers such potential, this study investigates its influence on the performance of case-hardened spur gears and evaluates its suitability as an alternative to shot peening as a conventional finishing method. Analog specimens with simplified geometries were treated using various MHP parameters to identify effective process settings. These optimized settings were then applied to real spur gears to assess performance under practical conditions. The experiments showed that MHP can significantly modify surface integrity, achieving surface roughness reductions of up to 55%, surface hardness increases of up to 30%, and compressive residual stresses exceeding −1400 MPa with stability to depths of 200 µm. These modifications resulted in improved wear and fatigue performance, with increases in load cycle number in the tooth flank up to 99% and an increase in load amplitude in the tooth root of more than 5%. For comparison, specimens were also treated with shot peening. Although MHP induced stronger surface integrity modifications, shot peening achieved higher overall load-carrying capacity because several critical areas could not be fully accessed by MHP, limiting its effectiveness. Overall, MHP shows promise as a finishing process, but its full potential depends on overcoming accessibility limitations in complex gear geometries.

In continuous cutting, the use of high-pressure cutting fluid supply improves fluid penetration at the tool/workpiece interface, improving heat dissipation and allowing higher cutting speeds for better machining efficiency. While prior research has mostly focused on the role of cutting fluid pressure, the influence of nozzle design on machining outcomes has not been thoroughly examined. This study explores the impact of various nozzle opening shapes and pressures on tool wear and workpiece surface roughness, utilizing additively manufactured nozzles. Findings indicate that chip morphology under the investigated cutting conditions is primarily influenced by cutting fluid pressure and is minimally affected by nozzle opening shape. In addition, the design of the flank face nozzle is crucial for optimizing surface roughness and extending tool life. The simulation results indicate that the difference in various flank face nozzles is due to changes in the flow velocity within the cutting zone.

Wire electrochemical machining (wire ECM) is a promising manufacturing process that combines the principle of anodic metal dissolution with the kinematic flexibility of a wire electrode. By using a wire instead of a task-specific tool, wire ECM enables the production of complex 2.5-dimensional contours without the costly and time-consuming tool design process.Process stability and productivity in wire ECM are strongly dependent on the effective flushing of the narrow working gap. A radial flushing method employs a perforated hollow wire electrode, allowing the electrolyte to be delivered directly into the machining zone. This approach improves removal of dissolved material and reduces the dependency of the flushing concept on workpiece thickness, unlike in axial flushing. Increasing the flushing pressure improves electrolyte exchange, which can enhance process stability and productivity.However, the influence of flushing pressures above 20 bar has not yet been sufficiently investigated for the radial flushing method. In this study, a high-pressure pump with operating pressures up to 100 bar is used to examine the role of flushing in wire ECM. Since the flushing regime is influenced by factors such as the internal diameter of the hollow wire electrode and perforation configuration, their effects on process stability, productivity, and surface quality in relation to workpiece thickness will be investigated.

Wire electrical discharge machining (WEDM) is widely used for high-precision manufacturing but remains limited in productivity by unpredictable wire breakage. The risk of wire failure is particularly high during the main cut, where elevated thermal loads from intense discharge activity coincide with mechanical stresses caused by wire tension, flushing, and vibration. This work presents an in-situ high-speed imaging approach for analyzing discharge activity and wire behavior in the temporal vicinity of wire breakage under realistic main-cut conditions. The accuracy and limitations of camera-based discharge detection are evaluated through comparison with electrical measurements, explicitly accounting for sampling-related effects. Discharge pulse areas are analyzed to characterize their statistical distribution and temporal evolution prior to wire breakage, revealing an increase in the aggregated rolling mean pulse area toward failure that is statistically supported at an exploratory significance level. Wire vibration measurements from a representative dataset provide characteristic amplitude and frequency ranges but do not yield a statistically robust precursor when considered in isolation. High-speed recordings further show that discharge activity can persist transiently after wire breakage and migrate along the workpiece surface, indicating a potential risk to surface integrity beyond productivity loss.

Accurate tool wear segmentation is essential for automated machining. However, deep learning models often require large labeled datasets that are rarely available in industrial environments. Limited image quality, diverse tool geometries, and costly annotation constrain data volume and weaken model robustness. This study proposes a diffusion based data synthesis framework to enhance wear segmentation under small sample conditions. A DreamBooth fine-tuned Stable Diffusion Inpainting model generates realistic wear patterns while preserving tool geometry, guided by structured prompts and a patch conditioned masking strategy. The synthetic data are used to retrain a U-Net segmentation network, achieving about 6% improvement in Dice coefficient on unseen tool types. Results demonstrate that combining real and diffusion-generated data effectively mitigates data scarcity and improves generalization, offering a practical approach for reliable tool wear monitoring in industrial applications.

During machining, the workpiece surface experiences intense thermomechanical loads, leading to changes in its crystalline structure and stress state. These subsurface and grain size modifications critically influence the mechanical properties of the finished component. Grain size evolution is typically investigated through cutting experiments or FEM-based chip formation simulations. However, both approaches are costly and time-consuming, limiting their broader applicability. This work presents a meshless simulation approach as an efficient alternative to FEM, incorporating a physically based grain size evolution model that accounts for strain, strain rate, and temperature effects such as dynamic recrystallization. The method leverages GPU parallelization to significantly reduce computation time. Model validation is performed through orthogonal cutting experiments and microstructure measurements. By integrating experimental data with simulation, the study systematically analyzes the influence of cutting parameters on thermomechanical loading and the resulting grain size modification. The proposed framework enables rapid and reliable prediction of subsurface grain structure evolution, providing a valuable tool for optimizing machining processes, tool designs, and surface integrity in both industrial and research contexts.