Pub Date : 2025-09-26DOI: 10.1016/j.cirpj.2025.09.001
Thomas Jacquet, Jean-Baptiste Guyon, Fabien Viprey, Guillaume Fromentin, David Prat
In modern manufacturing, accurately predicting cutting forces is essential for the design and control of machining operations. Common mechanistic models of cutting forces rely on a precise description of the local uncut chip area. However, in milling, the specific trajectories of cutting edges create challenges in modelling this quantity. Existing analytical models are typically limited to 2D contexts or assume circular tooth trajectories, which are mostly valid for cylindrical end mills. These assumptions limit their applicability to high-feed milling, especially due to low lead angles and complex insert cutter geometries producing non-circular paths. This article presents a new three-dimensional analytical model for evaluating the local uncut chip thickness in high-feed milling. It relies on closed-form expressions derived from geometric analysis and Taylor expansions to approximate the uncut chip area and cutter-workpiece engagement, even in regions where conventional models fail. The model applies to linear-path milling and accounts for tool run-out and differential pitch. Compared to a Newton–Raphson numerical method, it achieves a relative error below 5% while being 3 to 9 times faster, enabling efficient integration in force models. Beyond its computational efficiency, the explicit formulation enables analysis of geometric influence, such as sensitivity to feed per tooth or tooth count-capabilities not easily accessible with purely numerical approaches. This work contributes a rigorous and interpretable alternative for improving cutting force prediction in high-feed milling.
{"title":"Contribution to the analytical determination of uncut chip thickness for cutting force modelling in milling with refinements for high-feed milling","authors":"Thomas Jacquet, Jean-Baptiste Guyon, Fabien Viprey, Guillaume Fromentin, David Prat","doi":"10.1016/j.cirpj.2025.09.001","DOIUrl":"10.1016/j.cirpj.2025.09.001","url":null,"abstract":"<div><div>In modern manufacturing, accurately predicting cutting forces is essential for the design and control of machining operations. Common mechanistic models of cutting forces rely on a precise description of the local uncut chip area. However, in milling, the specific trajectories of cutting edges create challenges in modelling this quantity. Existing analytical models are typically limited to 2D contexts or assume circular tooth trajectories, which are mostly valid for cylindrical end mills. These assumptions limit their applicability to high-feed milling, especially due to low lead angles and complex insert cutter geometries producing non-circular paths. This article presents a new three-dimensional analytical model for evaluating the local uncut chip thickness in high-feed milling. It relies on closed-form expressions derived from geometric analysis and Taylor expansions to approximate the uncut chip area and cutter-workpiece engagement, even in regions where conventional models fail. The model applies to linear-path milling and accounts for tool run-out and differential pitch. Compared to a Newton–Raphson numerical method, it achieves a relative error below 5% while being 3 to 9 times faster, enabling efficient integration in force models. Beyond its computational efficiency, the explicit formulation enables analysis of geometric influence, such as sensitivity to feed per tooth or tooth count-capabilities not easily accessible with purely numerical approaches. This work contributes a rigorous and interpretable alternative for improving cutting force prediction in high-feed milling.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 240-264"},"PeriodicalIF":5.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159330","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}
Accurate multilayer overlay alignment in photolithography is critical for semiconductor manufacturing. It is crucial to use a limited number of measurement markers to ensure the throughput while maintaining the overlay estimation and control accuracy. This work presents a novel optimization framework for dynamically down-selecting overlay measurement markers. The framework employs a stochastic multilayer control algorithm for tractable real-time control and select an optimal subset of markers that maximize overlay error estimation accuracy. The optimal marker number is determined by maximizing an objective that balances production quality and throughput. Industrial evaluation in a 300 mm fab demonstrates substantial cost-benefit improvements over traditional Run-to-Run control, highlighting enhanced process efficiency and yield.
{"title":"Dynamic decision-making on the number and selection of measurement markers for stochastic control of overlay errors in photolithography","authors":"Yangmeng Li , Huidong Zhang , Noah Graff , Roberto Dailey , Dragan Djurdjanovic","doi":"10.1016/j.cirpj.2025.09.008","DOIUrl":"10.1016/j.cirpj.2025.09.008","url":null,"abstract":"<div><div>Accurate multilayer overlay alignment in photolithography is critical for semiconductor manufacturing. It is crucial to use a limited number of measurement markers to ensure the throughput while maintaining the overlay estimation and control accuracy. This work presents a novel optimization framework for dynamically down-selecting overlay measurement markers. The framework employs a stochastic multilayer control algorithm for tractable real-time control and select an optimal subset of markers that maximize overlay error estimation accuracy. The optimal marker number is determined by maximizing an objective that balances production quality and throughput. Industrial evaluation in a 300 mm fab demonstrates substantial cost-benefit improvements over traditional Run-to-Run control, highlighting enhanced process efficiency and yield.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 227-239"},"PeriodicalIF":5.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159467","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 : 2025-09-24DOI: 10.1016/j.cirpj.2025.09.014
Berend Denkena, Benjamin Bergmann, Henning Buhl, Miriam Handrup
Geometry and hardness fluctuations of formed blanks are challenging for process monitoring of the subsequent machining process because they lead to deviating process forces during roughing. Ordinary anomaly detection methods require the forces to be similar. In this work, a similarity-based anomaly detection method is proposed that utilizes Dynamic Time Warping to achieve robustness against blank fluctuations during roughing. It extracts the average signal shape from training signals, scales it individually for each novel process, and uses confidence limits for anomaly detection. The method is tested with multi-material shafts whose blank diameter is axially constant but varies between workpieces.
{"title":"Similarity-based anomaly detection method for turning of multi-material workpieces with varying axially constant blank diameter","authors":"Berend Denkena, Benjamin Bergmann, Henning Buhl, Miriam Handrup","doi":"10.1016/j.cirpj.2025.09.014","DOIUrl":"10.1016/j.cirpj.2025.09.014","url":null,"abstract":"<div><div>Geometry and hardness fluctuations of formed blanks are challenging for process monitoring of the subsequent machining process because they lead to deviating process forces during roughing. Ordinary anomaly detection methods require the forces to be similar. In this work, a similarity-based anomaly detection method is proposed that utilizes Dynamic Time Warping to achieve robustness against blank fluctuations during roughing. It extracts the average signal shape from training signals, scales it individually for each novel process, and uses confidence limits for anomaly detection. The method is tested with multi-material shafts whose blank diameter is axially constant but varies between workpieces.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 205-213"},"PeriodicalIF":5.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159328","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 : 2025-09-24DOI: 10.1016/j.cirpj.2025.09.015
Yakun Yang, Mingming Lu, Jieqiong Lin, Yongsheng Du
The processing stability and properties of magnetorheological polishing device (MPD) play a crucial role in the processing of optical materials. In this study, a novel MPD was developed to improve the processing stability and properties. The device uses free abrasives to assist in magnetorheological polishing, and completes the self-sharpening of the abrasives in flexible pad using a dynamic magnetic field. This paper presents the principles and structures design involved. The mechanical characteristics of main components and magnetic field characteristics of a Halbach array were analyzed. Based on the developed device, the stability is studied. The advantages of free abrasive assisted magnetorheological polishing method were investigated. The results indicate that the structural design of the main components is reasonable. A dynamic magnetic field device can achieve greater changes in magnetic field intensity and gradient with fewer magnets. It exhibits excellent magnetic field properties. The results obtained by marathon experiment under the same parameters are all distributed within 95 % confidence interval. The processing stability of the MPD was verified. The method can effectively improve the processing performance and has certain advantages. Compared with the traditional magnetorheological polishing method, the processing efficiency can be improved by more than 29.68 %.
{"title":"Free abrasive assisted magnetorheological polishing: Device design and processing performance analysis","authors":"Yakun Yang, Mingming Lu, Jieqiong Lin, Yongsheng Du","doi":"10.1016/j.cirpj.2025.09.015","DOIUrl":"10.1016/j.cirpj.2025.09.015","url":null,"abstract":"<div><div>The processing stability and properties of magnetorheological polishing device (MPD) play a crucial role in the processing of optical materials. In this study, a novel MPD was developed to improve the processing stability and properties. The device uses free abrasives to assist in magnetorheological polishing, and completes the self-sharpening of the abrasives in flexible pad using a dynamic magnetic field. This paper presents the principles and structures design involved. The mechanical characteristics of main components and magnetic field characteristics of a Halbach array were analyzed. Based on the developed device, the stability is studied. The advantages of free abrasive assisted magnetorheological polishing method were investigated. The results indicate that the structural design of the main components is reasonable. A dynamic magnetic field device can achieve greater changes in magnetic field intensity and gradient with fewer magnets. It exhibits excellent magnetic field properties. The results obtained by marathon experiment under the same parameters are all distributed within 95 % confidence interval. The processing stability of the MPD was verified. The method can effectively improve the processing performance and has certain advantages. Compared with the traditional magnetorheological polishing method, the processing efficiency can be improved by more than 29.68 %.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 214-226"},"PeriodicalIF":5.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159329","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 : 2025-09-23DOI: 10.1016/j.cirpj.2025.09.013
Lihua He, Jinhui Zhou, Bokai Lou, Jing Ni, Xiaoping Hu
Most safety-critical components and load-bearing structures continue to be manufactured using hard turning, a process that induces gradient nanostructures (GNS) in the surface layer. To investigate the effect of GNS layer on fatigue properties, crystal plasticity finite element model (CPFEM) and ± 0.8 % strain fatigue test were used in this study. The objectives were to investigate the correlation between turning parameters and surface GNS layer of 316 L stainless steel, and to reveal the fatigue failure mechanism of GNS layer from multiple scales. The results show that the turning parameters significantly influence the thickness of the GNS layer, with turning depth having the greatest impact, followed by cutting speed. CPFEM simulations predict stress distribution within the GNS layer across regions with varying grain sizes. stresses in fine-grained regions are primarily concentrated at grain boundaries, whereas stresses in coarse-grained regions are distributed within the grains. The model predictions of fatigue crack locations closely align with stress concentration distributions. Fatigue testing reveals that cracks in the GNS layer primarily propagate intergranular boundaries, while cracks in the coarse-grained (CG) layer exhibit both intergranular and transgranular extensions. This behavior mirrors the damage patterns predicted by simulation, demonstrating the model's high accuracy.
{"title":"Fatigue failure mechanism of gradient nanostructured materials produced by turning","authors":"Lihua He, Jinhui Zhou, Bokai Lou, Jing Ni, Xiaoping Hu","doi":"10.1016/j.cirpj.2025.09.013","DOIUrl":"10.1016/j.cirpj.2025.09.013","url":null,"abstract":"<div><div>Most safety-critical components and load-bearing structures continue to be manufactured using hard turning, a process that induces gradient nanostructures (GNS) in the surface layer. To investigate the effect of GNS layer on fatigue properties, crystal plasticity finite element model (CPFEM) and ± 0.8 % strain fatigue test were used in this study. The objectives were to investigate the correlation between turning parameters and surface GNS layer of 316 L stainless steel, and to reveal the fatigue failure mechanism of GNS layer from multiple scales. The results show that the turning parameters significantly influence the thickness of the GNS layer, with turning depth having the greatest impact, followed by cutting speed. CPFEM simulations predict stress distribution within the GNS layer across regions with varying grain sizes. stresses in fine-grained regions are primarily concentrated at grain boundaries, whereas stresses in coarse-grained regions are distributed within the grains. The model predictions of fatigue crack locations closely align with stress concentration distributions. Fatigue testing reveals that cracks in the GNS layer primarily propagate intergranular boundaries, while cracks in the coarse-grained (CG) layer exhibit both intergranular and transgranular extensions. This behavior mirrors the damage patterns predicted by simulation, demonstrating the model's high accuracy.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 156-169"},"PeriodicalIF":5.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120889","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 : 2025-09-23DOI: 10.1016/j.cirpj.2025.08.010
Deepak Kumar, Sunil Jha
WA-DED using CMT is emerging as a high-throughput metal AM strategy, yet it remains susceptible to a variety of thermomechanical instabilities and metallurgical discontinuities. In this study, we present an advanced AE based in-situ monitoring utilizing the generative ML framework to robustly detect and characterize anomalous conditions that compromise part integrity. Specifically, we examine five critical fault scenarios which are overcurrent, high travel speed, insufficient shielding gas flow, combination of overcurrent and low shielding gas flow rate and combination of high travel speed and low shielding gas flow rate elucidate their distinct signatures in the acoustic domain. A rigorous selection of time and frequency domain descriptors is leveraged to train the variational autoencoder, enabling accurate reconstruction of normal process states and efficient outlier detection. Microstructural analyses, encompassing FESEM, Micro-CT, and XRD, validate the detrimental influence of these faults on porosity evolution, grain morphology, and mechanical properties such as UTS. The proposed VAE model demonstrated robust performance across multiple defect types, achieving peak detection accuracies of 87% for overcurrent-induced faults, 85% for high travel speed anomalies, 81% for defects caused by insufficient shielding gas flow, 87% for combined effect of overcurrent and low gas flow rate, and 84% for combined effect of high travel speed and low gas flow rate. Overcurrent anomalies induce coarse columnar grains and high porosity content, while high travel speed amplifies geometric irregularities. Low gas flow conditions foster oxidation induced porosity. The proposed approach achieves high fidelity in detection of these defects, underscoring the synergy between data driven reconstruction errors and material characterization. By integrating unsupervised generative deep learning with domain specific interpretability through feature sensitivity analysis, this acoustic monitoring paradigm provides a scalable and cost effective pathway to detect defects and ensure structural reliability in WA-DED manufactured components. The comprehensive experimental validations and multi-physics correlational insights position this framework as a robust framework for in-situ process monitoring in WA-DED.
{"title":"Interpretable generative machine learning model based in-situ process monitoring in robotic wire arc based directed energy deposition of aluminum alloys","authors":"Deepak Kumar, Sunil Jha","doi":"10.1016/j.cirpj.2025.08.010","DOIUrl":"10.1016/j.cirpj.2025.08.010","url":null,"abstract":"<div><div>WA-DED using CMT is emerging as a high-throughput metal AM strategy, yet it remains susceptible to a variety of thermomechanical instabilities and metallurgical discontinuities. In this study, we present an advanced AE based in-situ monitoring utilizing the generative ML framework to robustly detect and characterize anomalous conditions that compromise part integrity. Specifically, we examine five critical fault scenarios which are overcurrent, high travel speed, insufficient shielding gas flow, combination of overcurrent and low shielding gas flow rate and combination of high travel speed and low shielding gas flow rate elucidate their distinct signatures in the acoustic domain. A rigorous selection of time and frequency domain descriptors is leveraged to train the variational autoencoder, enabling accurate reconstruction of normal process states and efficient outlier detection. Microstructural analyses, encompassing FESEM, Micro-CT, and XRD, validate the detrimental influence of these faults on porosity evolution, grain morphology, and mechanical properties such as UTS. The proposed VAE model demonstrated robust performance across multiple defect types, achieving peak detection accuracies of 87% for overcurrent-induced faults, 85% for high travel speed anomalies, 81% for defects caused by insufficient shielding gas flow, 87% for combined effect of overcurrent and low gas flow rate, and 84% for combined effect of high travel speed and low gas flow rate. Overcurrent anomalies induce coarse columnar grains and high porosity content, while high travel speed amplifies geometric irregularities. Low gas flow conditions foster oxidation induced porosity. The proposed approach achieves high fidelity in detection of these defects, underscoring the synergy between data driven reconstruction errors and material characterization. By integrating unsupervised generative deep learning with domain specific interpretability through feature sensitivity analysis, this acoustic monitoring paradigm provides a scalable and cost effective pathway to detect defects and ensure structural reliability in WA-DED manufactured components. The comprehensive experimental validations and multi-physics correlational insights position this framework as a robust framework for in-situ process monitoring in WA-DED.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 185-204"},"PeriodicalIF":5.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120891","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 : 2025-09-23DOI: 10.1016/j.cirpj.2025.09.011
Yanan Zhao, Shaoming Yao
This paper proposes a step-dependent machining uncertainty modeling method for the process routes. With the between-step interaction, the process route integrity is accurately interpreted and all error sources in the production environment are involved in, including the workpiece positioning surface, machine positioning surface, cutter kinetics, workpiece kinetics, clamping force, cutting force, environmental factors, residual stress distortion, heat treatment distortion, and coating/plating variations. The machining uncertainty model shows that the machining uncertainty consists of both regenerated and inherited uncertainties. The proposed modeling method can evaluate a process route in terms of its error source impact on workpiece accuracy. A herringbone gear ring is used to demonstrate its effectiveness as a case study, where an uncertainty model is developed for the full process route, and the process route is assessed before the costly process trial. The process number is reduced from 18 to 15 without affecting the final workpiece accuracy. The experiment shows a good agreement with the uncertainty model results.
{"title":"Step-dependent machining uncertainty modeling for the process route and application in the machining of the ring-gear","authors":"Yanan Zhao, Shaoming Yao","doi":"10.1016/j.cirpj.2025.09.011","DOIUrl":"10.1016/j.cirpj.2025.09.011","url":null,"abstract":"<div><div>This paper proposes a step-dependent machining uncertainty modeling method for the process routes. With the between-step interaction, the process route integrity is accurately interpreted and all error sources in the production environment are involved in, including the workpiece positioning surface, machine positioning surface, cutter kinetics, workpiece kinetics, clamping force, cutting force, environmental factors, residual stress distortion, heat treatment distortion, and coating/plating variations. The machining uncertainty model shows that the machining uncertainty consists of both regenerated and inherited uncertainties. The proposed modeling method can evaluate a process route in terms of its error source impact on workpiece accuracy. A herringbone gear ring is used to demonstrate its effectiveness as a case study, where an uncertainty model is developed for the full process route, and the process route is assessed before the costly process trial. The process number is reduced from 18 to 15 without affecting the final workpiece accuracy. The experiment shows a good agreement with the uncertainty model results.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 170-184"},"PeriodicalIF":5.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120881","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 : 2025-09-22DOI: 10.1016/j.cirpj.2025.09.010
Zhao Wang , Xiaowen Rong , Haoran Zhao , Yue Yang , Fusheng Liang , Cheng Fan
Abrasive water jet technology, as a non-traditional machining process, impinges on the workpiece surface with abrasive particles driven by the water jet beam to achieve material removal or surface modification. The abrasive particle distribution is the key factor affecting on the process quality, especially for Abrasive Water Jet Peening (AWJP) process. However, there is still limited research on the abrasive particle distribution in the AWJP process, especially regarding the distribution under variable traverse speeds and variable curvature movements of the abrasive water jet beam, which forms the basis for controlling abrasive water jet coverage, particularly on curved surfaces. In this study, an abrasive particle distribution prediction model is proposed for AWJP under different pump pressures, variable traverse speeds (accelerations), and various curvature radius by combining finite element and analytical modeling approaches. Validation experiments were conducted, and both simulation and experimental results under different parameters follow Gaussian distributions. The maximum prediction error was only 18.6 % across 24 comparisons from 15 experimental sets, confirming the feasibility and accuracy of the proposed model. Meanwhile, the influence of these three parameters on abrasive particle distribution laws is investigated respectively through comparisons between simulation and experimental results. The findings reveal that pump pressure primarily affects abrasive particle velocity and position distribution; traverse speed mainly influences abrasive particle position distribution and the percentage of particles at the central region; curvature radius predominantly affects the midline position of the abrasive particle distribution curve. This study not only provide a deep understanding of abrasive particle distribution laws under varying pump pressures, traverse speeds, and curvature radii, but the proposed model also offers valuable guidance for achieving uniform abrasive particle coverage on free-form surfaces during AWJP.
{"title":"Modelling of abrasive particle distribution for pre-mixed abrasive water jet peening surface","authors":"Zhao Wang , Xiaowen Rong , Haoran Zhao , Yue Yang , Fusheng Liang , Cheng Fan","doi":"10.1016/j.cirpj.2025.09.010","DOIUrl":"10.1016/j.cirpj.2025.09.010","url":null,"abstract":"<div><div>Abrasive water jet technology, as a non-traditional machining process, impinges on the workpiece surface with abrasive particles driven by the water jet beam to achieve material removal or surface modification. The abrasive particle distribution is the key factor affecting on the process quality, especially for Abrasive Water Jet Peening (AWJP) process. However, there is still limited research on the abrasive particle distribution in the AWJP process, especially regarding the distribution under variable traverse speeds and variable curvature movements of the abrasive water jet beam, which forms the basis for controlling abrasive water jet coverage, particularly on curved surfaces. In this study, an abrasive particle distribution prediction model is proposed for AWJP under different pump pressures, variable traverse speeds (accelerations), and various curvature radius by combining finite element and analytical modeling approaches. Validation experiments were conducted, and both simulation and experimental results under different parameters follow Gaussian distributions. The maximum prediction error was only 18.6 % across 24 comparisons from 15 experimental sets, confirming the feasibility and accuracy of the proposed model. Meanwhile, the influence of these three parameters on abrasive particle distribution laws is investigated respectively through comparisons between simulation and experimental results. The findings reveal that pump pressure primarily affects abrasive particle velocity and position distribution; traverse speed mainly influences abrasive particle position distribution and the percentage of particles at the central region; curvature radius predominantly affects the midline position of the abrasive particle distribution curve. This study not only provide a deep understanding of abrasive particle distribution laws under varying pump pressures, traverse speeds, and curvature radii, but the proposed model also offers valuable guidance for achieving uniform abrasive particle coverage on free-form surfaces during AWJP.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 135-155"},"PeriodicalIF":5.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108138","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}
Small-module gear-shaped parts (SMGSPs, module m < 1) with extreme diameter-to-module ratios (D/m>100) are critical components in miniature precision systems for spatial transmission and lightweight structural applications. However, it exhibits restricted fatigue strength and excessive material wastage when manufactured by conventional machining processes. A novel current-assisted splitting spinning forming (CASSF) method combining the precision of spinning technology with the electroplastic effects of pulsed current synergistically was proposed to realize the high-performance near-net shape forming of SMGSPs. A finite element model coupled with the electroplasticity effect is constructed. Finite element model (FEM) simulations and experimental studies systematically investigated the distribution of the electric field, temperature field, the equivalent stress and strain, and the dynamic material flow of small module gears during CASSF. The results revealed that the current density of the SMGSP is concentrated near the contact area of the roller, so the softening region, due to the electroplasticity effect, highly overlaps with the deformation region of the SMGSP. The gear profile deformation exhibits a non-uniform stress-strain distribution, with peak stress concentrations localized at the exit-side tooth root arc. The application of pulsed current effectively reduced equivalent stress and enhanced material deformability, achieving saturation thresholds at 17.5 A/mm² current density (Jp) and 40 % duty ratio (d). Five distinct material flow orientations develop during CASSF, forming four flow division surfaces between them. The uneven tooth height defect originates from asymmetric material flow between the entry and exit sides, whereas tooth underfilling stems from insufficient axial material flow. A forward-reversed forming strategy with intensified pulsed current eliminated tooth height discrepancies and improved tooth saturation (γ) to 97.8 %, demonstrating the potential of CASSF potential for forming extreme ratio SMGSPs.
{"title":"Research on the deformation mechanism for current-assisted splitting spinning forming of small-module gear-shaped parts with extreme diameter-to-module ratios","authors":"Qinxiang Xia , Haoyang Zhou , Gangfeng Xiao , Sizhu Cheng , Junhao Zhang","doi":"10.1016/j.cirpj.2025.08.012","DOIUrl":"10.1016/j.cirpj.2025.08.012","url":null,"abstract":"<div><div>Small-module gear-shaped parts (SMGSPs, module <em>m</em> < 1) with extreme diameter-to-module ratios (<em>D</em>/<em>m</em>>100) are critical components in miniature precision systems for spatial transmission and lightweight structural applications. However, it exhibits restricted fatigue strength and excessive material wastage when manufactured by conventional machining processes. A novel current-assisted splitting spinning forming (CASSF) method combining the precision of spinning technology with the electroplastic effects of pulsed current synergistically was proposed to realize the high-performance near-net shape forming of SMGSPs. A finite element model coupled with the electroplasticity effect is constructed. Finite element model (FEM) simulations and experimental studies systematically investigated the distribution of the electric field, temperature field, the equivalent stress and strain, and the dynamic material flow of small module gears during CASSF. The results revealed that the current density of the SMGSP is concentrated near the contact area of the roller, so the softening region, due to the electroplasticity effect, highly overlaps with the deformation region of the SMGSP. The gear profile deformation exhibits a non-uniform stress-strain distribution, with peak stress concentrations localized at the exit-side tooth root arc. The application of pulsed current effectively reduced equivalent stress and enhanced material deformability, achieving saturation thresholds at 17.5 A/mm² current density (<em>J</em><sub>p</sub>) and 40 % duty ratio (<em>d</em>). Five distinct material flow orientations develop during CASSF, forming four flow division surfaces between them. The uneven tooth height defect originates from asymmetric material flow between the entry and exit sides, whereas tooth underfilling stems from insufficient axial material flow. A forward-reversed forming strategy with intensified pulsed current eliminated tooth height discrepancies and improved tooth saturation (<em>γ</em>) to 97.8 %, demonstrating the potential of CASSF potential for forming extreme ratio SMGSPs.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 116-134"},"PeriodicalIF":5.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107773","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 : 2025-09-17DOI: 10.1016/j.cirpj.2025.09.006
Aswin P., Rakesh G. Mote
High aspect ratio, thin-walled miniature structures are critical in applications such as microfluidics and micromechanical cooling. Wire Electrical Discharge Machining (Wire EDM) presents a commercially viable alternative to specialized micromachining setups for fabricating such features. However, as part size decreases, conventional Wire EDM faces challenges in achieving accurate profiles due to intensified thermal effects and reduced part stiffness, leading to increased geometrical errors. To address this, a reduced-order surrogate framework based on Gaussian Process Regression (GPR) is developed to predict key geometrical deviations specifically, reduced wall thickness and wall deformation as functions of process parameters. The framework integrates four GPR models trained on hybrid datasets combining experimental data and physics-based numerical results. A discrepancy model further refines numerical predictions by accounting for deviations from experimental data. The final GPR models achieve mean absolute errors of 3.39 m and 6.08 m for wall thickness and deformation, with values of 0.96 and 0.99. K-fold cross-validation and validation experiments confirm model reliability, with prediction errors around 14.3 m and 12.1 m. The discrepancy model reduces the deviation of numerical predictions from actual values by 55%. Process parameter optimization is performed to fabricate thin walls with targeted deformation levels, achieving reasonable accuracy within 22.3 m. Furthermore, sensitivity analysis is conducted to quantify both individual and interactive influences of major process parameters on geometrical errors.
{"title":"Gaussian process-based surrogate framework for efficient prediction of geometrical inaccuracy in Wire Electrical Discharge Machining of thin-wall miniature components","authors":"Aswin P., Rakesh G. Mote","doi":"10.1016/j.cirpj.2025.09.006","DOIUrl":"10.1016/j.cirpj.2025.09.006","url":null,"abstract":"<div><div>High aspect ratio, thin-walled miniature structures are critical in applications such as microfluidics and micromechanical cooling. Wire Electrical Discharge Machining (Wire EDM) presents a commercially viable alternative to specialized micromachining setups for fabricating such features. However, as part size decreases, conventional Wire EDM faces challenges in achieving accurate profiles due to intensified thermal effects and reduced part stiffness, leading to increased geometrical errors. To address this, a reduced-order surrogate framework based on Gaussian Process Regression (GPR) is developed to predict key geometrical deviations specifically, reduced wall thickness and wall deformation as functions of process parameters. The framework integrates four GPR models trained on hybrid datasets combining experimental data and physics-based numerical results. A discrepancy model further refines numerical predictions by accounting for deviations from experimental data. The final GPR models achieve mean absolute errors of 3.39 <span><math><mi>μ</mi></math></span>m and 6.08 <span><math><mi>μ</mi></math></span>m for wall thickness and deformation, with <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 0.96 and 0.99. K-fold cross-validation and validation experiments confirm model reliability, with prediction errors around 14.3 <span><math><mi>μ</mi></math></span>m and 12.1 <span><math><mi>μ</mi></math></span>m. The discrepancy model reduces the deviation of numerical predictions from actual values by 55%. Process parameter optimization is performed to fabricate thin walls with targeted deformation levels, achieving reasonable accuracy within 22.3 <span><math><mi>μ</mi></math></span>m. Furthermore, sensitivity analysis is conducted to quantify both individual and interactive influences of major process parameters on geometrical errors.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"63 ","pages":"Pages 97-115"},"PeriodicalIF":5.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107772","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}