Journal of Manufacturing Processes最新文献_第8页

Reducing tool wear in high-speed milling of Inconel 718 by optimizing flank-face coolant direction: A CFD-supported approach toward sustainable machining 通过优化侧面冷却剂方向来减少高速铣削Inconel 718的刀具磨损：cfd支持的可持续加工方法

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-21 DOI: 10.1016/j.jmapro.2026.01.065

Jingtian Mao , Kensuke Tsuchiya , Chikara Morigo , Shinji Yukinari , Hiroki Tahara , Yoshihide Kurashiki

{"title":"Reducing tool wear in high-speed milling of Inconel 718 by optimizing flank-face coolant direction: A CFD-supported approach toward sustainable machining","authors":"Jingtian Mao , Kensuke Tsuchiya , Chikara Morigo , Shinji Yukinari , Hiroki Tahara , Yoshihide Kurashiki","doi":"10.1016/j.jmapro.2026.01.065","DOIUrl":"10.1016/j.jmapro.2026.01.065","url":null,"abstract":"<div><div>This study investigates the impact of flank face coolant nozzle orientation on tool wear suppression in the high-speed milling of Inconel 718. Preliminary dry milling of Ti-6Al-4V with a Tool-Nose-Aimed (TNA) tool revealed severe flank wear at the bolt side edge. This failure mode persisted in Inconel 718, where the TNA tool suffered from severe adhesion and brittle fracture at this location under dry, flood and all high-pressure coolant (HPC) conditions except ultra-high pressure coolant (UHPC) at 20 MPa. Thermal and Computational Fluid Dynamics (CFD) simulations diagnosed the cause: the bolt side edge is a thermal throttling zone, and the TNA's coolant jet core deviates from this critical spot. Guided by this analysis, a novel Bolt-Side-Edge-Flank-Aimed (BSEFA) tool was designed. Its nozzle orientation was optimized to ensure the jet core directly impinges on the flank face of the bolt side edge, enhancing convective heat transfer through higher liquidity, velocity, and Turbulence Kinetic Energy (TKE). Experimentally, the BSEFA tool suppressed catastrophic failure, massive adhesion and reduced maximum flank wear (VB<sub>max</sub>) within the wear land by 40–56% compared to the TNA tool. CFD results confirmed the mechanism, showing the optimized nozzle delivered superior coolant coverage (liquidity >0.95), higher velocity (>110 m/s), and drastically intensified turbulence (TKE increase >150%) at the target. This work establishes that strategic coolant orientation surpasses indiscriminate pressure increase. The BSEFA strategy enables high performance with minimal flow rate (<1.0 L/min), representing a > 95% reduction versus flood cooling, offering a highly efficient and sustainable machining strategy.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 111-133"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036892","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}

引用次数: 0

A physics-informed neural network with adaptive loss weighting for tool wear and remaining useful life prediction in deep hole boring 一种具有自适应损失加权的物理信息神经网络，用于深孔钻孔中刀具磨损和剩余使用寿命预测

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-21 DOI: 10.1016/j.jmapro.2026.01.047

Pengfei Zhang , Hanxiao Zhao , Ang Li , Chao Sun , Hongzhe Zhang , Naohiko Sugita , Liming Shu

{"title":"A physics-informed neural network with adaptive loss weighting for tool wear and remaining useful life prediction in deep hole boring","authors":"Pengfei Zhang , Hanxiao Zhao , Ang Li , Chao Sun , Hongzhe Zhang , Naohiko Sugita , Liming Shu","doi":"10.1016/j.jmapro.2026.01.047","DOIUrl":"10.1016/j.jmapro.2026.01.047","url":null,"abstract":"<div><div>Tool wear and remaining useful life (RUL) prediction are critical for ensuring machining quality and reducing production costs, playing an important role in deep-hole machining. Recently, physics-informed neural network (PINN) has attracted great attention to achieve this goal. However, the weights between physics-based models and data-driven models are often set empirically, which severely affects training accuracy and stability. To address this issue, this paper proposes a PINN with adaptive loss weighting, by quantifying the variance of prediction errors for tool wear and RUL prediction. First, multi-channel signals in deep hole boring are used to extract time-domain and frequency-domain features. Then, correlation coefficients between tool wear and features are calculated for feature selection, and combined with cutting stroke information to form the dataset. Next, based on the cutting stroke and flank wear values, a tool wear rate model is constructed using the least squares method. This equation serves as the physical consistency constraint of the PINN. The total loss function is constructed by combining the data loss from the data-driven model, the monotonicity loss, and the physical consistency loss. Finally, based on the AutoRegressive Integrated Moving Average (ARIMA) model and historical tool wear values, multi-step-ahead forecasting of tool wear and RUL prediction are achieved. Results show that the proposed PINN with adaptive loss weighting achieves the best tool wear prediction performance, compared with PINNs without weight adjustment (fixed weights), without monotonicity constraints, or without physical consistency constraints. Moreover, ARIMA multi-step-ahead forecasts closely match the measured tool wear and outperform the GRU baseline. The findings of this paper lay the foundation for automation and even unmanned operation in deep-hole machining.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 82-98"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036890","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}

引用次数: 0

Electric arc electrochemical machining: Synergistic effect, material removal mechanism and process research 电弧电化学加工：协同效应、材料去除机理及工艺研究

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-22 DOI: 10.1016/j.jmapro.2026.01.057

Shengsheng Zhang , Yinan Zhao , Jianping Zhou , Xiaoxiao Chen , Yufeng Wang , Wenwu Zhang

{"title":"Electric arc electrochemical machining: Synergistic effect, material removal mechanism and process research","authors":"Shengsheng Zhang , Yinan Zhao , Jianping Zhou , Xiaoxiao Chen , Yufeng Wang , Wenwu Zhang","doi":"10.1016/j.jmapro.2026.01.057","DOIUrl":"10.1016/j.jmapro.2026.01.057","url":null,"abstract":"<div><div>This paper presents a hybrid process- electric arc electrochemical machining (EAECM). This process utilizes synergistic effect of discharge and dissolution to achieve efficient and high quality manufacturing. The surface polishing characteristics, formation and dissolution behavior of recast layer, overcutting, electrode wear mechanism, and product properties of EAECM were experimentally investigated. The results show that EAECM can dissolve recast layer and polish surface at non-discharge moments while maintaining the efficiency of EAM. There is significant anisotropy in the recast layer, and the solidification rate difference also leads to the proliferation of internal grain dislocations, causing residual tensile stress to fail the material. EAECM eliminates the residual tensile stress during machining and restores the material's own property. In addition, EAECM has a heat diffusion inhibition behavior, which has a protective effect on the substrate. The alternating discharge and dissolution points of EAECM, combined with the limited dissolution time of the high-speed moving electrode, the electrolyte concentration of 10–15 Wt% is still no over-corrosion. The synergistic effect of lightweight electrolytic products with tiny volume and safe distance improves the discharge state and reduces the electrode wear, consequently maintaining the shape accuracy. The process performance of conventional electric arc machining (EAM) and EAECM was compared to demonstrate the process value of EAECM. Parameter optimization was also performed to enhance the process potential. Finally, the engineering potential of EAECM was demonstrated by some artifacts.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 222-241"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036897","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}

引用次数: 0

Retraction notice to “The role of defect structure and residual stress on fatigue failure mechanisms of Ti-6Al-4V manufactured via laser powder bed fusion: Effect of process parameters and geometrical factors” [Journal of Manufacturing Processes 102 (2023) 549–563] “缺陷结构和残余应力在Ti-6Al-4V激光粉末床熔合疲劳失效机制中的作用：工艺参数和几何因素的影响”[j] .制造工艺学报，102(2023):549-563。

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-27 DOI: 10.1016/j.jmapro.2025.12.069

Seyed Mehrab Hosseini , Ehsan Vaghefi , Elham Mirkoohi

引用次数: 0

A Physics-Informed Neural Network framework with strong robustness to low-accuracy physical models for predicting adhesive wear of self-made BNNC milling tool 自制BNNC铣刀黏着磨损预测的物理信息神经网络框架对低精度物理模型具有强鲁棒性

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-23 DOI: 10.1016/j.jmapro.2026.01.049

Shengyue Tan , Dongqian Wang , Yongliang Liu , Yonglin Cai , Jia Wei , Lei Wang , Uwe Teicher , Albrecht Hänel , Steffen Ihlenfeldt , Zhiqiang Liang

{"title":"A Physics-Informed Neural Network framework with strong robustness to low-accuracy physical models for predicting adhesive wear of self-made BNNC milling tool","authors":"Shengyue Tan , Dongqian Wang , Yongliang Liu , Yonglin Cai , Jia Wei , Lei Wang , Uwe Teicher , Albrecht Hänel , Steffen Ihlenfeldt , Zhiqiang Liang","doi":"10.1016/j.jmapro.2026.01.049","DOIUrl":"10.1016/j.jmapro.2026.01.049","url":null,"abstract":"<div><div>In high-speed hard milling, flank wear prediction of ultra-hard tools is necessary, where accuracy and stability are the two key indicators. Physics-Informed Neural Network (PINN) improves the prediction stability by embedding consistent physical laws into the training process. However, when low-accuracy physical models are commonly employed to constrain the solution space, optimization paths may be misled. This limits the performance of PINNs and their weighted frameworks. To address the issue of low-accuracy physical model misleading the optimization direction, a novel framework termed Physics-Informed Weighted Neural Network based on Prediction Error of Physics-driven models (PIWNN-PEP) is proposed. PIWNN-PEP can enhance the robustness of PINN against low-accuracy models. Furthermore, to capture long-term and complex dependencies over long time scales, a collaborative network xLSTM-Informer (xICNet) with a stacked mLSTM-sLSTM-Informer architecture is established, of which xICNet directly builds a mapping between multidimensional cutting forces and wear values within the PIWNN-PEP framework. The experimental result demonstrates that the proposed method prominently enhances robustness to low-accuracy physical models, compared with existing weighted PINN frameworks. The average tool wear prediction error remains below 1 μm.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 285-301"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036901","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}

引用次数: 0

Improving curved surface machining quality of blasting erosion arc milling by applying working fluid blockers 应用工作液阻断剂改善喷蚀弧铣曲面加工质量

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-20 DOI: 10.1016/j.jmapro.2026.01.056

Lin Gu , Kelin Li , Guojian He , Lijie Jiang , Xiaoka Wang

{"title":"Improving curved surface machining quality of blasting erosion arc milling by applying working fluid blockers","authors":"Lin Gu , Kelin Li , Guojian He , Lijie Jiang , Xiaoka Wang","doi":"10.1016/j.jmapro.2026.01.056","DOIUrl":"10.1016/j.jmapro.2026.01.056","url":null,"abstract":"<div><div>Electrical Arc Machining (EAM) is a promising method for processing difficult-to-cut materials, offering a satisfactory material removal rate and high efficiency. It has been applied in the aerospace industry to remove most of the residue from the blank to save machining time and cost. However, for aerospace parts with complex curved surfaces, such as blades, turbine disks, and impellers, it's prone to lead to intensive working fluid leakage during machining. This fluid leakage adversely recedes the arc breaking effect and results in an unacceptable coarse surface. To address this issue, this study defines the criteria for achieving good machined surface by EAM and proposes the working fluid guiding strategy including internal, external, and combined guiding approaches. The flow field is simulated and a blocker is designed to study the working fluid guiding strategy for the suppression of flushing deficiency. The results indicate that the combined strategy yields the most significant improvement effect, increasing the ratio of effective flushing by 1.2 times and desirable discharge rate by over 35%. Additionally, it noticeably reduces the surface roughness and the thickness of the recast layer. The validity of this novel approach is further demonstrated through the machining of a three-dimensional flow impeller sample using Blasting Erosion Arc Machining (BEAM) with the working fluid guiding strategy.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 1-14"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001851","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}

引用次数: 0

Electro-magnetic coupled field-assisted laser-directed energy of Ni-based WC composite coatings: Defect suppression, microstructural evolution, and tribological behavior 电磁耦合场辅助激光定向能制备镍基WC复合涂层：缺陷抑制、微观组织演变和摩擦学行为

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-26 DOI: 10.1016/j.jmapro.2026.01.069

Wanyang Li , Weiwei Liu , Huanqiang Liu , Junjie Tan , Haojun Yang , Baimao Lei , Qiang Chen , Jianrong Song , Zongyu Ma , Tao Li , Yulin Wang , Fengtao Wang , Hongchao Zhang

{"title":"Electro-magnetic coupled field-assisted laser-directed energy of Ni-based WC composite coatings: Defect suppression, microstructural evolution, and tribological behavior","authors":"Wanyang Li , Weiwei Liu , Huanqiang Liu , Junjie Tan , Haojun Yang , Baimao Lei , Qiang Chen , Jianrong Song , Zongyu Ma , Tao Li , Yulin Wang , Fengtao Wang , Hongchao Zhang","doi":"10.1016/j.jmapro.2026.01.069","DOIUrl":"10.1016/j.jmapro.2026.01.069","url":null,"abstract":"<div><div>In this study, laser-directed energy deposition (LDED) assisted by an electromagnetic coupled field (EMF) was employed to systematically investigate the effects of current intensity and direction on melt pool dynamics, defect evolution, microstructural evolution, and tribological behavior of composite coatings. The coupled field reshaped melt pool flow patterns and effectively suppress pores and cracks, reducing the defect density to 0.075%. Electromagnetic stirring effect enhanced solute redistribution and grain morphology transformation, unveiling a coupled mechanism of “grain-boundary transformation-defect accumulation-strain release”. EBSD and TEM analyses revealed a microstructural transition from a “WC/W<sub>2</sub>C-dominated” state to a “γ-Ni matrix-Cr carbide co-dominated” configuration, which regulated interfacial carbon activity, promoted stable Cr<sub>7</sub>C<sub>3</sub> precipitation, and facilitated the formation of Ni<sub>3</sub>Si + γ-Ni(Fe,Cr) eutectics. An appropriate current intensity further facilitated particle redistribution, suppressed fatigue spalling, and enhanced wear resistance by nearly 64.70%. These findings demonstrate that the external coupled field regulates microstructural evolution and wear behavior, providing new processing pathways for tailoring the performance of high-performance composite coatings.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 389-412"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080391","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}

引用次数: 0

Machine learning prediction of recoater damage topography deviations 重涂器损伤形貌偏差的机器学习预测

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-28 DOI: 10.1016/j.jmapro.2026.01.060

Caroline E. Massey , Christopher J. Saldaña

{"title":"Machine learning prediction of recoater damage topography deviations","authors":"Caroline E. Massey , Christopher J. Saldaña","doi":"10.1016/j.jmapro.2026.01.060","DOIUrl":"10.1016/j.jmapro.2026.01.060","url":null,"abstract":"<div><div>In-situ monitoring in laser powder bed fusion (PBF-LB) presents a paradigm for progress towards born qualified parts. This technology has proven useful in many applications such as monitoring for geometric error, layer-wise part defects, and spreading defects. The significance of spreading defects is particularly understudied, especially in the experimental domain. Recoater damage can be particularly detrimental to mechanical performance, as it lends to topography deviations in the build, which could cause porosity, geometric inaccuracies, and potential build failure. Yet, prior literature has not addressed machine learning's ability to predict the severity of recoater damage. This work used multiple feature-based and image-based machine learning algorithms combined with in-situ layer-wise monitoring to predict the amount of topography deviation within recoater damaged sections. The height and width of the topography deviations were measured after the spread profile was exposed to multiple different sizes of recoater wear at different recoater spread speeds and layer thicknesses. The acquired images had different image filtering methods applied to see if a particular image filtering method can increase prediction performance. Ultimately, the image-based machine learning methods showed the best performance when combined with noising filters. In all, this work seeks to find the ideal configuration for the prediction of topography height and width deviations when the powder bed is exposed to recoater damage.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 542-552"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080328","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}

引用次数: 0

QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with vision language models QA-VLM：利用视觉语言模型为线喂激光增材制造零件提供人类可解释的质量评估

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-30 DOI: 10.1016/j.jmapro.2026.01.071

Qiaojie Zheng , Jiucai Zhang , Joy Gockel , Michael B. Wakin , Craig Brice , Xiaoli Zhang

{"title":"QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with vision language models","authors":"Qiaojie Zheng , Jiucai Zhang , Joy Gockel , Michael B. Wakin , Craig Brice , Xiaoli Zhang","doi":"10.1016/j.jmapro.2026.01.071","DOIUrl":"10.1016/j.jmapro.2026.01.071","url":null,"abstract":"<div><div>Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. When evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These findings indicate that the literature-supported quality assessment model has the potential to improve reliability for QA tasks, motivating future validation on larger, multi-layer, and multi-pass builds, and broader process/material conditions.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 611-623"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080329","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}

引用次数: 0

An improved residual height model for compliant robotic belt grinding with application to dwell time optimization 一种改进的柔性机器人带磨削剩余高度模型及其在停留时间优化中的应用

IF 6.8 1区工程技术

Journal of Manufacturing Processes Pub Date : 2026-02-28 Epub Date: 2026-01-22 DOI: 10.1016/j.jmapro.2026.01.045

Xiaoyu Zhao , Lai Zou , Kangkang Song , Wenxi Wang , Yilin Mu

{"title":"An improved residual height model for compliant robotic belt grinding with application to dwell time optimization","authors":"Xiaoyu Zhao , Lai Zou , Kangkang Song , Wenxi Wang , Yilin Mu","doi":"10.1016/j.jmapro.2026.01.045","DOIUrl":"10.1016/j.jmapro.2026.01.045","url":null,"abstract":"<div><div>Compliant robotic belt grinding is widely used for finishing complex components like turbine blades and blisks. However, conventional geometric models often fail to predict residual height accurately under force-compliant conditions. This inaccuracy arises because the actual material removal profile is dynamically distorted by the coupling of contact wheel elastic deformation and time-varying abrasive wear. To address these effects, an improved residual height model is presented for structured abrasive belts. By integrating Hertzian contact mechanics and Archard's wear law, the model explicitly quantifies the over-grinding effect caused by pressure superposition in overlapping zones and the degradation of abrasive grains. Based on this predictive model, a dwell-time optimization strategy is formulated to coordinate process efficiency and contour precision under constraints of residual height, chord error, and allowance matching. Experimental validation on curved TC4 titanium alloy components shows that the proposed approach reduces residual height prediction error to within ±10 μm. Under the tested conditions, surface roughness decreased by approximately 30%, and profile accuracy by 25% compared to constant-feed strategies, demonstrating the engineering feasibility of the proposed framework.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"160 ","pages":"Pages 200-221"},"PeriodicalIF":6.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036896","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}

引用次数: 0