Pub Date : 2025-02-28DOI: 10.1016/j.jmapro.2025.02.069
Junyoung Yun , Yoonjae Lee , Minjae Kim , Jeongdai Jo , Changwoo Lee
With the increasing demand for electric vehicles, roll-to-roll manufacturing systems have emerged as a promising approach for the large-scale and cost-effective production method of Li-ion batteries. However, this process poses unique challenges, including web wrinkle defects, referring to undesired folds, creases, or distortions that occur in the flexible, thin substrate composed of copper film during anode production. Web wrinkles can occur because of strain deviation in the cross-machining direction due to the application of nonuniform tension and the differences in geometrical and material properties. This study analyzed the thermal effects on formation of web wrinkles with respect to operating conditions (coating thickness, web speed, and web tension) and suggested guidelines for battery anode production to ensure optimal operating conditions through a series of simulations followed by experimental verification. A simulation-based analysis was performed, and a copper film and anode material were utilized for experimental verification. Excessive wrinkling was observed in cases with low coating thickness, low operating speed and high operating tension. The results indicate the effect of thermal conditions on web wrinkling and the adjustment of operating conditions required to mitigate defects such as wrinkling.
{"title":"Thermal wrinkling in anode production: Defect analysis and mitigation strategies","authors":"Junyoung Yun , Yoonjae Lee , Minjae Kim , Jeongdai Jo , Changwoo Lee","doi":"10.1016/j.jmapro.2025.02.069","DOIUrl":"10.1016/j.jmapro.2025.02.069","url":null,"abstract":"<div><div>With the increasing demand for electric vehicles, roll-to-roll manufacturing systems have emerged as a promising approach for the large-scale and cost-effective production method of Li-ion batteries. However, this process poses unique challenges, including web wrinkle defects, referring to undesired folds, creases, or distortions that occur in the flexible, thin substrate composed of copper film during anode production. Web wrinkles can occur because of strain deviation in the cross-machining direction due to the application of nonuniform tension and the differences in geometrical and material properties. This study analyzed the thermal effects on formation of web wrinkles with respect to operating conditions (coating thickness, web speed, and web tension) and suggested guidelines for battery anode production to ensure optimal operating conditions through a series of simulations followed by experimental verification. A simulation-based analysis was performed, and a copper film and anode material were utilized for experimental verification. Excessive wrinkling was observed in cases with low coating thickness, low operating speed and high operating tension. The results indicate the effect of thermal conditions on web wrinkling and the adjustment of operating conditions required to mitigate defects such as wrinkling.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"141 ","pages":"Pages 81-92"},"PeriodicalIF":6.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.jmapro.2025.02.062
Jing Liu , Lingquan Hu , Kin-Wa Lui , Sidney Wing-fai Wong , Shou-xiang Jiang
3D printed lattice structures offer a promising solution for the textile and fashion industries due to their advantage of reducing material consumption while maintaining mechanical strength and versatility. There were researches have studied on resistance capacity and energy absorption efficiency. The investigation and optimization of 3D printed lattice structures for wearable materials are largely unexplored. This study addresses this gap by developing three distinct lattice structures: diamond, dodecahedron, and sinusquare structures using Low Force Stereolithography. The mechanical properties and wearable performance of the 3D printed lattice structure have been evaluated in tensile, compression, bending, thermal, and permeability tests, as well as finite element analyses. The results indicate that all three structures withstand significant deformations, showcasing their durability and breathability for garment use. The printed diamond structures exhibit excellent flexibility and breathability, with a breaking load of ∼290.33 N and extension of ∼188.65 mm at thickness 6 mm. Dodecahedron structures are the stiffest, with bending rigidity increasing from 2.4544 μN·m to 22.8661 μN·m due to its thickness increased. Sinusquare structures balance tensile strength (∼268.12 N at 6 mm) and moderate extension (∼94.98 mm), making them suitable for various garment applications. These findings offer insights into the mechanical properties and suitability of lattice structures for wearable materials. Thus, the 3D printed lattice structures show promising applications as a practical design way for textiles.
{"title":"Design and characterization of breathable 3D printed textiles with flexible lattice structures","authors":"Jing Liu , Lingquan Hu , Kin-Wa Lui , Sidney Wing-fai Wong , Shou-xiang Jiang","doi":"10.1016/j.jmapro.2025.02.062","DOIUrl":"10.1016/j.jmapro.2025.02.062","url":null,"abstract":"<div><div>3D printed lattice structures offer a promising solution for the textile and fashion industries due to their advantage of reducing material consumption while maintaining mechanical strength and versatility. There were researches have studied on resistance capacity and energy absorption efficiency. The investigation and optimization of 3D printed lattice structures for wearable materials are largely unexplored. This study addresses this gap by developing three distinct lattice structures: diamond, dodecahedron, and sinusquare structures using Low Force Stereolithography. The mechanical properties and wearable performance of the 3D printed lattice structure have been evaluated in tensile, compression, bending, thermal, and permeability tests, as well as finite element analyses. The results indicate that all three structures withstand significant deformations, showcasing their durability and breathability for garment use. The printed diamond structures exhibit excellent flexibility and breathability, with a breaking load of ∼290.33 N and extension of ∼188.65 mm at thickness 6 mm. Dodecahedron structures are the stiffest, with bending rigidity increasing from 2.4544 μN·m to 22.8661 μN·m due to its thickness increased. Sinusquare structures balance tensile strength (∼268.12 N at 6 mm) and moderate extension (∼94.98 mm), making them suitable for various garment applications. These findings offer insights into the mechanical properties and suitability of lattice structures for wearable materials. Thus, the 3D printed lattice structures show promising applications as a practical design way for textiles.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"141 ","pages":"Pages 48-58"},"PeriodicalIF":6.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-27DOI: 10.1016/j.jmapro.2025.02.073
Yi Zhu , Dengting Li , Chao Zhang , Fangye Lin , Ming Wu , Yong Chen
Abrasive flow machining (AFM) is efficient in polishing large channels while it faces challenges in post-processing mini-channels (diameters from 1 to 2 mm) fabricated by Laser-Powder Bed Fusion (L-PBF) due to an unpredictive material removal in the mini-channels. This paper aims to develop an accurate material removal model for L-PBF mini-channels accounting for an inhomogeneous channel profile. Ti6Al4V channels with a 2 mm diameter were fabricated using L-PBF. The surface topography of the mini-channels' inner wall, including circularity and surface roughness, was characterized using a micro-CT to study its evolution during the AFM process. An AFM model accounting for surface features of L-PBF fabricated mini-channels was developed, including transient erosion wear and steady-state abrasive wear. Results indicate that the material removal rate of the top region with high surface roughness is significantly high in the first 46 cycles of AFM, with nearly 79 % of the total material removal occurring in this period. During this time, the surface roughness Sa reduces to a saturation value of approximately 2.5 μm. The proposed model predicts surface topography evolution during the AFM process, with an average deviation in material removal of 8 μm (relative deviation 5.29 %), after reaching a steady state at 46 cycles. Such a tool provides engineers with a reference for minimal material removal and optimized processing time.
{"title":"Abrasive flow machining of laser powder bed fusion fabricated mini-channels: Modelling and verification","authors":"Yi Zhu , Dengting Li , Chao Zhang , Fangye Lin , Ming Wu , Yong Chen","doi":"10.1016/j.jmapro.2025.02.073","DOIUrl":"10.1016/j.jmapro.2025.02.073","url":null,"abstract":"<div><div>Abrasive flow machining (AFM) is efficient in polishing large channels while it faces challenges in post-processing mini-channels (diameters from 1 to 2 mm) fabricated by Laser-Powder Bed Fusion (L-PBF) due to an unpredictive material removal in the mini-channels. This paper aims to develop an accurate material removal model for L-PBF mini-channels accounting for an inhomogeneous channel profile. Ti6Al4V channels with a 2 mm diameter were fabricated using L-PBF. The surface topography of the mini-channels' inner wall, including circularity and surface roughness, was characterized using a micro-CT to study its evolution during the AFM process. An AFM model accounting for surface features of L-PBF fabricated mini-channels was developed, including transient erosion wear and steady-state abrasive wear. Results indicate that the material removal rate of the top region with high surface roughness is significantly high in the first 46 cycles of AFM, with nearly 79 % of the total material removal occurring in this period. During this time, the surface roughness <em>S</em><sub><em>a</em></sub> reduces to a saturation value of approximately 2.5 μm. The proposed model predicts surface topography evolution during the AFM process, with an average deviation in material removal of 8 μm (relative deviation 5.29 %), after reaching a steady state at 46 cycles. Such a tool provides engineers with a reference for minimal material removal and optimized processing time.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"141 ","pages":"Pages 36-47"},"PeriodicalIF":6.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-26DOI: 10.1016/j.jmapro.2025.02.048
Zhihang Wei , Zhaoyu Li , Min Li , Shijing Wu , Xiaosun Wang , Deng Li
In the domain of hard and brittle material machining, Computer Numerical Control (CNC) machining processes frequently face challenges like tool wear and material fragmentation. Abrasive Water Jet Machining (AWJM) offers a viable alternative to conventional CNC machining. Nevertheless, conventional AWJM techniques are inadequate for the surface machining as the entire jet beam acts upon the workpiece to achieve cleaning or separating functions. The study introduces a novel Abrasive Water Jet (AWJ) side cutting method that employs part of the jet beam for machining, which is capable of delivering high-quality and efficient AWJ surface processing and curvature machining. The mechanism of side cutting is explored using theories related to elastic collision and solid damage, based on which the machining profile prediction model is built. Then, relying on the side-cutting characteristics and the analysis of the contact relationship between the jet beam and the workpiece, an AWJ side cutting process planning method is developed, in which an optimization algorithm is employed to iteratively identify the optimal jet cutting path, thereby achieving an efficient process for surface profiling. Both computer simulation and physical cutting experiments of the proposed method have been conducted, and the results provide initial validation for the effectiveness and benefits of the proposed method.
{"title":"A novel abrasive water jet side machining method for curved surface profile","authors":"Zhihang Wei , Zhaoyu Li , Min Li , Shijing Wu , Xiaosun Wang , Deng Li","doi":"10.1016/j.jmapro.2025.02.048","DOIUrl":"10.1016/j.jmapro.2025.02.048","url":null,"abstract":"<div><div>In the domain of hard and brittle material machining, Computer Numerical Control (CNC) machining processes frequently face challenges like tool wear and material fragmentation. Abrasive Water Jet Machining (AWJM) offers a viable alternative to conventional CNC machining. Nevertheless, conventional AWJM techniques are inadequate for the surface machining as the entire jet beam acts upon the workpiece to achieve cleaning or separating functions. The study introduces a novel Abrasive Water Jet (AWJ) side cutting method that employs part of the jet beam for machining, which is capable of delivering high-quality and efficient AWJ surface processing and curvature machining. The mechanism of side cutting is explored using theories related to elastic collision and solid damage, based on which the machining profile prediction model is built. Then, relying on the side-cutting characteristics and the analysis of the contact relationship between the jet beam and the workpiece, an AWJ side cutting process planning method is developed, in which an optimization algorithm is employed to iteratively identify the optimal jet cutting path, thereby achieving an efficient process for surface profiling. Both computer simulation and physical cutting experiments of the proposed method have been conducted, and the results provide initial validation for the effectiveness and benefits of the proposed method.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"140 ","pages":"Pages 262-276"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-26DOI: 10.1016/j.jmapro.2025.02.061
Xuan Li, Zhiwei Zhao, Xinyu Cheng, Hui Chen, Jun Xiong
Arc-directed energy deposition (arc-DED) has gained attention as a promising technology for producing large-size metal components with high deposition rates and lower costs. However, the complex thermal processes involved in the deposition often lead to residual stress (RS) and distortion in deposited parts, which negatively impact the quality and performance of the final products. Therefore, controlling RS and distortion is essential for ensuring the high-quality production of arc-DED components. To mitigate RS and distortion in arc-DED, this paper presents a comprehensive review of the efforts made in previous research. The formation mechanisms of RS and distortion in arc-DED are analyzed, the influencing factors are discussed, the analysis techniques for RS and distortion are introduced, and the mitigation strategies applicable to different stages of arc-DED are summarized. The paper concludes by providing a prospective outlook on future research directions for mitigating RS and distortion. This review serves as a valuable reference for manufacturing parts with minimal RS and distortion in industrial arc-DED applications.
{"title":"Residual stress and distortion in arc-directed energy deposition: Formation mechanisms, analysis techniques, and mitigation strategies","authors":"Xuan Li, Zhiwei Zhao, Xinyu Cheng, Hui Chen, Jun Xiong","doi":"10.1016/j.jmapro.2025.02.061","DOIUrl":"10.1016/j.jmapro.2025.02.061","url":null,"abstract":"<div><div>Arc-directed energy deposition (arc-DED) has gained attention as a promising technology for producing large-size metal components with high deposition rates and lower costs. However, the complex thermal processes involved in the deposition often lead to residual stress (RS) and distortion in deposited parts, which negatively impact the quality and performance of the final products. Therefore, controlling RS and distortion is essential for ensuring the high-quality production of arc-DED components. To mitigate RS and distortion in arc-DED, this paper presents a comprehensive review of the efforts made in previous research. The formation mechanisms of RS and distortion in arc-DED are analyzed, the influencing factors are discussed, the analysis techniques for RS and distortion are introduced, and the mitigation strategies applicable to different stages of arc-DED are summarized. The paper concludes by providing a prospective outlook on future research directions for mitigating RS and distortion. This review serves as a valuable reference for manufacturing parts with minimal RS and distortion in industrial arc-DED applications.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"141 ","pages":"Pages 17-35"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Owing to the difficult-to-machine characteristics of nickel-based superalloy, the deep hole drilling (DHD) is prone to various issues, including high thrust force and torque, high cutting temperature, difficult chip breaking, tool fracture and poor surface quality caused by poor chip evacuation. Low-frequency vibration-assisted drilling (LFVAD) facilitates the tool-workpiece periodic contact and separation, as well as effective chip breaking, which provides a high potential for the DHD of difficult-to-machine materials. In the present study, the DHD of nickel-based superalloy with LFVAD and conventional drilling (CD) is comparatively analyzed in detail. Firstly, the theoretical analysis shows that the good chip breaking ability of LFVAD can effectively enhance the chip evacuation of DHD. The chip unfolded areas of LFVAD are much smaller compared to those of CD. Then, LFVAD can effectively reduce the average drilling forces (by 14.6 % and 16.3 % for average thrust force and average torque, respectively) due to the less friction and enhanced cooling effect. As for chip morphologies, there are irregular small burrs at the edge of the chip for CD with dense and tight segmentation, while LFVAD shows a relatively smooth bottom edge with hypertrophic segmentation. Finally, LFVAD is benefit for improving surface quality under appropriate vibration condition. The findings of this paper indicate that LFVAD offers several machinability advantages, making it a promising technique for the DHD of difficult-to-machine materials.
{"title":"Research on deep hole drilling of nickel-based superalloy with low-frequency vibration: Chip evacuation characteristic, chip formation and surface morphology","authors":"Dexiong Chen, Yan Chen, Jiuhua Xu, Zonghui Yang, Xiaoyu Wang, ShunXing Gao, Qiang Zhu","doi":"10.1016/j.jmapro.2025.02.058","DOIUrl":"10.1016/j.jmapro.2025.02.058","url":null,"abstract":"<div><div>Owing to the difficult-to-machine characteristics of nickel-based superalloy, the deep hole drilling (DHD) is prone to various issues, including high thrust force and torque, high cutting temperature, difficult chip breaking, tool fracture and poor surface quality caused by poor chip evacuation. Low-frequency vibration-assisted drilling (LFVAD) facilitates the tool-workpiece periodic contact and separation, as well as effective chip breaking, which provides a high potential for the DHD of difficult-to-machine materials. In the present study, the DHD of nickel-based superalloy with LFVAD and conventional drilling (CD) is comparatively analyzed in detail. Firstly, the theoretical analysis shows that the good chip breaking ability of LFVAD can effectively enhance the chip evacuation of DHD. The chip unfolded areas of LFVAD are much smaller compared to those of CD. Then, LFVAD can effectively reduce the average drilling forces (by 14.6 % and 16.3 % for average thrust force and average torque, respectively) due to the less friction and enhanced cooling effect. As for chip morphologies, there are irregular small burrs at the edge of the chip for CD with dense and tight segmentation, while LFVAD shows a relatively smooth bottom edge with hypertrophic segmentation. Finally, LFVAD is benefit for improving surface quality under appropriate vibration condition. The findings of this paper indicate that LFVAD offers several machinability advantages, making it a promising technique for the DHD of difficult-to-machine materials.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"140 ","pages":"Pages 241-261"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-26DOI: 10.1016/j.jmapro.2025.02.055
Qi Liu , Jian Cheng , Linjie Zhao , Rongkai Tan , Hongqin Lei , Mingjun Chen
Micro ball-end milling has emerged as a promising technique for repairing micro-defects on the surfaces of KH2PO4 (KDP) optics used in Inertial Confinement Fusion (ICF) facilities, which are crucial for advancing the clean energy production. However, the widespread recycling of KDP optics through this technique presents significant challenges. A critical issue is that various milling modes—such as pull, push, up, and down milling—can periodically engage in the repair process, leading to different surface topographies that ultimately affect the optical performance of the repaired optics. This study systematically analyzes the surface topographies produced by different milling modes using advanced characterization methods, including power density spectrum (PSD), continuous wavelet transform (CWT), and fractal dimension (FD). Our findings indicate that residual tool marks on micro-milled KDP surfaces are consistently more pronounced perpendicular to the milling feed direction (i.e., along the step direction) than parallel to it across all milling modes. PSD analysis reveals a dominant frequency along the step direction, which is inversely proportional to milling step intervals. The precise orientation of these tool marks for each milling mode can be accurately determined using angular spectrum analysis. Wavelet-based frequency analysis successfully identifies waviness features arising from the dynamic characteristics of the micro-milling system associated with each milling mode, providing insights for optimizing repair processes. Furthermore, the microscopic topographic features of the repaired surfaces were evaluated using FD, demonstrating stronger characterization capabilities than traditional surface roughness metrics. Notably, the circumferential FD of surfaces machined using up-milling modes was smaller than those of the other milling modes, attributed to the occurrence of micro-cracks due to the ploughing effect during up-milling. By examining the theoretical relationship between the amplitude and periodicity of residual tool marks, we recommend milling step intervals of <28 μm for future engineering repairs of KDP optics in ICF facilities.
{"title":"Invesitgation on characterizing the surface topography of KH2PO4 optics repaired by different milling modes","authors":"Qi Liu , Jian Cheng , Linjie Zhao , Rongkai Tan , Hongqin Lei , Mingjun Chen","doi":"10.1016/j.jmapro.2025.02.055","DOIUrl":"10.1016/j.jmapro.2025.02.055","url":null,"abstract":"<div><div>Micro ball-end milling has emerged as a promising technique for repairing micro-defects on the surfaces of KH<sub>2</sub>PO<sub>4</sub> (KDP) optics used in Inertial Confinement Fusion (ICF) facilities, which are crucial for advancing the clean energy production. However, the widespread recycling of KDP optics through this technique presents significant challenges. A critical issue is that various milling modes—such as pull, push, up, and down milling—can periodically engage in the repair process, leading to different surface topographies that ultimately affect the optical performance of the repaired optics. This study systematically analyzes the surface topographies produced by different milling modes using advanced characterization methods, including power density spectrum (PSD), continuous wavelet transform (CWT), and fractal dimension (FD). Our findings indicate that residual tool marks on micro-milled KDP surfaces are consistently more pronounced perpendicular to the milling feed direction (i.e., along the step direction) than parallel to it across all milling modes. PSD analysis reveals a dominant frequency along the step direction, which is inversely proportional to milling step intervals. The precise orientation of these tool marks for each milling mode can be accurately determined using angular spectrum analysis. Wavelet-based frequency analysis successfully identifies waviness features arising from the dynamic characteristics of the micro-milling system associated with each milling mode, providing insights for optimizing repair processes. Furthermore, the microscopic topographic features of the repaired surfaces were evaluated using FD, demonstrating stronger characterization capabilities than traditional surface roughness metrics. Notably, the circumferential FD of surfaces machined using up-milling modes was smaller than those of the other milling modes, attributed to the occurrence of micro-cracks due to the ploughing effect during up-milling. By examining the theoretical relationship between the amplitude and periodicity of residual tool marks, we recommend milling step intervals of <28 μm for future engineering repairs of KDP optics in ICF facilities.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"141 ","pages":"Pages 1-16"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-26DOI: 10.1016/j.jmapro.2025.02.052
Vahid Modanloo , Sewon Jang , Taeyong Lee , Luca Quagliato
Machine learning (ML) prediction tools are becoming an ever-growing topic of interest across various disciplines but are often perceived as probabilistic black boxes with good interpolation and lesser extrapolation performances. To this end, this contribution investigates the modeling and performance of a Gradient Enhanced-Expert Informed Neural Network (GE-EINN) consisting of a neural network where the backpropagation was upgraded by user-defined constraints in terms of partial differential equations (PDEs) aimed at improving the prediction accuracy outside the latent space. Experiments and finite element analysis (FEA) results under cold and warm forming conditions of pure Titanium sheet employed in Li-battery housing were carried out by recording the maximum filling depth before failure onset, subsequently correlated to 17 material and process features. Given the absence of physics-based PDEs, three numerical solutions based on the three features with the highest importance were developed for the GE-EINN model and associated with three residuals' functions based on first- and second-order PDEs. As benchmarks for the developed GE-EINN model, a Gradient Boosting (GB) ensemble and a Deep Neural Network (DNN) algorithm were considered. First, no significant differences were observed between GE-EINN, GB, and DNN during the cross-validation process, with average deviations equal to 3.9 %, 5.6 %, and 5.7 %. However, when tested on additional points within and outside the latent space, the GE-EINN model showed average improvements equal to 25.6 % and 114.2 % for the GB model, and 30.7 % and 67.3 % for the DNN model, respectively. All three models were further tested on a metallic bipolar plate (MBP) for proton exchange membrane fuel cell (PEMFC), resulting in 46.8 % and 14.5 % average improvements to GB and DNN formulations, when the GE-EINN residuals include first and second-order derivatives, but no PDEs cross-products among multiple features. Overall, the results show the improvements provided by the GE-EINN approach in extrapolation scenarios but also the need for additional research on the equation discovery phase to improve the generality of the solution.
{"title":"Gradient Enhanced-Expert Informed Neural Network (GE-EINN) for forming depth prediction from a small-scale metal stamping dataset","authors":"Vahid Modanloo , Sewon Jang , Taeyong Lee , Luca Quagliato","doi":"10.1016/j.jmapro.2025.02.052","DOIUrl":"10.1016/j.jmapro.2025.02.052","url":null,"abstract":"<div><div>Machine learning (ML) prediction tools are becoming an ever-growing topic of interest across various disciplines but are often perceived as probabilistic black boxes with good interpolation and lesser extrapolation performances. To this end, this contribution investigates the modeling and performance of a Gradient Enhanced-Expert Informed Neural Network (GE-EINN) consisting of a neural network where the backpropagation was upgraded by user-defined constraints in terms of partial differential equations (PDEs) aimed at improving the prediction accuracy outside the latent space. Experiments and finite element analysis (FEA) results under cold and warm forming conditions of pure Titanium sheet employed in Li-battery housing were carried out by recording the maximum filling depth before failure onset, subsequently correlated to 17 material and process features. Given the absence of physics-based PDEs, three numerical solutions based on the three features with the highest importance were developed for the GE-EINN model and associated with three residuals' functions based on first- and second-order PDEs. As benchmarks for the developed GE-EINN model, a Gradient Boosting (GB) ensemble and a Deep Neural Network (DNN) algorithm were considered. First, no significant differences were observed between GE-EINN, GB, and DNN during the cross-validation process, with average deviations equal to 3.9 %, 5.6 %, and 5.7 %. However, when tested on additional points within and outside the latent space, the GE-EINN model showed average improvements equal to 25.6 % and 114.2 % for the GB model, and 30.7 % and 67.3 % for the DNN model, respectively. All three models were further tested on a metallic bipolar plate (MBP) for proton exchange membrane fuel cell (PEMFC), resulting in 46.8 % and 14.5 % average improvements to GB and DNN formulations, when the GE-EINN residuals include first and second-order derivatives, but no PDEs cross-products among multiple features. Overall, the results show the improvements provided by the GE-EINN approach in extrapolation scenarios but also the need for additional research on the equation discovery phase to improve the generality of the solution.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"140 ","pages":"Pages 224-240"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-25DOI: 10.1016/j.jmapro.2025.02.056
Jiancheng Xie , Feng Shi , Shanshan Wang , Xin Liu , Shuo Qiao , Ye Tian , Qun Hao
Sapphire crystals are extensively used in laser high-energy systems due to their exceptional optical properties. However, achieving high surface quality and minimal damage in sapphire crystals is extremely challenging. This paper presents a novel method, force magnetic shear combined with chemical rheological polishing (FMS-CRP) based on shear-induced thickening and magnetically-induced thickening combined with chemical interaction, designed to enhance the quality of sapphire. A model of polishing pressure (Pd) in the FMS-CRP zone was developed based on Reynolds and magnetisation equation. The material removal rate (MRR) was derived from active abrasive theory. According to FMS-CRP experiments, the maximum variance between theoretical and experimental values was 8.6%, confirming the validity of the MRR theoretical model. The risk of subsurface damage (SSD) was mitigated using maximum depth of cut and crack depth theories. Material Studio (MS) software simulations, along with X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR), were used to analyse the complexation reaction process of sapphire and to identify the composition of the chemically softened layer. Under optimal polishing conditions (Da = 4 μm, pH = 10, h0 = 1.0 mm, T = 25 °C, wa = 30 wt%, vf = 2.5 m/s, and B = 300 mT), the accuracy of sapphire faceting significantly improved, achieving a surface roughness of Ra = 0.2 nm and a peak-to-valley (PV) value of 10 nm. SSD was controlled within 0.5 μm, ensuring excellent surface quality. Thus, the FMS-CRP processing method is shown to produce high-precision sapphire crystals with substantially improved surface quality and controlled subsurface damage.
{"title":"Mechanisms of force magnetic shear combined with chemical rheological polishing (FMS-CRP): A case study in sapphire processing","authors":"Jiancheng Xie , Feng Shi , Shanshan Wang , Xin Liu , Shuo Qiao , Ye Tian , Qun Hao","doi":"10.1016/j.jmapro.2025.02.056","DOIUrl":"10.1016/j.jmapro.2025.02.056","url":null,"abstract":"<div><div>Sapphire crystals are extensively used in laser high-energy systems due to their exceptional optical properties. However, achieving high surface quality and minimal damage in sapphire crystals is extremely challenging. This paper presents a novel method, force magnetic shear combined with chemical rheological polishing (FMS-CRP) based on shear-induced thickening and magnetically-induced thickening combined with chemical interaction, designed to enhance the quality of sapphire. A model of polishing pressure (<em>P</em><sub>d</sub>) in the FMS-CRP zone was developed based on Reynolds and magnetisation equation. The material removal rate (MRR) was derived from active abrasive theory. According to FMS-CRP experiments, the maximum variance between theoretical and experimental values was 8.6%, confirming the validity of the MRR theoretical model. The risk of subsurface damage (SSD) was mitigated using maximum depth of cut and crack depth theories. Material Studio (MS) software simulations, along with X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR), were used to analyse the complexation reaction process of sapphire and to identify the composition of the chemically softened layer. Under optimal polishing conditions (<em>D</em><sub>a</sub> = 4 μm, pH = 10, <em>h</em><sub>0</sub> = 1.0 mm, <em>T</em> = 25 °C, <em>w</em><sub>a</sub> = 30 wt%, <em>v</em><sub>f</sub> = 2.5 m/s, and <em>B</em> = 300 mT), the accuracy of sapphire faceting significantly improved, achieving a surface roughness of <em>R</em><sub>a</sub> = 0.2 nm and a peak-to-valley (PV) value of 10 nm. SSD was controlled within 0.5 μm, ensuring excellent surface quality. Thus, the FMS-CRP processing method is shown to produce high-precision sapphire crystals with substantially improved surface quality and controlled subsurface damage.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"140 ","pages":"Pages 181-203"},"PeriodicalIF":6.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The evaluation of radiographic indications in welds plays a critical role in the quality assurance of the manufacturing process for metal products. The traditional visual approach for the evaluation of defects is inefficient and inconsistent. Various techniques for automated defect recognition of indications in weld radiographs have been proposed in the last three decades. In recent years, notable progresses have been made with the development of deep learning-based techniques. However, to date, the literature still lacks a comprehensive review of automated defect recognition in radiographic images. Therefore, this paper reviews the automated defect recognition in X-ray weld inspection, including traditional and deep-learning-based techniques. The review of traditional techniques is outlined from the perspective of image pre-processing, feature extraction, and defect analysis and evaluation. Deep-learning-based methods are reviewed from the perspective of datasets and networks structures, discussing the techniques employed to solve the small datasets problem, segmentation and classification of defects in welds. Finally, potential advancements in automated weld inspection techniques are drawn.
{"title":"A comprehensive review of welding defect recognition from X-ray images","authors":"Xiaopeng Wang , Uwe Zscherpel , Paolo Tripicchio , Salvatore D'Avella , Baoxin Zhang , Juntao Wu , Zhimin Liang , Shaoxin Zhou , Xinghua Yu","doi":"10.1016/j.jmapro.2025.02.039","DOIUrl":"10.1016/j.jmapro.2025.02.039","url":null,"abstract":"<div><div>The evaluation of radiographic indications in welds plays a critical role in the quality assurance of the manufacturing process for metal products. The traditional visual approach for the evaluation of defects is inefficient and inconsistent. Various techniques for automated defect recognition of indications in weld radiographs have been proposed in the last three decades. In recent years, notable progresses have been made with the development of deep learning-based techniques. However, to date, the literature still lacks a comprehensive review of automated defect recognition in radiographic images. Therefore, this paper reviews the automated defect recognition in X-ray weld inspection, including traditional and deep-learning-based techniques. The review of traditional techniques is outlined from the perspective of image pre-processing, feature extraction, and defect analysis and evaluation. Deep-learning-based methods are reviewed from the perspective of datasets and networks structures, discussing the techniques employed to solve the small datasets problem, segmentation and classification of defects in welds. Finally, potential advancements in automated weld inspection techniques are drawn.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"140 ","pages":"Pages 161-180"},"PeriodicalIF":6.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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