Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.062
Yuan Jing , Guanchen Gong , Albrecht Hänel , Steffen Ihlenfeldt
Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and Machine Learning. Among these key technologies, digital twins stand out by offering a promising approach to intelligently utilize this data. In the virtual representation of a physical asset, data reflects the conditions of the physical entity, while models simulate and predict its behavior. In this paper, a hybrid cutting force model is proposed for digital twins of helical end mills, focusing on cutting force analysis during the utilization phase of the machining process. This model combines a fairly mature physical process modelling approach with a data-driven method, specifically a neural network trained on real process data, to address the limitations inherent in their respective applications. The physics-based model provides meaningful constraints on the neural network’s training, ensuring reliable cutting force prediction, particularly in scenarios with limited process data availability. The cutter’s profile, generated by the geometric model, and the cutter-workpiece engagement maps, derived from the virtual machining model, together serve as inputs for the hybrid cutting force model.
{"title":"Integrating Hybrid Physics-Data Approaches for Enhanced Cutting Force Modeling in Digital Twins of Helical End Mills","authors":"Yuan Jing , Guanchen Gong , Albrecht Hänel , Steffen Ihlenfeldt","doi":"10.1016/j.procir.2025.02.062","DOIUrl":"10.1016/j.procir.2025.02.062","url":null,"abstract":"<div><div>Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and Machine Learning. Among these key technologies, digital twins stand out by offering a promising approach to intelligently utilize this data. In the virtual representation of a physical asset, data reflects the conditions of the physical entity, while models simulate and predict its behavior. In this paper, a hybrid cutting force model is proposed for digital twins of helical end mills, focusing on cutting force analysis during the utilization phase of the machining process. This model combines a fairly mature physical process modelling approach with a data-driven method, specifically a neural network trained on real process data, to address the limitations inherent in their respective applications. The physics-based model provides meaningful constraints on the neural network’s training, ensuring reliable cutting force prediction, particularly in scenarios with limited process data availability. The cutter’s profile, generated by the geometric model, and the cutter-workpiece engagement maps, derived from the virtual machining model, together serve as inputs for the hybrid cutting force model.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 358-363"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.064
Markus Meurer , Tobias Kelliger , Nicklas Gerhard , Adrian Karl Rüppel , Adina Grimmert , Thomas Bergs
The manufacturing of safety-critical engine components for aerospace applications involves extensive development and testing throughout the entire process chain. The numerous necessary experimental investigations and destructive metallographic analyses result in significant costs and high levels of scrap material. A key quality characteristic in the safety-critical low-pressure region of the engine is Surface Integrity (SI). This characteristic is primarily influenced by the thermo-mechanical loads induced during the manufacturing process, along with the manufacturing history of the semi-finished product. Currently, SI can only be characterized through destructive testing methods after production. This paper presents an approach currently developed by the Manufacturing Technology Institute MTI of RWTH Aachen University, the Fraunhofer Institute for Production Technology IPT, and MTU Aero Engines AG for the model-based prediction, monitoring, control, and evaluation of machining processes concerning process-induced SI characteristics. Using a multiscale approach, SI is predicted with spatial resolution along a complex component contour, based on the machining conditions. The focus of model development is on the operations of turning, broaching, and grinding the blade-disc combination. The developed models are coupled with a soft sensor installed in the machine environment, enabling SI monitoring during machining. The digital twin of the component, derived from the data, aims to enable quality assessment without the need for destructive testing of the component. This paper marks the beginning of a publication series presenting the project results obtained throughout the next years.
{"title":"ManuSafeNextGen: Model-Based Manufacturing of Safety-Critical Components for Next Generation Engines – Part I: Methodology","authors":"Markus Meurer , Tobias Kelliger , Nicklas Gerhard , Adrian Karl Rüppel , Adina Grimmert , Thomas Bergs","doi":"10.1016/j.procir.2025.02.064","DOIUrl":"10.1016/j.procir.2025.02.064","url":null,"abstract":"<div><div>The manufacturing of safety-critical engine components for aerospace applications involves extensive development and testing throughout the entire process chain. The numerous necessary experimental investigations and destructive metallographic analyses result in significant costs and high levels of scrap material. A key quality characteristic in the safety-critical low-pressure region of the engine is Surface Integrity (SI). This characteristic is primarily influenced by the thermo-mechanical loads induced during the manufacturing process, along with the manufacturing history of the semi-finished product. Currently, SI can only be characterized through destructive testing methods after production. This paper presents an approach currently developed by the Manufacturing Technology Institute MTI of RWTH Aachen University, the Fraunhofer Institute for Production Technology IPT, and MTU Aero Engines AG for the model-based prediction, monitoring, control, and evaluation of machining processes concerning process-induced SI characteristics. Using a multiscale approach, SI is predicted with spatial resolution along a complex component contour, based on the machining conditions. The focus of model development is on the operations of turning, broaching, and grinding the blade-disc combination. The developed models are coupled with a soft sensor installed in the machine environment, enabling SI monitoring during machining. The digital twin of the component, derived from the data, aims to enable quality assessment without the need for destructive testing of the component. This paper marks the beginning of a publication series presenting the project results obtained throughout the next years.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 370-375"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.096
Syed Ahsan Adeeb , Yigit Karpat
In plunging-type tests, a cutting tool is given a sinusoidal movement as the work material with a web on its surface is rotated at a constant speed. If the amplitude and feed rate of the cutting tool and rotational speed of the work material are correctly set, the plunging test can be completed within a full rotation. As a result, a detailed investigation of different episodes of micro-scale machining, such as rubbing, plowing, and shearing, can be conducted with a single test. Combined with force measurements and cut chip morphology, the process mechanics can be investigated in detail. This study conducted plunging tests on an ultra-precision CNC with a diamond cutting tool on commercially pure titanium alloy. The differences in tangential and normal forces observed during plunge-in and pull-out periods corresponding to the same amplitude were analyzed using an analytical model. Resultant forces during the pull-out phase are larger than those observed in the plunge-in phase, attributed to an increase in cut chip thickness. A computational model of the plunging-type experiment has also been developed based on the findings of the analytical model. The proposed hybrid approach may be useful to improve identification of material constitutive model parameters based on micro scale machining experiments.
{"title":"Using plunging-type testing to investigate process mechanics at micro scale machining","authors":"Syed Ahsan Adeeb , Yigit Karpat","doi":"10.1016/j.procir.2025.02.096","DOIUrl":"10.1016/j.procir.2025.02.096","url":null,"abstract":"<div><div>In plunging-type tests, a cutting tool is given a sinusoidal movement as the work material with a web on its surface is rotated at a constant speed. If the amplitude and feed rate of the cutting tool and rotational speed of the work material are correctly set, the plunging test can be completed within a full rotation. As a result, a detailed investigation of different episodes of micro-scale machining, such as rubbing, plowing, and shearing, can be conducted with a single test. Combined with force measurements and cut chip morphology, the process mechanics can be investigated in detail. This study conducted plunging tests on an ultra-precision CNC with a diamond cutting tool on commercially pure titanium alloy. The differences in tangential and normal forces observed during plunge-in and pull-out periods corresponding to the same amplitude were analyzed using an analytical model. Resultant forces during the pull-out phase are larger than those observed in the plunge-in phase, attributed to an increase in cut chip thickness. A computational model of the plunging-type experiment has also been developed based on the findings of the analytical model. The proposed hybrid approach may be useful to improve identification of material constitutive model parameters based on micro scale machining experiments.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 561-566"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.044
U. Guerricagoitia , J. Álvarez , D. Barrenetxea , M. García , U. Alonso
The shoe-type centerless grinding (STCG) process stands out for its high productivity and geometric precision in grinding bearing rings. The roundness error of these parts is critical, as it affects on the generation of unwanted noise during operation, dynamic performance and service life of the components among other problems. Recently, the industry has required that the Fast Fourier Transform (FFT) of the peripherical surface of the part remains below a specific acceptance curve to avoid problems arising from this roundness error. Geometric regeneration, which is mainly affected by the geometry and angular positioning of the support shoes is a crucial aspect, as it can produce components with high amplitude that exceed the acceptance curve. Previous studies have investigated this phenomenon with fixed single contact shoes; however, the industry has started using double and tilting support shoes. In this paper, the geometric stability of double shoes has been characterized and experimentally validated. This has enabled the development of stability maps that predict the components produced under different shoe angle combinations, allowing the selection of the optimal combination and reducing set-up times.
{"title":"New Geometric Stability Maps for Predicting Unstable Lobe Regeneration During Shoe-Type Centerless Grinding with Tilting Shoes","authors":"U. Guerricagoitia , J. Álvarez , D. Barrenetxea , M. García , U. Alonso","doi":"10.1016/j.procir.2025.02.044","DOIUrl":"10.1016/j.procir.2025.02.044","url":null,"abstract":"<div><div>The shoe-type centerless grinding (STCG) process stands out for its high productivity and geometric precision in grinding bearing rings. The roundness error of these parts is critical, as it affects on the generation of unwanted noise during operation, dynamic performance and service life of the components among other problems. Recently, the industry has required that the Fast Fourier Transform (FFT) of the peripherical surface of the part remains below a specific acceptance curve to avoid problems arising from this roundness error. Geometric regeneration, which is mainly affected by the geometry and angular positioning of the support shoes is a crucial aspect, as it can produce components with high amplitude that exceed the acceptance curve. Previous studies have investigated this phenomenon with fixed single contact shoes; however, the industry has started using double and tilting support shoes. In this paper, the geometric stability of double shoes has been characterized and experimentally validated. This has enabled the development of stability maps that predict the components produced under different shoe angle combinations, allowing the selection of the optimal combination and reducing set-up times.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 250-255"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.045
Vasiliki C. Panagiotopoulou , Evangelia Xydea , Panagiotis Stavropoulos
All environmental issues, and particularly climate change, have irreversible environmental, social, and financial impacts. Significantly reducing carbon emissions by 2030, as the highest contributor to climate change, is a vital for EU as expressed in the European Green Deal. In this direction, sustainable manufacturing intends to reduce negative impacts by minimizing energy consumption, lowering carbon emissions, and optimizing resource efficiency. Hybrid Manufacturing (HM), combining additive (AM) and subtractive manufacturing (SM) processes, is a very promising process in sustainable manufacturing, in addition of being a novel approach. The aim of this paper is to identify the carbon intensive parts of a HM cell, perform carbon footprint calculations through mathematical modelling and Life Cycle Assessment (LCA) and classify them as either energy or material related carbon emissions. This methodology is implemented in an HM including Direct Energy Deposition (DED) and CNC milling, successively alternating between the two to complete the part. Results indicate that at machine tool level, the material related emissions (4.64 kg CO2-eq), slightly dominate over the energy related emissions (4.51 kg CO2-eq). Powder consumption is almost solely responsible for material related emissions. Among energy related emissions, the AM cell’s chiller was the largest contributor (accounting for 28.3% of the total emissions), followed by the AM head motion system (10.9%), and laser machine (9.6%), while the subtractive process emitting considerably less in this case. Future work will aim to optimize process parameters to reduce HM emissions while ensuring high product quality.
所有环境问题，特别是气候变化，都具有不可逆转的环境、社会和金融影响。正如《欧洲绿色协议》所表达的那样，到2030年大幅减少碳排放对欧盟至关重要，因为碳排放是气候变化的最大贡献者。在这个方向上，可持续制造旨在通过最大限度地减少能源消耗、降低碳排放和优化资源效率来减少负面影响。混合制造（HM）结合了增材制造（AM）和减材制造（SM）工艺，是一种非常有前途的可持续制造工艺，也是一种新颖的方法。本文的目的是确定HM细胞的碳密集部分，通过数学建模和生命周期评估（LCA）进行碳足迹计算，并将其分类为能源或材料相关的碳排放。该方法在HM中实现，包括直接能量沉积（DED）和数控铣削，在两者之间依次交替以完成零件。结果表明，在机床水平上，材料相关排放（4.64 kg CO2-eq）略高于能源相关排放（4.51 kg CO2-eq）。粉末消耗几乎是材料相关排放的唯一原因。在与能源相关的排放中，AM电池的冷却器是最大的贡献者（占总排放量的28.3%），其次是AM头部运动系统（10.9%）和激光机器（9.6%），而减法过程在这种情况下的排放量要少得多。未来的工作将旨在优化工艺参数，以减少HM排放，同时确保高产品质量。
{"title":"Εvaluating Carbon Emissions of Hybrid Manufacturing Process: A Case Study on Additive and Subtractive Manufacturing","authors":"Vasiliki C. Panagiotopoulou , Evangelia Xydea , Panagiotis Stavropoulos","doi":"10.1016/j.procir.2025.02.045","DOIUrl":"10.1016/j.procir.2025.02.045","url":null,"abstract":"<div><div>All environmental issues, and particularly climate change, have irreversible environmental, social, and financial impacts. Significantly reducing carbon emissions by 2030, as the highest contributor to climate change, is a vital for EU as expressed in the European Green Deal. In this direction, sustainable manufacturing intends to reduce negative impacts by minimizing energy consumption, lowering carbon emissions, and optimizing resource efficiency. Hybrid Manufacturing (HM), combining additive (AM) and subtractive manufacturing (SM) processes, is a very promising process in sustainable manufacturing, in addition of being a novel approach. The aim of this paper is to identify the carbon intensive parts of a HM cell, perform carbon footprint calculations through mathematical modelling and Life Cycle Assessment (LCA) and classify them as either energy or material related carbon emissions. This methodology is implemented in an HM including Direct Energy Deposition (DED) and CNC milling, successively alternating between the two to complete the part. Results indicate that at machine tool level, the material related emissions (4.64 kg CO<sub>2</sub>-eq), slightly dominate over the energy related emissions (4.51 kg CO<sub>2</sub>-eq). Powder consumption is almost solely responsible for material related emissions. Among energy related emissions, the AM cell’s chiller was the largest contributor (accounting for 28.3% of the total emissions), followed by the AM head motion system (10.9%), and laser machine (9.6%), while the subtractive process emitting considerably less in this case. Future work will aim to optimize process parameters to reduce HM emissions while ensuring high product quality.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 256-261"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.050
Juan Manuel Bello Bermejo , Berk Saatçi , Daniel Johansson , Sören Hägglund , Jan-Eric Ståhl , Christina Windmark
Optimization of machining processes, such as milling, is essential for industrial efficiency and product quality. To achieve greater efficiency, it is necessary to understand how tools wear down in different conditions in order to anticipate possible undesirable events like sudden breakage or unpredictable degradation. This study focuses on understanding tool wear in dry milling of compacted graphite iron (CGI) EN-GJV-450 using PVD-coated cemented carbide and cBN tools to predict tool life effectively. The research builds on the Colding model, an empirical framework for tool life estimation, by incorporating and comparing two chip thickness concepts in order to optimize the Colding model’s performance, maximum chip thickness (hmax) and equivalent chip thickness (he). Through systematic experimentation and modelling, this work has identified optimal conditions for tool life prediction, with hmax offering a potentially resource-efficient cross-validation alternative aligned with sustainability goals. The results demonstrate that the optimized Colding model effectively predicts tool life for both coated cemented carbide and cBN cutting tools with round geometry in dry milling of CGI. The insights gained further enhance our understanding of the milling process and provide a solid foundation for selecting appropriate machining parameters to extend tool life and improve process efficiency.
{"title":"Optimal modelling of Colding parameters for round inserts with respect to tool use-time criteria","authors":"Juan Manuel Bello Bermejo , Berk Saatçi , Daniel Johansson , Sören Hägglund , Jan-Eric Ståhl , Christina Windmark","doi":"10.1016/j.procir.2025.02.050","DOIUrl":"10.1016/j.procir.2025.02.050","url":null,"abstract":"<div><div>Optimization of machining processes, such as milling, is essential for industrial efficiency and product quality. To achieve greater efficiency, it is necessary to understand how tools wear down in different conditions in order to anticipate possible undesirable events like sudden breakage or unpredictable degradation. This study focuses on understanding tool wear in dry milling of compacted graphite iron (CGI) EN-GJV-450 using PVD-coated cemented carbide and cBN tools to predict tool life effectively. The research builds on the Colding model, an empirical framework for tool life estimation, by incorporating and comparing two chip thickness concepts in order to optimize the Colding model’s performance, maximum chip thickness (h<sub>max</sub>) and equivalent chip thickness (h<sub>e</sub>). Through systematic experimentation and modelling, this work has identified optimal conditions for tool life prediction, with h<sub>max</sub> offering a potentially resource-efficient cross-validation alternative aligned with sustainability goals. The results demonstrate that the optimized Colding model effectively predicts tool life for both coated cemented carbide and cBN cutting tools with round geometry in dry milling of CGI. The insights gained further enhance our understanding of the milling process and provide a solid foundation for selecting appropriate machining parameters to extend tool life and improve process efficiency.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 286-291"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The applications of advanced ceramics such as Silicon carbide (SiC) or Silicon-SiC (Si-SiC) are widely developed in electronic, automotive and aerospace. The grinding of such hard and brittle materials remains challenging in terms of efficiency, accuracy and surface integrity. Grinding process involves the simultaneous interaction of multiple cutting edges with random geometries. The grain engagement is used to analyse and model the forces generated. The uncut chip thickness is difficult to determine in the case of grinding due to the uncertainty of the grain shapes, sizes and positions. This study presents a new method to simulate the interaction between each grain of the grinding wheel and the workpiece through the evaluation of the uncut chip thickness. Firstly, the real 3D topography of the electroplated diamond grinding wheel is measured using a focus-variation microscope. Then, the uncut chip thickness for each grain is calculated using this tool topography. The results coming from the simulation are used to evaluate forces generated during the grinding process. Finally, the results from the simulation are compared with experimental measurements on SiC material grinding.
{"title":"Determination of grain engagement based on real 3D wheel topography for modelling forces and surface during silicon carbide grinding","authors":"Clement Lestremau , Charly Euzenat , Frederic Rossi , Guillaume Fromentin , Freddy Guilbaud , Sebastien Denneulin","doi":"10.1016/j.procir.2025.02.031","DOIUrl":"10.1016/j.procir.2025.02.031","url":null,"abstract":"<div><div>The applications of advanced ceramics such as Silicon carbide (SiC) or Silicon-SiC (Si-SiC) are widely developed in electronic, automotive and aerospace. The grinding of such hard and brittle materials remains challenging in terms of efficiency, accuracy and surface integrity. Grinding process involves the simultaneous interaction of multiple cutting edges with random geometries. The grain engagement is used to analyse and model the forces generated. The uncut chip thickness is difficult to determine in the case of grinding due to the uncertainty of the grain shapes, sizes and positions. This study presents a new method to simulate the interaction between each grain of the grinding wheel and the workpiece through the evaluation of the uncut chip thickness. Firstly, the real 3D topography of the electroplated diamond grinding wheel is measured using a focus-variation microscope. Then, the uncut chip thickness for each grain is calculated using this tool topography. The results coming from the simulation are used to evaluate forces generated during the grinding process. Finally, the results from the simulation are compared with experimental measurements on SiC material grinding.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 173-178"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.032
Chenghan Wang , Ting Yue , Dongdong Xu , Zhirong Liao , Jun Wu , Bin Shen
Cutting processes involve complex interactions among various physical factors that collectively influence machining performance, including cutting force, tool wear, deformation, and chatter. Accurately simulating these factors is essential for enhancing the efficiency of process development and optimization, yet it remains a significant challenge in the field. One of the main obstacles is the lack of a comprehensive simulation framework that integrates multiple physical models. To address this challenge, this paper presents a novel multi-physics simulation model that combines material removal, cutting force and temperature predictions, and tool wear distribution assessment. A key feature of our approach is the enhanced point-based Cutter-Workpiece Engagement (CWE) extraction algorithm, which accurately models cutting tools with arbitrary cutting-edge shapes and discretizes the cutting process into explicit orthogonal cutting elements. By breaking down complex time-varying processes into a series of standard problems, we can effectively integrate various physical factors. We utilize neural networks trained on physical datasets to derive cutting forces and temperatures for each element, facilitating precise predictions of tool wear evolution along the cutting edge throughout the machining process. The effectiveness of our method has been validated through ball-end milling experiments and an application of aeroengine blade milling process. This innovative, machine learning-integrated framework for multi-physics modeling establishes a solid foundation for a reliable and comprehensive virtual machining system.
{"title":"A Multi-Physics Simulation Model for Universal Cutting Process based on an Enhanced CWE Extraction Method","authors":"Chenghan Wang , Ting Yue , Dongdong Xu , Zhirong Liao , Jun Wu , Bin Shen","doi":"10.1016/j.procir.2025.02.032","DOIUrl":"10.1016/j.procir.2025.02.032","url":null,"abstract":"<div><div>Cutting processes involve complex interactions among various physical factors that collectively influence machining performance, including cutting force, tool wear, deformation, and chatter. Accurately simulating these factors is essential for enhancing the efficiency of process development and optimization, yet it remains a significant challenge in the field. One of the main obstacles is the lack of a comprehensive simulation framework that integrates multiple physical models. To address this challenge, this paper presents a novel multi-physics simulation model that combines material removal, cutting force and temperature predictions, and tool wear distribution assessment. A key feature of our approach is the enhanced point-based Cutter-Workpiece Engagement (CWE) extraction algorithm, which accurately models cutting tools with arbitrary cutting-edge shapes and discretizes the cutting process into explicit orthogonal cutting elements. By breaking down complex time-varying processes into a series of standard problems, we can effectively integrate various physical factors. We utilize neural networks trained on physical datasets to derive cutting forces and temperatures for each element, facilitating precise predictions of tool wear evolution along the cutting edge throughout the machining process. The effectiveness of our method has been validated through ball-end milling experiments and an application of aeroengine blade milling process. This innovative, machine learning-integrated framework for multi-physics modeling establishes a solid foundation for a reliable and comprehensive virtual machining system.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 179-184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.033
Iker Urresti Espilla , Iñigo Llanos , Luis Norberto López de Lacalle
Drilling of CFRP components is a key manufacturing process in the aircraft manufacturing industry. Airframe structures and fuselage skins are often assembled using riveted joints, which require pre-drilling of composite parts. However, drilling CFRP can be challenging due to the inhomogeneous and anisotropic nature of the material, which can lead to major defects such as delamination, fiber pull-out and uncut fibers. In particular, push-out delamination at the hole exit is considered critical, as it can compromise the structural health of the components and even lead to their rejection. Therefore, the development of monitoring and modeling techniques to predict push-out delamination is of great importance to the industry. In this regard, the present work introduces a theoretical model for the prediction of exit delamination and thrust force evolution during CFRP drilling. The results are evaluated in comparison to part delamination and thrust force data obtained from experimental drilling tests. The results indicate that the proposed model can provide valuable insight into the CFRP drilling for process optimization, enabling the aerospace industry to improve current drilling practices towards delamination free drilling of CFRP components.
{"title":"Exit delamination failure modelling during drilling of CFRP laminates","authors":"Iker Urresti Espilla , Iñigo Llanos , Luis Norberto López de Lacalle","doi":"10.1016/j.procir.2025.02.033","DOIUrl":"10.1016/j.procir.2025.02.033","url":null,"abstract":"<div><div>Drilling of CFRP components is a key manufacturing process in the aircraft manufacturing industry. Airframe structures and fuselage skins are often assembled using riveted joints, which require pre-drilling of composite parts. However, drilling CFRP can be challenging due to the inhomogeneous and anisotropic nature of the material, which can lead to major defects such as delamination, fiber pull-out and uncut fibers. In particular, push-out delamination at the hole exit is considered critical, as it can compromise the structural health of the components and even lead to their rejection. Therefore, the development of monitoring and modeling techniques to predict push-out delamination is of great importance to the industry. In this regard, the present work introduces a theoretical model for the prediction of exit delamination and thrust force evolution during CFRP drilling. The results are evaluated in comparison to part delamination and thrust force data obtained from experimental drilling tests. The results indicate that the proposed model can provide valuable insight into the CFRP drilling for process optimization, enabling the aerospace industry to improve current drilling practices towards delamination free drilling of CFRP components.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 185-190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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