Pub Date : 2026-03-30Epub Date: 2026-02-12DOI: 10.1016/j.jmapro.2026.01.036
Hongyan Zhang
The growing demand for lightweight, multi-material structures has accelerated the development of joining technologies capable of uniting dissimilar metals, polymers, and composites. Conventional resistance spot welding (RSW) remains prevalent in automotive body construction but faces inherent limitations when applied to high-strength aluminum, magnesium, and polymer-matrix materials. These challenges—arising from electrical, thermal, and metallurgical incompatibilities—have driven the emergence of alternative spot-joining technologies that employ frictional, mechanical, or hybrid mechanisms to achieve localized bonding without melting.
This paper provides a comprehensive review of these methods, categorized as rivet-less, tubular-rivet, and solid-rivet joining systems. The discussion covers mechanisms of heat generation and material flow, representative joint microstructures, and mechanical performance across various material combinations. Recent advances in numerical and data-driven modeling are examined, including thermo-mechanical finite-element analysis, machine-learning-assisted surrogate modeling, crashworthiness simulation, and corrosion–fatigue prediction, all of which contribute to the realization of digital-twin frameworks for joining process design.
The paper concludes with an outlook on AI-enabled process monitoring, adaptive control, and sustainability-oriented design, emphasizing the transition from empirically tuned operations to intelligent, self-optimizing joining systems that support next-generation manufacturing.
{"title":"Advances in alternative spot-joining technologies for difficult-to-weld materials","authors":"Hongyan Zhang","doi":"10.1016/j.jmapro.2026.01.036","DOIUrl":"10.1016/j.jmapro.2026.01.036","url":null,"abstract":"<div><div>The growing demand for lightweight, multi-material structures has accelerated the development of joining technologies capable of uniting dissimilar metals, polymers, and composites. Conventional resistance spot welding (RSW) remains prevalent in automotive body construction but faces inherent limitations when applied to high-strength aluminum, magnesium, and polymer-matrix materials. These challenges—arising from electrical, thermal, and metallurgical incompatibilities—have driven the emergence of <strong>alternative spot-joining technologies</strong> that employ frictional, mechanical, or hybrid mechanisms to achieve localized bonding without melting.</div><div>This paper provides a comprehensive review of these methods, categorized as <strong>rivet-less</strong>, <strong>tubular-rivet</strong>, and <strong>solid-rivet</strong> joining systems. The discussion covers mechanisms of heat generation and material flow, representative joint microstructures, and mechanical performance across various material combinations. Recent advances in <strong>numerical and data-driven modeling</strong> are examined, including thermo-mechanical finite-element analysis, <strong>machine-learning-assisted surrogate modeling</strong>, <strong>crashworthiness simulation</strong>, and <strong>corrosion–fatigue prediction</strong>, all of which contribute to the realization of <strong>digital-twin frameworks</strong> for joining process design.</div><div>The paper concludes with an outlook on <strong>AI-enabled process monitoring, adaptive control, and sustainability-oriented design</strong>, emphasizing the transition from empirically tuned operations to intelligent, self-optimizing joining systems that support next-generation manufacturing.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"162 ","pages":"Pages 118-138"},"PeriodicalIF":6.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192739","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 : 2026-03-30Epub Date: 2026-02-09DOI: 10.1016/j.jmapro.2026.01.080
Marzia Saghafi , Ruth Jill Urbanic , Bob Hedrick
Directed Energy Deposition (DED) is increasingly adopted for manufacturing large-scale metal components where conventional methods are impractical. While it offers material efficiency and tailored properties, it also introduces challenges associated with repeated thermal cycles. Even for a single geometry, different decomposition strategies, toolpaths, and process parameters can significantly alter the resulting thermal histories, which in turn govern microstructure and final properties. For process plan optimization, conventional finite element (FEM) models can capture these cycles, but their reliance on meshing, specialized expertise, and long runtimes restrict use in real-world cases.
This research aimed to develop a framework that balances computational efficiency with predictive fidelity while remaining suitable for both academic and industrial deployment. To this end, the VoxeLogic Heat Model was developed: a new, mesh-free formulation built from first principles to simulate heat transfer directly from deposition toolpaths, providing complete temperature–time histories across the build. Rooted in physical principles rather than training datasets, VoxeLogic is broadly applicable across geometries and process conditions. Applications include single-layer deposition on flat and curved substrates with complex curvilinear toolpaths. Benchmarking against experimentally validated FEM simulations and thermocouple measurements showed that VoxeLogic reproduced temperature–time profiles with errors below 5% for peak temperatures and 10% for cooling rates. Microstructure-relevant thermal metrics were also captured with accuracy suitable for engineering analysis. This fidelity was achieved while reducing computation time by 99.8%, from hours to seconds. These results also establish VoxeLogic as a foundation for extending voxel-based thermal simulation to multilayer 3D deposition and future digital twin applications.
{"title":"VoxeLogic: A voxel-based, mesh-free model for fast, high-fidelity temperature prediction and process planning in directed energy deposition","authors":"Marzia Saghafi , Ruth Jill Urbanic , Bob Hedrick","doi":"10.1016/j.jmapro.2026.01.080","DOIUrl":"10.1016/j.jmapro.2026.01.080","url":null,"abstract":"<div><div>Directed Energy Deposition (DED) is increasingly adopted for manufacturing large-scale metal components where conventional methods are impractical. While it offers material efficiency and tailored properties, it also introduces challenges associated with repeated thermal cycles. Even for a single geometry, different decomposition strategies, toolpaths, and process parameters can significantly alter the resulting thermal histories, which in turn govern microstructure and final properties. For process plan optimization, conventional finite element (FEM) models can capture these cycles, but their reliance on meshing, specialized expertise, and long runtimes restrict use in real-world cases.</div><div>This research aimed to develop a framework that balances computational efficiency with predictive fidelity while remaining suitable for both academic and industrial deployment. To this end, the VoxeLogic Heat Model was developed: a new, mesh-free formulation built from first principles to simulate heat transfer directly from deposition toolpaths, providing complete temperature–time histories across the build. Rooted in physical principles rather than training datasets, VoxeLogic is broadly applicable across geometries and process conditions. Applications include single-layer deposition on flat and curved substrates with complex curvilinear toolpaths. Benchmarking against experimentally validated FEM simulations and thermocouple measurements showed that VoxeLogic reproduced temperature–time profiles with errors below 5% for peak temperatures and 10% for cooling rates. Microstructure-relevant thermal metrics were also captured with accuracy suitable for engineering analysis. This fidelity was achieved while reducing computation time by 99.8%, from hours to seconds. These results also establish VoxeLogic as a foundation for extending voxel-based thermal simulation to multilayer 3D deposition and future digital twin applications.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"162 ","pages":"Pages 1-20"},"PeriodicalIF":6.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192809","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 : 2026-03-30Epub Date: 2026-02-13DOI: 10.1016/j.jmapro.2026.02.017
Hao Tu, Baokai Ren, Wenxiao Yu, Kang Zhou
The reliable joining of aluminum/steel dissimilar materials remains a critical challenge in lightweight manufacturing. This study aims to systematically investigate the influence of ultrasonic vibration assistance on the microstructure, residual stress, and mechanical properties of aluminum/steel resistance spot welded joints. A comparative analysis was conducted between conventional resistance spot welding and ultrasonic vibration-assisted resistance spot welding joints. Electron backscatter diffraction analysis revealed that ultrasonic treatment effectively relaxed the localized strain in critical zones of the weld, particularly alleviating strain concentration at the aluminum/steel interface. XRD residual stress measurements indicated that the peak macroscopic tensile residual stress in the joint was reduced by over 50% due to the release of microscale strain and optimization of the thermo-mechanical coupling field. The synergistic optimization of both microstructure and stress state promoted a transition in the fracture mode from brittle interfacial fracture to ductile button fracture, with the fracture morphology changing from cleavage river patterns to uniform dimples. Finite element simulation results further validated the modulatory effect of ultrasonic vibration on the welding thermal cycle and stress evolution. This study demonstrates that the ultrasonic vibration-assisted process provides an effective pathway for achieving high-performance spot welding of aluminum/steel dissimilar materials through optimization of the interfacial reactions, crystalline structure, and stress field.
{"title":"Effect of ultrasonic vibration on residual stress and microstructure in resistance spot welding of aluminum/steel dissimilar materials","authors":"Hao Tu, Baokai Ren, Wenxiao Yu, Kang Zhou","doi":"10.1016/j.jmapro.2026.02.017","DOIUrl":"10.1016/j.jmapro.2026.02.017","url":null,"abstract":"<div><div>The reliable joining of aluminum/steel dissimilar materials remains a critical challenge in lightweight manufacturing. This study aims to systematically investigate the influence of ultrasonic vibration assistance on the microstructure, residual stress, and mechanical properties of aluminum/steel resistance spot welded joints. A comparative analysis was conducted between conventional resistance spot welding and ultrasonic vibration-assisted resistance spot welding joints. Electron backscatter diffraction analysis revealed that ultrasonic treatment effectively relaxed the localized strain in critical zones of the weld, particularly alleviating strain concentration at the aluminum/steel interface. XRD residual stress measurements indicated that the peak macroscopic tensile residual stress in the joint was reduced by over 50% due to the release of microscale strain and optimization of the thermo-mechanical coupling field. The synergistic optimization of both microstructure and stress state promoted a transition in the fracture mode from brittle interfacial fracture to ductile button fracture, with the fracture morphology changing from cleavage river patterns to uniform dimples. Finite element simulation results further validated the modulatory effect of ultrasonic vibration on the welding thermal cycle and stress evolution. This study demonstrates that the ultrasonic vibration-assisted process provides an effective pathway for achieving high-performance spot welding of aluminum/steel dissimilar materials through optimization of the interfacial reactions, crystalline structure, and stress field.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"162 ","pages":"Pages 178-196"},"PeriodicalIF":6.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192740","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 : 2026-03-15Epub Date: 2026-02-02DOI: 10.1016/j.jmapro.2026.01.101
Mingming Zhang , Xun Xu , Jan Polzer , Qunfeng Liu , XuFan Chen , XiaoQi Chen
This review provides a detailed analysis of real time welding defect detection systems focusing on the critical integration of advanced sensing technologies with artificial intelligence to enhance welding quality assurance. Traditional post process inspection methods are time consuming costly and fundamentally incompatible with the modern automated manufacturing requirement for real time quality control. This necessitates a shift toward in process monitoring systems that detect defects during the welding operation enabling immediate corrective action. The study evaluates the effectiveness of various sensor technologies including optical electrical acoustic thermal and radiographic sensors in identifying diverse welding defects. It then examines the application of advanced AI techniques for welding defect diagnosis covering specialized models such as convolutional and recurrent neural networks transformer and generative models transfer learning multimodal data fusion and hybrid approaches. The review also discusses key challenges such as data quality acquisition scarcity computational resource limitations and system integration complexity. Finally it highlights promising future research directions including lightweight AI models sophisticated multi sensor fusion strategies and digital twin technologies. These advancements have the potential to improve diagnosis accuracy and truly enable real time defect detection during the welding operation ultimately increasing manufacturing efficiency reducing waste and ensuring the production of safer and more reliable welded structures in critical industrial sectors.
{"title":"Data-driven artificial intelligence methods for real-time welding defect diagnosis: A critical review and future outlook","authors":"Mingming Zhang , Xun Xu , Jan Polzer , Qunfeng Liu , XuFan Chen , XiaoQi Chen","doi":"10.1016/j.jmapro.2026.01.101","DOIUrl":"10.1016/j.jmapro.2026.01.101","url":null,"abstract":"<div><div>This review provides a detailed analysis of real time welding defect detection systems focusing on the critical integration of advanced sensing technologies with artificial intelligence to enhance welding quality assurance. Traditional post process inspection methods are time consuming costly and fundamentally incompatible with the modern automated manufacturing requirement for real time quality control. This necessitates a shift toward in process monitoring systems that detect defects during the welding operation enabling immediate corrective action. The study evaluates the effectiveness of various sensor technologies including optical electrical acoustic thermal and radiographic sensors in identifying diverse welding defects. It then examines the application of advanced AI techniques for welding defect diagnosis covering specialized models such as convolutional and recurrent neural networks transformer and generative models transfer learning multimodal data fusion and hybrid approaches. The review also discusses key challenges such as data quality acquisition scarcity computational resource limitations and system integration complexity. Finally it highlights promising future research directions including lightweight AI models sophisticated multi sensor fusion strategies and digital twin technologies. These advancements have the potential to improve diagnosis accuracy and truly enable real time defect detection during the welding operation ultimately increasing manufacturing efficiency reducing waste and ensuring the production of safer and more reliable welded structures in critical industrial sectors.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 49-63"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190296","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 : 2026-03-15Epub Date: 2026-02-03DOI: 10.1016/j.jmapro.2026.01.083
Junbo Tuo , Yongkai Zhao , Bo Liang , Yongliang Li
In the actual production process of machine tools, hole machining is the most common machining method, which produces different amounts of carbon emissions depending on the processing methods. Among them, optimizing the cutting parameters of hole machining is a common and effective method to diminish carbon emissions. However, concentrating on the research of optimizing carbon emission parameters related to hole machining, which has displayed that most of them are focused on a single option in diverse processes, for instance drilling, milling, turning, boring, etc., with limited research on compound processes. In order to supplement this deficiency, this article proposes a new optimization process of cutting parameters for hole compound machining based on the “drilling-boring” craft route. By using different drill bits and the same boring tool, the boring allowance can be changed by changing the diameter of the drill bit. This method links two processes with boring allowance as a medium, not only considering carbon emissions, surface quality, and machining efficiency, but also seeking to achieve a balance among the three in the actual production department. Originally, we have summarized the functional relationship between the compound process parameters involved in from drilling to boring process and the objective functions, then subsequently established corresponding multi-objective optimization models. Whereupon, an improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) was propounded to solve the problem. Taking actual machining as an example, we conducted experiments on hole machining on aluminum alloys. Finally, the optimization results were screened for the optimal solution by combining subjective and objective weighting methods with TOPSIS method. The optimized results represent carbon emissions, surface roughness, and processing time have been reduced by 2.4%, 15.1%, and 2.8%, respectively, which demonstrated the effectiveness and correctness of the method researched in this article.
{"title":"An optimization method of compound cutting parameters for hole machining in consideration of low-carbon and surface roughness","authors":"Junbo Tuo , Yongkai Zhao , Bo Liang , Yongliang Li","doi":"10.1016/j.jmapro.2026.01.083","DOIUrl":"10.1016/j.jmapro.2026.01.083","url":null,"abstract":"<div><div>In the actual production process of machine tools, hole machining is the most common machining method, which produces different amounts of carbon emissions depending on the processing methods. Among them, optimizing the cutting parameters of hole machining is a common and effective method to diminish carbon emissions. However, concentrating on the research of optimizing carbon emission parameters related to hole machining, which has displayed that most of them are focused on a single option in diverse processes, for instance drilling, milling, turning, boring, etc., with limited research on compound processes. In order to supplement this deficiency, this article proposes a new optimization process of cutting parameters for hole compound machining based on the “drilling-boring” craft route. By using different drill bits and the same boring tool, the boring allowance can be changed by changing the diameter of the drill bit. This method links two processes with boring allowance as a medium, not only considering carbon emissions, surface quality, and machining efficiency, but also seeking to achieve a balance among the three in the actual production department. Originally, we have summarized the functional relationship between the compound process parameters involved in from drilling to boring process and the objective functions, then subsequently established corresponding multi-objective optimization models. Whereupon, an improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) was propounded to solve the problem. Taking actual machining as an example, we conducted experiments on hole machining on aluminum alloys. Finally, the optimization results were screened for the optimal solution by combining subjective and objective weighting methods with TOPSIS method. The optimized results represent carbon emissions, surface roughness, and processing time have been reduced by 2.4%, 15.1%, and 2.8%, respectively, which demonstrated the effectiveness and correctness of the method researched in this article.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 170-193"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190300","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 : 2026-03-15Epub Date: 2026-02-02DOI: 10.1016/j.jmapro.2026.01.081
Bo Chen , Kelu Wang , Cuiyuan Lu , Hao Yang , Shiqiang Lu , Xin Li , Quan Yao
Selective laser melting (SLM) process parameters critically influence the properties of fabricated components. However, establishing optimal parameters experimentally is challenging due to the complex nonlinear relationship between parameters and quality. To address this, this study proposes an intelligent parameter design method for 17-4PH stainless steel, integrating optimal Latin hypercube sampling (OLHS), machine learning, and multi-objective optimization. The variables considered are laser power, scanning speed, and scanning pitch. Comparisons with Newton-Raphson-based optimization (NBRO), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms reveal that NBRO demonstrates superior predictive performance and convergence accuracy when automatically searching for optimal XGBoost hyperparameters. Its test set R2 improved by 15.63%, 17.79%, and 16.38% for relative density, surface roughness, and microhardness predictions, respectively, compared to pre-optimization. Based on this, NSGA-III is employed for multi-objective optimization, combined with CRITIC-TOPSIS decision-making to obtain optimal process parameters. Experimental validation demonstrates that the prediction errors for relative density, surface roughness, and microhardness of the optimized specimens are only 0.20%, 5.45%, and 0.29%, respectively. This method significantly enhances process exploration efficiency and accuracy, providing a reliable solution for automated process design in additive manufacturing of high-performance material systems.
{"title":"Performance prediction and multi-objective optimization of 17-4PH stainless steel prepared by selective laser melting","authors":"Bo Chen , Kelu Wang , Cuiyuan Lu , Hao Yang , Shiqiang Lu , Xin Li , Quan Yao","doi":"10.1016/j.jmapro.2026.01.081","DOIUrl":"10.1016/j.jmapro.2026.01.081","url":null,"abstract":"<div><div>Selective laser melting (SLM) process parameters critically influence the properties of fabricated components. However, establishing optimal parameters experimentally is challenging due to the complex nonlinear relationship between parameters and quality. To address this, this study proposes an intelligent parameter design method for 17-4PH stainless steel, integrating optimal Latin hypercube sampling (OLHS), machine learning, and multi-objective optimization. The variables considered are laser power, scanning speed, and scanning pitch. Comparisons with Newton-Raphson-based optimization (NBRO), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms reveal that NBRO demonstrates superior predictive performance and convergence accuracy when automatically searching for optimal XGBoost hyperparameters. Its test set R<sup>2</sup> improved by 15.63%, 17.79%, and 16.38% for relative density, surface roughness, and microhardness predictions, respectively, compared to pre-optimization. Based on this, NSGA-III is employed for multi-objective optimization, combined with CRITIC-TOPSIS decision-making to obtain optimal process parameters. Experimental validation demonstrates that the prediction errors for relative density, surface roughness, and microhardness of the optimized specimens are only 0.20%, 5.45%, and 0.29%, respectively. This method significantly enhances process exploration efficiency and accuracy, providing a reliable solution for automated process design in additive manufacturing of high-performance material systems.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 79-100"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191477","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 : 2026-03-15Epub Date: 2026-02-02DOI: 10.1016/j.jmapro.2026.01.090
Jiajia Shen, Kai Wang, Richard Davies, Ken E. Evans, Oana Ghita
Large-format additive manufacturing (LFAM) of high-performance polymers like carbon fibre-reinforced polyaryletherketone (CF/PAEK) faces significant thermal management challenges, where uncontrolled cooling rates and thermal gradients can compromise interlayer bonding and are key drivers for residual stress development. While localized heating has emerged as a promising strategy to modulate thermal histories, predictive models capable of capturing its coupled effects with deposition dynamics in LFAM remain underdeveloped. This study presents a high-fidelity finite element framework to simulate the transient thermal behaviour in LFAM with integrated localized heating. Using the Abaqus AM module, the model incorporates a moving double-ellipsoid heat source to represent pre-deposition heating, sequential element activation for material deposition, and dynamic cooling boundaries. The framework is employed to systematically investigate the influence of critical process parameters-including localized heating power, nozzle-to-heater distance, layer thickness, and printing speed–on the thermal profile at a representative interfacial location. Results demonstrate that localized heating effectively elevates the thermal baseline, reduces cooling rates, and extends the dwell time above the glass transition temperature, thereby promoting conditions favourable for interlayer diffusion. The analysis reveals a strong, non-linear coupling between heating power and printing speed in setting the pre-deposition interface temperature. Furthermore, an optimal balance between layer thickness and heater penetration depth is identified to maximize thermal build-up while avoiding geometric instability. This computational work elucidates the thermal mechanisms governing LFAM with auxiliary heating and provides a validated foundation for optimizing thermal management strategies. The developed framework paves the way for implementing digital twins and physics-informed surrogate models to accelerate the development of robust, high-quality LFAM processes for advanced thermoplastic composites.
{"title":"Computational thermal analysis of large-format additive manufacturing for CF/PAEK with integrated localized heating","authors":"Jiajia Shen, Kai Wang, Richard Davies, Ken E. Evans, Oana Ghita","doi":"10.1016/j.jmapro.2026.01.090","DOIUrl":"10.1016/j.jmapro.2026.01.090","url":null,"abstract":"<div><div>Large-format additive manufacturing (LFAM) of high-performance polymers like carbon fibre-reinforced polyaryletherketone (CF/PAEK) faces significant thermal management challenges, where uncontrolled cooling rates and thermal gradients can compromise interlayer bonding and are key drivers for residual stress development. While localized heating has emerged as a promising strategy to modulate thermal histories, predictive models capable of capturing its coupled effects with deposition dynamics in LFAM remain underdeveloped. This study presents a high-fidelity finite element framework to simulate the transient thermal behaviour in LFAM with integrated localized heating. Using the <span>Abaqus</span> AM module, the model incorporates a moving double-ellipsoid heat source to represent pre-deposition heating, sequential element activation for material deposition, and dynamic cooling boundaries. The framework is employed to systematically investigate the influence of critical process parameters-including localized heating power, nozzle-to-heater distance, layer thickness, and printing speed–on the thermal profile at a representative interfacial location. Results demonstrate that localized heating effectively elevates the thermal baseline, reduces cooling rates, and extends the dwell time above the glass transition temperature, thereby promoting conditions favourable for interlayer diffusion. The analysis reveals a strong, non-linear coupling between heating power and printing speed in setting the pre-deposition interface temperature. Furthermore, an optimal balance between layer thickness and heater penetration depth is identified to maximize thermal build-up while avoiding geometric instability. This computational work elucidates the thermal mechanisms governing LFAM with auxiliary heating and provides a validated foundation for optimizing thermal management strategies. The developed framework paves the way for implementing digital twins and physics-informed surrogate models to accelerate the development of robust, high-quality LFAM processes for advanced thermoplastic composites.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 136-151"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190298","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 : 2026-03-15Epub Date: 2026-02-02DOI: 10.1016/j.jmapro.2026.01.085
Shengnan Hu , Yuming Xie , Xiangchen Meng , Yilong Han , Shenglong Wang , Cheng Shan , Yongxian Huang
Friction stir channeling (FSC) forms internal flow channels within single pass by extracting plasticized materials to the surface with a profiled tool, enabling monolithic cold-plate structures for electric-vehicle battery thermal management. Cast aluminum alloys used in battery housings, however, exhibit limited flowability that leads to irregular geometry and rough inner walls. We reported an ultrasonic-assisted FSC (UaFSC) route for ZL114 cast aluminum alloys that leverages acoustic softening to reduce flow stress, promote upward material flow, and regularize the channel. Rather than exhaustive orthogonal trials, a compact “random seed→neural network→adversarial refinement” workflow learned the multi-parameter process window and yielded high-accuracy predictions of rectangularity, width, height, and a surface-quality index. The model across validation sets achieved 100% accuracy for cover-surface grades and > 80% for geometric metrics. Pareto analysis showed ultrasound increases mean channel height by ∼26% and expanded feasible windows. A coupled Eulerian-Lagrangian finite-element model ascribed these improvements to reduced stress, lower temperature rise, and higher void fractions. Tracer-based kinematics revealed a periodic, probe-entrained flow in UaFSC that recovered wall-normal displacements and smoothed the advancing side, cutting inner-wall roughness. The results clarified the formation mechanisms and provided a data-efficient pathway to robust process design for compact thermal devices.
{"title":"Flow mechanisms and machine learning-based formation optimization on ultrasonic-assisted friction stir channeling of cast aluminum alloys","authors":"Shengnan Hu , Yuming Xie , Xiangchen Meng , Yilong Han , Shenglong Wang , Cheng Shan , Yongxian Huang","doi":"10.1016/j.jmapro.2026.01.085","DOIUrl":"10.1016/j.jmapro.2026.01.085","url":null,"abstract":"<div><div>Friction stir channeling (FSC) forms internal flow channels within single pass by extracting plasticized materials to the surface with a profiled tool, enabling monolithic cold-plate structures for electric-vehicle battery thermal management. Cast aluminum alloys used in battery housings, however, exhibit limited flowability that leads to irregular geometry and rough inner walls. We reported an ultrasonic-assisted FSC (UaFSC) route for ZL114 cast aluminum alloys that leverages acoustic softening to reduce flow stress, promote upward material flow, and regularize the channel. Rather than exhaustive orthogonal trials, a compact “random seed→neural network→adversarial refinement” workflow learned the multi-parameter process window and yielded high-accuracy predictions of rectangularity, width, height, and a surface-quality index. The model across validation sets achieved 100% accuracy for cover-surface grades and > 80% for geometric metrics. Pareto analysis showed ultrasound increases mean channel height by ∼26% and expanded feasible windows. A coupled Eulerian-Lagrangian finite-element model ascribed these improvements to reduced stress, lower temperature rise, and higher void fractions. Tracer-based kinematics revealed a periodic, probe-entrained flow in UaFSC that recovered wall-normal displacements and smoothed the advancing side, cutting inner-wall roughness. The results clarified the formation mechanisms and provided a data-efficient pathway to robust process design for compact thermal devices.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 152-169"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190302","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 : 2026-03-15Epub Date: 2026-02-05DOI: 10.1016/j.jmapro.2026.01.096
Ashish Kumar , Lei Shi , Xiankun Zhang , Xiaochao Liu , Zhixin Xia , Swee Leong Sing , Chuansong Wu , Amitava De
Heterogeneous metallic composites fabricated from dissimilar alloys have attracted growing interest owing to their ability to combine complementary properties and achieve multifunctional performance in advanced engineering applications. Fusion-based additive manufacturing is often employed for such metallic structures, yet it remains constrained by the formation of brittle intermetallic compounds, chemical segregation, and inferior mechanical properties. Solid-state additive manufacturing (SSAM) processes, particularly friction-based techniques such as friction stir additive manufacturing (FSAM), friction surfacing (FS), additive friction stir deposition (AFSD), and additive friction extrusion deposition (AFED), have emerged as promising alternatives. Unlike fusion-based AM, these processes operate below the melting temperature and achieve metallurgical bonding through severe plastic deformation, frictional heating, and dynamic recrystallization. As a result, SSAM effectively suppresses solidification-related defects, reduces residual stresses, and improves interfacial integrity. However, a comprehensive review focused on the fabrication of heterogeneous metallic composites via solid-state friction-based additive manufacturing (SSFAM), particularly AFSD and FS, is still lacking. This article addresses this gap by critically analysing interfacial microstructural evolution, diffusion behaviour, and mechanical performance in dissimilar alloy processed by FS and AFSD. The process–structure–property relationships, identifying current limitations in interfacial control, and evaluating emerging machine learning (ML)–assisted strategies for process optimisation, defect mitigation, and property prediction in SSFAM. By integrating insights from experimental studies, mechanistic modeling, and data-driven approaches, this review highlights the growing role of ML in accelerating SSFAM design and improving the reliability of heterogeneous structures. Overall, this review seeks to serve as a state-of-the-art reference for researchers and practitioners aiming to develop structurally robust multifunctional components through data-informed, next-generation SSFAM techniques.
{"title":"Solid state friction-based additive manufacturing of heterogeneous metallic composites: Recent progress and future prospects","authors":"Ashish Kumar , Lei Shi , Xiankun Zhang , Xiaochao Liu , Zhixin Xia , Swee Leong Sing , Chuansong Wu , Amitava De","doi":"10.1016/j.jmapro.2026.01.096","DOIUrl":"10.1016/j.jmapro.2026.01.096","url":null,"abstract":"<div><div>Heterogeneous metallic composites fabricated from dissimilar alloys have attracted growing interest owing to their ability to combine complementary properties and achieve multifunctional performance in advanced engineering applications. Fusion-based additive manufacturing is often employed for such metallic structures, yet it remains constrained by the formation of brittle intermetallic compounds, chemical segregation, and inferior mechanical properties. Solid-state additive manufacturing (SSAM) processes, particularly friction-based techniques such as friction stir additive manufacturing (FSAM), friction surfacing (FS), additive friction stir deposition (AFSD), and additive friction extrusion deposition (AFED), have emerged as promising alternatives. Unlike fusion-based AM, these processes operate below the melting temperature and achieve metallurgical bonding through severe plastic deformation, frictional heating, and dynamic recrystallization. As a result, SSAM effectively suppresses solidification-related defects, reduces residual stresses, and improves interfacial integrity. However, a comprehensive review focused on the fabrication of heterogeneous metallic composites via solid-state friction-based additive manufacturing (SSFAM), particularly AFSD and FS, is still lacking. This article addresses this gap by critically analysing interfacial microstructural evolution, diffusion behaviour, and mechanical performance in dissimilar alloy processed by FS and AFSD. The process–structure–property relationships, identifying current limitations in interfacial control, and evaluating emerging machine learning (ML)–assisted strategies for process optimisation, defect mitigation, and property prediction in SSFAM. By integrating insights from experimental studies, mechanistic modeling, and data-driven approaches, this review highlights the growing role of ML in accelerating SSFAM design and improving the reliability of heterogeneous structures. Overall, this review seeks to serve as a state-of-the-art reference for researchers and practitioners aiming to develop structurally robust multifunctional components through data-informed, next-generation SSFAM techniques.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 309-335"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191450","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 : 2026-03-15Epub Date: 2026-02-06DOI: 10.1016/j.jmapro.2026.02.005
Liyu Wang , Yutao Wang , Songmei Yuan , Longpeng Li , Hanjun Gao , Obaid Muhammad , Qilin Li , Zhen Li
Thin-walled structural components are widely used in aerospace industries due to their lightweight and high-strength properties. However, their low-rigidity structural characteristics cause machining deformation during processing. For thin-walled components made of continuous silicon carbide fiber-reinforced titanium matrix composites (SiCf/Ti), the simultaneous existence of both metal and fibers, lead to more complex machining deformation than traditional titanium materials or other composites and their deformation mechanism remains unclear. This paper investigates side-milling of SiCf/Ti thin-walled components and revealed that, compared to titanium alloy, their machining process shows global dynamic instability. Specifically, SiCf/Ti components with vertically and horizontally aligned fibers exhibited displacement reductions of 33.2% and 56.93%, respectively, compared to Ti baseline. The machined surface demonstrates the material-response dominance. For vertically aligned fibers, the cutting inlet of SiCf/Ti exhibits lowest stiffness and dominates dynamic instability, with a peak vibration amplitude of 154.3 m/s2. In contrast, horizontally aligned fibers show more severe stiffness collapse at the cutting outlet, reaching 160.7 m/s2. With vertically aligned fibers, the deformation mechanism of SiCf/Ti is dominated by constrained plastic flow of matrix and elastic coordination at interface. Energy is dissipated efficiently through the friction at fiber-matrix interface and propagation of stress wave along fiber direction, which shows a stable dynamic response. In contrast, horizontally aligned fibers induce the cross-scale laminate bending and interlaminar shear deformation. Energy repeatedly superimposes and accumulates within the fibers with difficulty in dissipation, which keep the system in intense vibrational state. This study provides valuable insights for high quality machining of SiCf/Ti thin-walled components.
{"title":"Machining of thin-walled components of fiber-reinforced titanium matrix composites—Dynamic response mechanism of fiber orientation","authors":"Liyu Wang , Yutao Wang , Songmei Yuan , Longpeng Li , Hanjun Gao , Obaid Muhammad , Qilin Li , Zhen Li","doi":"10.1016/j.jmapro.2026.02.005","DOIUrl":"10.1016/j.jmapro.2026.02.005","url":null,"abstract":"<div><div>Thin-walled structural components are widely used in aerospace industries due to their lightweight and high-strength properties. However, their low-rigidity structural characteristics cause machining deformation during processing. For thin-walled components made of continuous silicon carbide fiber-reinforced titanium matrix composites (SiC<sub>f</sub>/Ti), the simultaneous existence of both metal and fibers, lead to more complex machining deformation than traditional titanium materials or other composites and their deformation mechanism remains unclear. This paper investigates side-milling of SiC<sub>f</sub>/Ti thin-walled components and revealed that, compared to titanium alloy, their machining process shows global dynamic instability. Specifically, SiC<sub>f</sub>/Ti components with vertically and horizontally aligned fibers exhibited displacement reductions of 33.2% and 56.93%, respectively, compared to Ti baseline. The machined surface demonstrates the material-response dominance. For vertically aligned fibers, the cutting inlet of SiC<sub>f</sub>/Ti exhibits lowest stiffness and dominates dynamic instability, with a peak vibration amplitude of 154.3 m/s<sup>2</sup>. In contrast, horizontally aligned fibers show more severe stiffness collapse at the cutting outlet, reaching 160.7 m/s<sup>2</sup>. With vertically aligned fibers, the deformation mechanism of SiC<sub>f</sub>/Ti is dominated by constrained plastic flow of matrix and elastic coordination at interface. Energy is dissipated efficiently through the friction at fiber-matrix interface and propagation of stress wave along fiber direction, which shows a stable dynamic response. In contrast, horizontally aligned fibers induce the cross-scale laminate bending and interlaminar shear deformation. Energy repeatedly superimposes and accumulates within the fibers with difficulty in dissipation, which keep the system in intense vibrational state. This study provides valuable insights for high quality machining of SiC<sub>f</sub>/Ti thin-walled components.</div></div>","PeriodicalId":16148,"journal":{"name":"Journal of Manufacturing Processes","volume":"161 ","pages":"Pages 336-351"},"PeriodicalIF":6.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191475","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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