Pub Date : 2025-11-28DOI: 10.1016/j.cirpj.2025.11.012
Hagen Klippel , Matthias Röthlin , Mohamadreza Afrasiabi , Michal Kuffa , Konrad Wegener
Determining material properties for machining simulations is challenging because direct measurement methods cannot reproduce the conditions of machining. Instead, an inverse parameter identification is used in this work to determine the material parameters for the Johnson-Cook model for Ti6Al4V (3.7165, Grade 5). A numerical simulation model using the smoothed particle hydrodynamics code mfree_iwf is used to recalculate an orthogonal cutting experiment. Due to GPU-acceleration the computational time is less than 5 min per simulation. Three different optimization algorithms (Simplex, Bayes, Differential Evolution) are used for the identification of the material parameters by minimizing the process force errors between experiment and simulation. Best results are obtained with the Differential Evolution algorithm. The sensitivity of material model parameters to the computed process force errors are shown and reveal for some of the material parameters adverse effects on these errors. Recomputations of experiments at different process conditions with the identified material parameters show good agreements in terms of process forces and the chip segmentation behaviour can be reproduced in high resolution simulations.
{"title":"Inverse identification of Johnson–Cook flow stress parameters for Ti6Al4V","authors":"Hagen Klippel , Matthias Röthlin , Mohamadreza Afrasiabi , Michal Kuffa , Konrad Wegener","doi":"10.1016/j.cirpj.2025.11.012","DOIUrl":"10.1016/j.cirpj.2025.11.012","url":null,"abstract":"<div><div>Determining material properties for machining simulations is challenging because direct measurement methods cannot reproduce the conditions of machining. Instead, an inverse parameter identification is used in this work to determine the material parameters for the Johnson-Cook model for Ti6Al4V (3.7165, Grade 5). A numerical simulation model using the smoothed particle hydrodynamics code <span><span>mfree_iwf</span><svg><path></path></svg></span> is used to recalculate an orthogonal cutting experiment. Due to GPU-acceleration the computational time is less than 5 min per simulation. Three different optimization algorithms (Simplex, Bayes, Differential Evolution) are used for the identification of the material parameters by minimizing the process force errors between experiment and simulation. Best results are obtained with the Differential Evolution algorithm. The sensitivity of material model parameters to the computed process force errors are shown and reveal for some of the material parameters adverse effects on these errors. Recomputations of experiments at different process conditions with the identified material parameters show good agreements in terms of process forces and the chip segmentation behaviour can be reproduced in high resolution simulations.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"64 ","pages":"Pages 15-31"},"PeriodicalIF":5.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a prediction model for the three-dimensional (3D) geometry of multi-layer single-track (wall-deposition) workpieces in Wire Arc Additive Manufacturing (WAAM), enabling accurate predictions with minimal experimental effort. The model extends a prior single-bead prediction model by incorporating four key enhancements: (1) using multiple cross-sections to capture the full wall geometry, (2) integration of additional input parameters to account for thermal history and deposition sequence, (3) development of an improved geometric-characterization function for better representation of wall geometry, and (4) employing a hybrid dataset composed of synthetic and experimental datasets acquired without specialized equipment, such as in-process geometry measurement systems, thereby simplifying the data collection process. A two-step transfer learning strategy was employed to pretrain the model on a synthetic dataset and subsequently train it using an experimental dataset. This approach enables accurate predictions, even when only a limited amount of experimental data is available. Compared with baseline models without transfer learning, the developed model achieved a substantial reduction in prediction errors, averaging improvements between 5–30 %. Specifically, it attained an error of approximately 10 % for height predictions and 15 % for width predictions. These contributions enhance the adaptability and scalability of the WAAM processes, thereby enabling more efficient and precise manufacturing.
{"title":"3D geometry prediction for wall deposition using transfer learning in wire arc additive manufacturing","authors":"Hayato Kitagawa , Talash Malek , Daisuke Kono , Berend Denkena","doi":"10.1016/j.cirpj.2025.11.007","DOIUrl":"10.1016/j.cirpj.2025.11.007","url":null,"abstract":"<div><div>This paper introduces a prediction model for the three-dimensional (3D) geometry of multi-layer single-track (wall-deposition) workpieces in Wire Arc Additive Manufacturing (WAAM), enabling accurate predictions with minimal experimental effort. The model extends a prior single-bead prediction model by incorporating four key enhancements: (1) using multiple cross-sections to capture the full wall geometry, (2) integration of additional input parameters to account for thermal history and deposition sequence, (3) development of an improved geometric-characterization function for better representation of wall geometry, and (4) employing a hybrid dataset composed of synthetic and experimental datasets acquired without specialized equipment, such as in-process geometry measurement systems, thereby simplifying the data collection process. A two-step transfer learning strategy was employed to pretrain the model on a synthetic dataset and subsequently train it using an experimental dataset. This approach enables accurate predictions, even when only a limited amount of experimental data is available. Compared with baseline models without transfer learning, the developed model achieved a substantial reduction in prediction errors, averaging improvements between 5–30 %. Specifically, it attained an error of approximately 10 % for height predictions and 15 % for width predictions. These contributions enhance the adaptability and scalability of the WAAM processes, thereby enabling more efficient and precise manufacturing.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"64 ","pages":"Pages 1-14"},"PeriodicalIF":5.4,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.cirpj.2025.11.006
Peiyuan Su , Chung-Yu Tai, Yusuf Altintas
Tool tip frequency response functions (FRFs) are fundamental to predicting stability lobe diagrams and mitigating chatter in machining operations. This study introduces a hybrid framework that integrates physics-based modeling with data-driven learning to reduce approximation errors in tool holder–tool geometries and mitigate uncertainties in their contact parameters. The tools and tool holders are modeled using a Timoshenko beam-based finite element formulation and assembled as free-free structures via receptance coupling substructure analysis (RCSA). Uncertainties in the elastic modulus, Poisson’s ratio, and density of the tool and holder materials are minimized by aligning the measured and simulated natural frequencies of representative tool and holder samples. Neural network models are pre-trained using simulated FRFs with approximate contact parameters and subsequently fine-tuned through a limited number of experimental free-free impact tests on holder–tool assemblies. The optimized contact parameters are then archived in the database for each holder type. The finite element models of the tools and holders are coupled using the tuned contact parameters and subsequently assembled with the stored spindle model via RCSA. The proposed hybrid approach is experimentally validated through impact testing of diverse holder–tool configurations mounted on machine tools. The resulting methodology contributes to the establishment of a robust digital machine tool database, thereby facilitating more reliable stability predictions and enabling enhanced productivity in NC part programming within CAM systems.
{"title":"Hybrid neural network framework for predicting tool tip dynamics via receptance coupling","authors":"Peiyuan Su , Chung-Yu Tai, Yusuf Altintas","doi":"10.1016/j.cirpj.2025.11.006","DOIUrl":"10.1016/j.cirpj.2025.11.006","url":null,"abstract":"<div><div>Tool tip frequency response functions (FRFs) are fundamental to predicting stability lobe diagrams and mitigating chatter in machining operations. This study introduces a hybrid framework that integrates physics-based modeling with data-driven learning to reduce approximation errors in tool holder–tool geometries and mitigate uncertainties in their contact parameters. The tools and tool holders are modeled using a Timoshenko beam-based finite element formulation and assembled as free-free structures via receptance coupling substructure analysis (RCSA). Uncertainties in the elastic modulus, Poisson’s ratio, and density of the tool and holder materials are minimized by aligning the measured and simulated natural frequencies of representative tool and holder samples. Neural network models are pre-trained using simulated FRFs with approximate contact parameters and subsequently fine-tuned through a limited number of experimental free-free impact tests on holder–tool assemblies. The optimized contact parameters are then archived in the database for each holder type. The finite element models of the tools and holders are coupled using the tuned contact parameters and subsequently assembled with the stored spindle model via RCSA. The proposed hybrid approach is experimentally validated through impact testing of diverse holder–tool configurations mounted on machine tools. The resulting methodology contributes to the establishment of a robust digital machine tool database, thereby facilitating more reliable stability predictions and enabling enhanced productivity in NC part programming within CAM systems.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 566-581"},"PeriodicalIF":5.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-15DOI: 10.1016/j.cirpj.2025.11.005
Adriana Neag , Tudor Balan
The work-hardening curve of sheet metals under large plastic strains can be extracted from the Plane Strain Compression Test (PSCT) using an analytical method that relies on several simplifying assumptions and correction factors (friction, boundary conditions, lateral spreading, tool geometry, yield criterion, anisotropy). This study rigorously assesses each of these correction factors using finite element simulations. Synthetic materials with predefined hardening laws are used to enable direct comparison between the reference curves and those extracted from simulated PSCTs. Dedicated simulation setups were developed to isolate the effect of each factor through progressive 2D and 3D configurations. The results show that the analytical method is generally valid when appropriate corrections are applied, with improved accuracy observed when using rounded tools with small radii under low-friction conditions. Recommendations for the selection of correction factors are provided to enhance the reliability of flow curves obtained through this method.
{"title":"Verification of flow curve determination from plane strain compression tests","authors":"Adriana Neag , Tudor Balan","doi":"10.1016/j.cirpj.2025.11.005","DOIUrl":"10.1016/j.cirpj.2025.11.005","url":null,"abstract":"<div><div>The work-hardening curve of sheet metals under large plastic strains can be extracted from the Plane Strain Compression Test (PSCT) using an analytical method that relies on several simplifying assumptions and correction factors (friction, boundary conditions, lateral spreading, tool geometry, yield criterion, anisotropy). This study rigorously assesses each of these correction factors using finite element simulations. Synthetic materials with predefined hardening laws are used to enable direct comparison between the reference curves and those extracted from simulated PSCTs. Dedicated simulation setups were developed to isolate the effect of each factor through progressive 2D and 3D configurations. The results show that the analytical method is generally valid when appropriate corrections are applied, with improved accuracy observed when using rounded tools with small radii under low-friction conditions. Recommendations for the selection of correction factors are provided to enhance the reliability of flow curves obtained through this method.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 554-565"},"PeriodicalIF":5.4,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.cirpj.2025.11.003
Wei Yang , Xiaogang Wang , Yuewei Bai , Xinlin Zhou , Jianwei Song , Hua Mu
The 3D process model of a part is a series of solid models that reflect the process of transforming a blank into a finished part through specific machining steps. The rapid and automated generation of 3D process models is essential for establishing the digital thread that seamlessly integrates data, models, and processes across the entire product lifecycle. Traditional process models are typically created manually or generated as the by-products of CAM outputs. These approaches are inefficient, prone to errors, and often disconnected from the process route, which further widens the digital divide across various stages of the product lifecycle. To address these issues, this paper proposed a method for generating 3D process models based on machining zone contour. First, based on the part model and the manufacturing process specifications, the machining zones of each machining step are identified as the Boolean difference between the blank model and the corresponding cutting bodies for that step. Next, following the hierarchical decomposition of the machining zones, the corresponding machining zone contours are derived from the geometric structure and material boundaries of each individual machining zone. Finally, these contours are employed in simple modeling operations to construct the cutting bodies, thereby enabling the automatic and rapid generation of process models. Case studies show that, compared with existing methods, the proposed approach improves process model generation efficiency by more than 37 %, ensures high accuracy through strong consistency with process planning, and is applicable to both rotationally and non-rotationally machining parts. In digital manufacturing, this method can provide models for CAM toolpath and subsequent production guidance, thereby facilitating the integration of CAD, CAPP, and CAM.
{"title":"A machining zone contour-based method of automatic 3D process model generation","authors":"Wei Yang , Xiaogang Wang , Yuewei Bai , Xinlin Zhou , Jianwei Song , Hua Mu","doi":"10.1016/j.cirpj.2025.11.003","DOIUrl":"10.1016/j.cirpj.2025.11.003","url":null,"abstract":"<div><div>The 3D process model of a part is a series of solid models that reflect the process of transforming a blank into a finished part through specific machining steps. The rapid and automated generation of 3D process models is essential for establishing the digital thread that seamlessly integrates data, models, and processes across the entire product lifecycle. Traditional process models are typically created manually or generated as the by-products of CAM outputs. These approaches are inefficient, prone to errors, and often disconnected from the process route, which further widens the digital divide across various stages of the product lifecycle. To address these issues, this paper proposed a method for generating 3D process models based on machining zone contour. First, based on the part model and the manufacturing process specifications, the machining zones of each machining step are identified as the Boolean difference between the blank model and the corresponding cutting bodies for that step. Next, following the hierarchical decomposition of the machining zones, the corresponding machining zone contours are derived from the geometric structure and material boundaries of each individual machining zone. Finally, these contours are employed in simple modeling operations to construct the cutting bodies, thereby enabling the automatic and rapid generation of process models. Case studies show that, compared with existing methods, the proposed approach improves process model generation efficiency by more than 37 %, ensures high accuracy through strong consistency with process planning, and is applicable to both rotationally and non-rotationally machining parts. In digital manufacturing, this method can provide models for CAM toolpath and subsequent production guidance, thereby facilitating the integration of CAD, CAPP, and CAM.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 533-553"},"PeriodicalIF":5.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.cirpj.2025.11.004
Michal Straka , Martin Mareš , Otakar Horejš , Matěj Sulitka , Soyeong Je , Hyeok Kim , Chang-Ju Kim
Machine tool (MT) thermal errors induced by external and internal heat sources are important elements in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors due to their economic and ecological advantages. As the sensory equipment of MTs increases, thermal error models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into the model structures. However, adaptive functionalities require discrete interruptions of the MT’s work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. This research investigates the applicability and validity of touch trigger probe (TTP) measurements for thermal error evaluation on MTs, comparing it with established reference methods. The study examines various operational conditions including no-load, idle heating and machining processes, with particular focus on the method's potential integration into adaptive thermal error compensation systems. Another goal of the paper is to emphasise the need for quality input information for modelling efforts and the industrial applicability of scientific results. Eight experiments were performed on two vertical 5axis milling centres (MT1, MT2) under no-load, idle heating, dry and wet machining, and climate chamber conditions. Results showed that TTP measurements were sufficiently consistent with reference methods under no-load and cool-down transition phases. In spindle idle heating, TTP exhibited nonlinear deviations up to 25 % during the transient part of the behaviour. In case of dry machining, TTP showed a linear deviation of ∼19 % compared to the reference method, which is correctable by a scalar factor. Under wet machining, deviations were negligible due to the homogenising effect of the cutting fluid. Climate chamber tests further confirmed strong ambient temperature dependence and increased error with multiple datum ball (DB) cycles. The study was limited to thermal displacements in the Z-axis. Findings demonstrate that while TTP is not universally reliable, it provides a valuable, industry-relevant approach for updating thermal error models if it is applied in suitable scenarios excluding method's limitations.
{"title":"Study on applicability of touch trigger probes in issues of on-machine measurement of machine tool thermal errors","authors":"Michal Straka , Martin Mareš , Otakar Horejš , Matěj Sulitka , Soyeong Je , Hyeok Kim , Chang-Ju Kim","doi":"10.1016/j.cirpj.2025.11.004","DOIUrl":"10.1016/j.cirpj.2025.11.004","url":null,"abstract":"<div><div>Machine tool (MT) thermal errors induced by external and internal heat sources are important elements in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors due to their economic and ecological advantages. As the sensory equipment of MTs increases, thermal error models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into the model structures. However, adaptive functionalities require discrete interruptions of the MT’s work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. This research investigates the applicability and validity of touch trigger probe (TTP) measurements for thermal error evaluation on MTs, comparing it with established reference methods. The study examines various operational conditions including no-load, idle heating and machining processes, with particular focus on the method's potential integration into adaptive thermal error compensation systems. Another goal of the paper is to emphasise the need for quality input information for modelling efforts and the industrial applicability of scientific results. Eight experiments were performed on two vertical 5axis milling centres (MT1, MT2) under no-load, idle heating, dry and wet machining, and climate chamber conditions. Results showed that TTP measurements were sufficiently consistent with reference methods under no-load and cool-down transition phases. In spindle idle heating, TTP exhibited nonlinear deviations up to 25 % during the transient part of the behaviour. In case of dry machining, TTP showed a linear deviation of ∼19 % compared to the reference method, which is correctable by a scalar factor. Under wet machining, deviations were negligible due to the homogenising effect of the cutting fluid. Climate chamber tests further confirmed strong ambient temperature dependence and increased error with multiple datum ball (DB) cycles. The study was limited to thermal displacements in the <em>Z</em>-axis. Findings demonstrate that while TTP is not universally reliable, it provides a valuable, industry-relevant approach for updating thermal error models if it is applied in suitable scenarios excluding method's limitations.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 522-532"},"PeriodicalIF":5.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1016/j.cirpj.2025.11.002
P. Nunes , J. Santos , E. Rocha
Accurately predicting the remaining useful life (RUL) is essential for effective prognostics and health management, enabling optimal maintenance scheduling. However, the black-box nature of many deep learning models hinders their adoption by maintenance practitioners. This study presents k-LSTM-GFT, a novel hybrid framework that integrates automated data-driven generalized fault tree (GFT) construction with a k-fold ensemble of LSTM models to enhance both interpretability and prediction accuracy. Unlike prior work, our method automatically constructs fault trees from sensor data, simultaneously enabling interpretable failure pathway analysis and enriching temporal features for RUL estimation, a unified integration that constitutes a key contribution of this work. Experimental results on benchmark datasets (C-MAPSS and N-CMAPSS) show that k-LSTM-GFT outperforms state-of-the-art models, reducing RMSE by over 18% in multi-fault scenarios. Validation on a real-world injection molding machine dataset further confirms its industrial applicability. By combining model transparency with robust performance, k-LSTM-GFT offers a practical solution tailored to the needs of maintenance practitioners.
{"title":"Combining generalized fault trees and k-LSTM ensembles for enhancing prognostics and health management","authors":"P. Nunes , J. Santos , E. Rocha","doi":"10.1016/j.cirpj.2025.11.002","DOIUrl":"10.1016/j.cirpj.2025.11.002","url":null,"abstract":"<div><div>Accurately predicting the remaining useful life (RUL) is essential for effective prognostics and health management, enabling optimal maintenance scheduling. However, the black-box nature of many deep learning models hinders their adoption by maintenance practitioners. This study presents k-LSTM-GFT, a novel hybrid framework that integrates automated data-driven generalized fault tree (GFT) construction with a k-fold ensemble of LSTM models to enhance both interpretability and prediction accuracy. Unlike prior work, our method automatically constructs fault trees from sensor data, simultaneously enabling interpretable failure pathway analysis and enriching temporal features for RUL estimation, a unified integration that constitutes a key contribution of this work. Experimental results on benchmark datasets (C-MAPSS and N-CMAPSS) show that k-LSTM-GFT outperforms state-of-the-art models, reducing RMSE by over 18% in multi-fault scenarios. Validation on a real-world injection molding machine dataset further confirms its industrial applicability. By combining model transparency with robust performance, k-LSTM-GFT offers a practical solution tailored to the needs of maintenance practitioners.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 505-521"},"PeriodicalIF":5.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the shift in the thermal process limit for CBN compared to corundum abrasives as a function of specific grinding power and contact time during surface and cylindrical grinding. The results indicate that the use of CBN significantly reduces the thermal load, as demonstrated by metallographic cross-sections, residual stress depth profiles and hardness measurements. An empirical function is derived to calculate the depth of tempering zones. This enables a quantifiable comparison of CBN and corundum in terms of their thermal impact and provides a practical basis for the targeted and thermally stable design of multi-stage grinding processes.
{"title":"Comprehensive study of grinding burn limit and subsurface modifications in grinding with CBN and corundum abrasives","authors":"Gerrit Kuhlmann , Nikolai Guba , Tobias Hüsemann , Carsten Heinzel","doi":"10.1016/j.cirpj.2025.11.001","DOIUrl":"10.1016/j.cirpj.2025.11.001","url":null,"abstract":"<div><div>This study investigates the shift in the thermal process limit for CBN compared to corundum abrasives as a function of specific grinding power and contact time during surface and cylindrical grinding. The results indicate that the use of CBN significantly reduces the thermal load, as demonstrated by metallographic cross-sections, residual stress depth profiles and hardness measurements. An empirical function is derived to calculate the depth of tempering zones. This enables a quantifiable comparison of CBN and corundum in terms of their thermal impact and provides a practical basis for the targeted and thermally stable design of multi-stage grinding processes.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 490-504"},"PeriodicalIF":5.4,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-06DOI: 10.1016/j.cirpj.2025.10.009
Swarit Anand Singh, K.A. Desai
Achieving Zero Defect Manufacturing (ZDM) during the mass production of critical components, such as tapered rollers, requires 100% in-line inspection to ensure the supply of defect-free products to consumers. Integrating vision systems with the production line can enable 100% in-line surface inspection of mass-manufactured components, facilitating the realization of ZDM. This work presents a vision-based system designed for integration with a tapered roller production line to detect surface defects. The system features indigenously designed hardware elements for in-line image acquisition, a hybrid algorithm combining image processing and deep learning to detect defective rollers, and seamless synchronization with the production line through an interactive user interface. The image acquisition hardware comprises a single-camera system and a horizontal belt conveyor for controlled translation and rotation of tapered rollers. The image processing framework encompasses algorithms for Region of Interest (ROI) detection, noise removal, and Convolutional Neural Network (CNN)-based surface defect detection, enabling robust prediction capabilities. The developed system is comprehensively evaluated through a performance analysis and Strengths, Weaknesses, Opportunities, and Threats (SWOT) assessment. The outcomes of the present study demonstrate that the vision-based system can be effectively integrated with the tapered roller manufacturing line to achieve reliability and efficacy during in-line inspections. The study also demonstrated that vision-based inspection systems can be implemented to achieve ZDM, enabling manufacturing industries to improve product quality and enhance global competitiveness.
{"title":"Integrated vision-based in-line surface defect detection system for realizing zero-defect manufacturing of tapered rollers","authors":"Swarit Anand Singh, K.A. Desai","doi":"10.1016/j.cirpj.2025.10.009","DOIUrl":"10.1016/j.cirpj.2025.10.009","url":null,"abstract":"<div><div>Achieving Zero Defect Manufacturing (ZDM) during the mass production of critical components, such as tapered rollers, requires 100% in-line inspection to ensure the supply of defect-free products to consumers. Integrating vision systems with the production line can enable 100% in-line surface inspection of mass-manufactured components, facilitating the realization of ZDM. This work presents a vision-based system designed for integration with a tapered roller production line to detect surface defects. The system features indigenously designed hardware elements for in-line image acquisition, a hybrid algorithm combining image processing and deep learning to detect defective rollers, and seamless synchronization with the production line through an interactive user interface. The image acquisition hardware comprises a single-camera system and a horizontal belt conveyor for controlled translation and rotation of tapered rollers. The image processing framework encompasses algorithms for Region of Interest (ROI) detection, noise removal, and Convolutional Neural Network (CNN)-based surface defect detection, enabling robust prediction capabilities. The developed system is comprehensively evaluated through a performance analysis and Strengths, Weaknesses, Opportunities, and Threats (SWOT) assessment. The outcomes of the present study demonstrate that the vision-based system can be effectively integrated with the tapered roller manufacturing line to achieve reliability and efficacy during in-line inspections. The study also demonstrated that vision-based inspection systems can be implemented to achieve ZDM, enabling manufacturing industries to improve product quality and enhance global competitiveness.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 475-489"},"PeriodicalIF":5.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1016/j.cirpj.2025.10.008
Shun Liu , Xueming Du , Yang Xiang , Sun Jin
Circumferential topography is a key indicator of the machining quality of ring-shaped workpieces, and it is generally affected by multiple geometric and location error sources arising from the machine tool–fixture–workpiece system during the machining process. To enable effective control of surface quality under these compounded errors, an integrated 3D surface topography prediction model is formulated. In the proposed model, the combined motion errors of the workpiece and tool, the surface topography blank errors of the workpiece, and the alignment deviations in ring positioning using a quick-change clamping system are simultaneously represented within a unified framework. The effects of geometric errors in the machine tool, fixture, tool and workpiece on the machined surface topography are simulated using the equivalent error transmission chain of a multibody machining system. The topography deviations of matching features in the machining system and the workpiece alignment deviations of the clamping system are incorporated via a location deviation simulation algorithm. The circumferential surface topography is then reconstructed over the entire tool trajectory of the turning process. The simulation and experimental results indicate that the proposed model can effectively predict the 3D surface topography in auto-located turning of cylindrical thin-walled parts, which are typically affected by multiple geometric and location deviations, and offer theoretical guidance for surface topography control.
{"title":"Integration modeling of surface topography for machining ring-shaped workpiece considering multiple geometric and location error sources","authors":"Shun Liu , Xueming Du , Yang Xiang , Sun Jin","doi":"10.1016/j.cirpj.2025.10.008","DOIUrl":"10.1016/j.cirpj.2025.10.008","url":null,"abstract":"<div><div>Circumferential topography is a key indicator of the machining quality of ring-shaped workpieces, and it is generally affected by multiple geometric and location error sources arising from the machine tool–fixture–workpiece system during the machining process. To enable effective control of surface quality under these compounded errors, an integrated 3D surface topography prediction model is formulated. In the proposed model, the combined motion errors of the workpiece and tool, the surface topography blank errors of the workpiece, and the alignment deviations in ring positioning using a quick-change clamping system are simultaneously represented within a unified framework. The effects of geometric errors in the machine tool, fixture, tool and workpiece on the machined surface topography are simulated using the equivalent error transmission chain of a multibody machining system. The topography deviations of matching features in the machining system and the workpiece alignment deviations of the clamping system are incorporated via a location deviation simulation algorithm. The circumferential surface topography is then reconstructed over the entire tool trajectory of the turning process. The simulation and experimental results indicate that the proposed model can effectively predict the 3D surface topography in auto-located turning of cylindrical thin-walled parts, which are typically affected by multiple geometric and location deviations, and offer theoretical guidance for surface topography control.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 457-474"},"PeriodicalIF":5.4,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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