Pub Date : 2026-02-02DOI: 10.1016/j.rcim.2026.103247
Shengjie Jiang, Qijia Qian, Jianhong Liu, Pan Wang, Xiao Zhuang, Di Zhou, Weifang Sun, Jiawei Xiang
Conformance verification in assembly processes is crucial for ensuring manufacturing quality, yet it is often challenged in real production environments by viewpoint variations and individual differences in operator behavior. This paper presents a rotation-invariant conformance verification framework for intelligent assembly, adopting a hybrid modeling paradigm that synergizes data-driven learning with geometric priors. By jointly integrating action recognition, temporal logic validation, and spatial path evaluation, the framework enables fine-grained assessment of deviations from standard operating procedures. The research develops a spatio-temporal-semantic triple-attention network to achieve adaptive, high-accuracy procedural-level action recognition in a data-driven manner. Then, a dynamic state-transition model is introduced to capture temporal violations by online updating of operation transition probabilities. By combining differential chain codes with cyclic shift normalization, the proposed geometry-guided trajectory representation method enables rotation-robust quantification of path deviations in critical assembly processes without requiring multi-view training data. Experiments on our WZU complex product assembly process dataset show that the proposed framework achieves 96.17% accuracy in violation detection, significantly outperforming CNN-LSTM (＋10.39%), I3D (＋1.02%), and MobileNetV3 (＋1.24%), with an end-to-end inference latency under 50 ms, making it suitable for edge deployment. This work provides an efficient, interpretable, and viewpoint-invariant vision-based solution for assembly process monitoring in industrial applications.
{"title":"Intelligent assembly conformance verification for complex products: A rotationally invariant multi-view visual framework","authors":"Shengjie Jiang, Qijia Qian, Jianhong Liu, Pan Wang, Xiao Zhuang, Di Zhou, Weifang Sun, Jiawei Xiang","doi":"10.1016/j.rcim.2026.103247","DOIUrl":"https://doi.org/10.1016/j.rcim.2026.103247","url":null,"abstract":"Conformance verification in assembly processes is crucial for ensuring manufacturing quality, yet it is often challenged in real production environments by viewpoint variations and individual differences in operator behavior. This paper presents a rotation-invariant conformance verification framework for intelligent assembly, adopting a hybrid modeling paradigm that synergizes data-driven learning with geometric priors. By jointly integrating action recognition, temporal logic validation, and spatial path evaluation, the framework enables fine-grained assessment of deviations from standard operating procedures. The research develops a spatio-temporal-semantic triple-attention network to achieve adaptive, high-accuracy procedural-level action recognition in a data-driven manner. Then, a dynamic state-transition model is introduced to capture temporal violations by online updating of operation transition probabilities. By combining differential chain codes with cyclic shift normalization, the proposed geometry-guided trajectory representation method enables rotation-robust quantification of path deviations in critical assembly processes without requiring multi-view training data. Experiments on our WZU complex product assembly process dataset show that the proposed framework achieves 96.17% accuracy in violation detection, significantly outperforming CNN-LSTM (＋10.39%), I3D (＋1.02%), and MobileNetV3 (＋1.24%), with an end-to-end inference latency under 50 ms, making it suitable for edge deployment. This work provides an efficient, interpretable, and viewpoint-invariant vision-based solution for assembly process monitoring in industrial applications.","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"42 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the demand for personalized products increases, manufacturing processes are becoming more complex due to greater variety and uncertainty in product requirements. Traditional manufacturing systems face challenges in adapting to product changes without manual interventions, leading to an increase in product delays and operational costs. Multi-agent manufacturing control systems, a decentralized framework consisting of collaborative agents, have been employed to enhance flexibility and adaptability in manufacturing. However, existing multi-agent system approaches are often initialized with predefined capabilities, limiting their ability to handle new requirements that were not modeled in advance. To address this challenge, this work proposes a large language model-enabled multi-agent framework that enables adaptive matching, translating new product requirements to manufacturing process control at runtime. A product agent, which is a decision-maker for a product, interprets unforeseen product requirements and matches with manufacturing capabilities by dynamically retrieving manufacturing knowledge during runtime. Communication strategies and a decision-making method are also introduced to facilitate adaptive task planning and coordination. The proposed framework was evaluated using an assembly task board testbed across three case studies of increasing complexity. Results demonstrate that the framework can process unforeseen product requirements into executable operations, dynamically discover manufacturing capabilities, and improve resource utilization.
{"title":"Adaptive task planning and coordination in multi-agent manufacturing systems using large language models","authors":"Jonghan Lim , Jiabao Zhao , Ezekiel Hernandez , Ilya Kovalenko","doi":"10.1016/j.rcim.2026.103245","DOIUrl":"10.1016/j.rcim.2026.103245","url":null,"abstract":"<div><div>As the demand for personalized products increases, manufacturing processes are becoming more complex due to greater variety and uncertainty in product requirements. Traditional manufacturing systems face challenges in adapting to product changes without manual interventions, leading to an increase in product delays and operational costs. Multi-agent manufacturing control systems, a decentralized framework consisting of collaborative agents, have been employed to enhance flexibility and adaptability in manufacturing. However, existing multi-agent system approaches are often initialized with predefined capabilities, limiting their ability to handle new requirements that were not modeled in advance. To address this challenge, this work proposes a large language model-enabled multi-agent framework that enables adaptive matching, translating new product requirements to manufacturing process control at runtime. A product agent, which is a decision-maker for a product, interprets unforeseen product requirements and matches with manufacturing capabilities by dynamically retrieving manufacturing knowledge during runtime. Communication strategies and a decision-making method are also introduced to facilitate adaptive task planning and coordination. The proposed framework was evaluated using an assembly task board testbed across three case studies of increasing complexity. Results demonstrate that the framework can process unforeseen product requirements into executable operations, dynamically discover manufacturing capabilities, and improve resource utilization.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103245"},"PeriodicalIF":11.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.rcim.2026.103253
Ali Karevan, Sylvie Nadeau
With the rise of Industry 5.0, wearables have become increasingly common in manufacturing, making effective risk management more critical than ever. Despite this trend, there remains a significant gap in research regarding the risks associated with the simultaneous use of multiple wearables, particularly in complex hybrid systems involving human operators. This study addresses this gap by using an improved Systems-Theoretic Process Analysis combined with Particle Swarm Optimization (STPA-PSO) methodology. Moreover, it introduces a circular, semi-automated methodology (incorporating mitigation measures) that can systematically identify, analyze, quantify, and mitigate risks, including those arising from human error, in the integration of multiple wearables. Three case studies, two assembly lines and one disassembly line, were tested to check the effectiveness of this method. The findings indicate that increased interactions among system components can lead to elevated risk levels. It demonstrates that highlighting the hazardous areas, calibration regulations, and training of workers are high-risk control action scenarios that need to be reduced. This methodology can provide a safer and more efficient integration of wearable technologies in human-centered manufacturing environments.
{"title":"Integrating smart glasses and smart gloves in hybrid assembly/disassembly systems: an STPA-driven semi-automated risk management tool","authors":"Ali Karevan, Sylvie Nadeau","doi":"10.1016/j.rcim.2026.103253","DOIUrl":"10.1016/j.rcim.2026.103253","url":null,"abstract":"<div><div>With the rise of Industry 5.0, wearables have become increasingly common in manufacturing, making effective risk management more critical than ever. Despite this trend, there remains a significant gap in research regarding the risks associated with the simultaneous use of multiple wearables, particularly in complex hybrid systems involving human operators. This study addresses this gap by using an improved Systems-Theoretic Process Analysis combined with Particle Swarm Optimization (STPA-PSO) methodology. Moreover, it introduces a circular, semi-automated methodology (incorporating mitigation measures) that can systematically identify, analyze, quantify, and mitigate risks, including those arising from human error, in the integration of multiple wearables. Three case studies, two assembly lines and one disassembly line, were tested to check the effectiveness of this method. The findings indicate that increased interactions among system components can lead to elevated risk levels. It demonstrates that highlighting the hazardous areas, calibration regulations, and training of workers are high-risk control action scenarios that need to be reduced. This methodology can provide a safer and more efficient integration of wearable technologies in human-centered manufacturing environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103253"},"PeriodicalIF":11.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1016/j.rcim.2026.103249
Wei Ma , Tianliang Hu , Chengrui Zhang , Tieshuang Zhu
Robotic laser remanufacturing is an energy-saving, material-saving, time-saving, and environmentally beneficial method to restore the functionality and performance of failed turbine blades. During turbine blade remanufacturing, the cladding posture influences both the size and shape of the laser spot, which can dramatically affect the remanufacturing quality. Due to the considerable deformation and abrasion, the accurate and smooth posture control synchronized with the repairing position motion confronted with difficulties. To overcome these difficulties, a posture optimization method including 2D angle smoothing and NURBS synchronization based on-line reverse modeling is proposed. Firstly, the reverse model for the wear turbine blade is efficiently established by the systematical integration of the linear laser scanner and robotic laser cladding platform, which provides accurate information for the macroscopical shape and the microscopical surface morphology. Next, by establishing the working plane, the original surface normal vector is decomposed into the rolling and pitching angles, and the polynomial regression method with a sliding window is employed to self-adaptively remove the vibration as well as keep the cladding nozzle dynamically perpendicular to the repairing surface. Further, to realize the continuous transformation of the cladding postures, the smooth cladding path in the posture space is generated based on 3D NURBS fitting with the independent parameter, and considering exact correspondence between the position and posture, a synchronization method based on 2D NURBS interpolation is proposed for the accurate posture control. Finally, the comparison experiments are conducted, and the results suggest that dynamical cladding posture control with vibration-free optimization can realize a smoother motion of industrial robots and reduce surface waviness as well as evenly improve the hardness of turbine blades remanufactured with Ti-6Al-4 V powder. This work demonstrates the improvement of the dynamical posture on the forming quality and provides a practicable method of posture control for freeform surface remanufacturing.
{"title":"Laser cladding posture optimization method for freeform surface remanufacturing and its improvement on forming quality of turbine blade remanufactured with Ti-6Al-4 V powder","authors":"Wei Ma , Tianliang Hu , Chengrui Zhang , Tieshuang Zhu","doi":"10.1016/j.rcim.2026.103249","DOIUrl":"10.1016/j.rcim.2026.103249","url":null,"abstract":"<div><div>Robotic laser remanufacturing is an energy-saving, material-saving, time-saving, and environmentally beneficial method to restore the functionality and performance of failed turbine blades. During turbine blade remanufacturing, the cladding posture influences both the size and shape of the laser spot, which can dramatically affect the remanufacturing quality. Due to the considerable deformation and abrasion, the accurate and smooth posture control synchronized with the repairing position motion confronted with difficulties. To overcome these difficulties, a posture optimization method including 2D angle smoothing and NURBS synchronization based on-line reverse modeling is proposed. Firstly, the reverse model for the wear turbine blade is efficiently established by the systematical integration of the linear laser scanner and robotic laser cladding platform, which provides accurate information for the macroscopical shape and the microscopical surface morphology. Next, by establishing the working plane, the original surface normal vector is decomposed into the rolling and pitching angles, and the polynomial regression method with a sliding window is employed to self-adaptively remove the vibration as well as keep the cladding nozzle dynamically perpendicular to the repairing surface. Further, to realize the continuous transformation of the cladding postures, the smooth cladding path in the posture space is generated based on 3D NURBS fitting with the independent parameter, and considering exact correspondence between the position and posture, a synchronization method based on 2D NURBS interpolation is proposed for the accurate posture control. Finally, the comparison experiments are conducted, and the results suggest that dynamical cladding posture control with vibration-free optimization can realize a smoother motion of industrial robots and reduce surface waviness as well as evenly improve the hardness of turbine blades remanufactured with Ti-6Al-4 V powder. This work demonstrates the improvement of the dynamical posture on the forming quality and provides a practicable method of posture control for freeform surface remanufacturing.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103249"},"PeriodicalIF":11.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1016/j.rcim.2026.103251
Weibo Li , Jie Zhang , Jiazhen Pang , Dangdang Zheng
In the context of smart manufacturing and robotic technology integration, Boundary representation (B-rep) model feature recognition is a crucial technical bottleneck linking design intent to robotic automated manufacturing. As a core link bridging computer-aided design and robotic machining execution, it offers robots semantic analysis of manufacturable features. However, existing methods are limited to analyze face features in the B-rep model and neglect in-depth modeling of the topological-geometric associations among geometric elements. This makes it hard for robots to accurately perceive multi-features. To address these issues, this paper proposes a heterogeneous graph learning framework named BrepHGNet. Firstly, a face-vertex interaction heterogeneous graph descriptor for B-rep models is constructed. Two distinct types of nodes, namely face nodes and vertex nodes, along with two types of relationships, face-adjacency-face and vertex-subordination-face, are defined. This construction serves to retain the hierarchical topological structure and geometric information inherent in B-rep models. Secondly, a vertex global shape mapping approach is introduced. By computing the Euclidean distances from vertices to sampling points on other faces, this method captures the impact of vertices within complex geometric structures. Thirdly, a heterogeneous graph neural network is built. Node features are updated via message passing and aggregation mechanisms tailored to different relationships. Finally, comparative experiments conducted on the manufacturing and real word datasets demonstrate that the proposed face-vertex interaction heterogeneous graph can effectively capture the internal geometric-topological associations within B-rep models, providing a new technical pathway for B-rep model feature recognition.
{"title":"BrepHGNet: A face-vertex interaction heterogeneous graph neural network for feature recognition","authors":"Weibo Li , Jie Zhang , Jiazhen Pang , Dangdang Zheng","doi":"10.1016/j.rcim.2026.103251","DOIUrl":"10.1016/j.rcim.2026.103251","url":null,"abstract":"<div><div>In the context of smart manufacturing and robotic technology integration, Boundary representation (B-rep) model feature recognition is a crucial technical bottleneck linking design intent to robotic automated manufacturing. As a core link bridging computer-aided design and robotic machining execution, it offers robots semantic analysis of manufacturable features. However, existing methods are limited to analyze face features in the B-rep model and neglect in-depth modeling of the topological-geometric associations among geometric elements. This makes it hard for robots to accurately perceive multi-features. To address these issues, this paper proposes a heterogeneous graph learning framework named BrepHGNet. Firstly, a face-vertex interaction heterogeneous graph descriptor for B-rep models is constructed. Two distinct types of nodes, namely face nodes and vertex nodes, along with two types of relationships, face-adjacency-face and vertex-subordination-face, are defined. This construction serves to retain the hierarchical topological structure and geometric information inherent in B-rep models. Secondly, a vertex global shape mapping approach is introduced. By computing the Euclidean distances from vertices to sampling points on other faces, this method captures the impact of vertices within complex geometric structures. Thirdly, a heterogeneous graph neural network is built. Node features are updated via message passing and aggregation mechanisms tailored to different relationships. Finally, comparative experiments conducted on the manufacturing and real word datasets demonstrate that the proposed face-vertex interaction heterogeneous graph can effectively capture the internal geometric-topological associations within B-rep models, providing a new technical pathway for B-rep model feature recognition.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103251"},"PeriodicalIF":11.4,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autonomous grasping has long been a central topic in robotics, yet deployment in small and medium-sized enterprises (SMEs) is still hindered by low-level robot programming and the lack of natural language interaction. Recent Vision-Language-Action models (VLAs) allow robots to interpret natural language commands for intuitive interaction and control, but they still exhibit output uncertainty and are not yet well suited to directly generating reliable, precise actions in safety-critical industrial contexts. To address this gap, we present VL-GRiP3, a hierarchical Vision-Language model (VLM)-enabled pipeline for autonomous 3D robotic grasping that bridges natural language interaction and accurate, reliable manipulation in SME settings. The framework decomposes language understanding, perception, and action planning in a transparent modular architecture, improving flexibility and interpretability. Within this architecture, a single VLM backbone handles natural language interpretation, target perception, and high-level action planning. CAD-augmented point cloud registration then mitigates occlusions in single RGB-D views while keeping hardware cost low, and an M2T2-based grasp planner predicts accurate 3D grasp poses that explicitly account for complex object geometry from the augmented point cloud, enabling reliable manipulation of irregular industrial parts. Experiments show that our fine-tuned VLM modules achieve segmentation performance comparable to YOLOv8n, and VL-GRiP3 attains a 94.67% success rate over 150 randomized grasping trials. A comparative evaluation against state-of-the-art end-to-end VLAs further indicates that our modular, CAD-augmented design with explicit 3D grasp pose prediction yields more reliable and controllable behavior for SME manufacturing applications.
{"title":"VL-GRiP3: A hierarchical pipeline leveraging vision-language models for autonomous robotic 3D grasping","authors":"Mirco Polonara , Xingyu Yang , Luca Carbonari , Xuping Zhang","doi":"10.1016/j.rcim.2026.103244","DOIUrl":"10.1016/j.rcim.2026.103244","url":null,"abstract":"<div><div>Autonomous grasping has long been a central topic in robotics, yet deployment in small and medium-sized enterprises (SMEs) is still hindered by low-level robot programming and the lack of natural language interaction. Recent Vision-Language-Action models (VLAs) allow robots to interpret natural language commands for intuitive interaction and control, but they still exhibit output uncertainty and are not yet well suited to directly generating reliable, precise actions in safety-critical industrial contexts. To address this gap, we present VL-GRiP3, a hierarchical Vision-Language model (VLM)-enabled pipeline for autonomous 3D robotic grasping that bridges natural language interaction and accurate, reliable manipulation in SME settings. The framework decomposes language understanding, perception, and action planning in a transparent modular architecture, improving flexibility and interpretability. Within this architecture, a single VLM backbone handles natural language interpretation, target perception, and high-level action planning. CAD-augmented point cloud registration then mitigates occlusions in single RGB-D views while keeping hardware cost low, and an M2T2-based grasp planner predicts accurate 3D grasp poses that explicitly account for complex object geometry from the augmented point cloud, enabling reliable manipulation of irregular industrial parts. Experiments show that our fine-tuned VLM modules achieve segmentation performance comparable to YOLOv8n, and VL-GRiP3 attains a 94.67% success rate over 150 randomized grasping trials. A comparative evaluation against state-of-the-art end-to-end VLAs further indicates that our modular, CAD-augmented design with explicit 3D grasp pose prediction yields more reliable and controllable behavior for SME manufacturing applications.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103244"},"PeriodicalIF":11.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.rcim.2026.103248
Jiale Wu , Qi Liu , Jiachen Ye , Kai Ren , Yanlong Cao
Process planning is critical for robot-assisted Directed Energy Deposition (DED) on curved component manufacturing, which involves various factors such as laser parameters, nozzle orientation, velocity strategy, and non-printing paths. For the DED process with coaxial powder feeding, an 8-axis robot system consisting of a 6-axis robotic arm and a 2-axis positioner has 3 functionally redundant degrees of freedom, including axial rotation of the nozzle, tilt and rotation of the positioner. This research aims to achieve the optimal nozzle orientation for deposition by utilizing the redundancy of the 8-axis robot system. A nozzle orientation evaluation metric is initially proposed to evaluate that the nozzle axis vector is opposite to the gravity and aligned with the build surface normal vector. Subsequently, robot trajectory planning strategies with different nozzle orientation constraints are designed for revolved blades. Finally, actual printing was performed based on numerical simulation. The developed theoretical optimal robot trajectory planning strategies have achieved geometric accuracy primarily within ±1 mm. The method can be adapted to the fabrication of more complex curved components by appropriately relaxing the nozzle orientation constraints.
{"title":"Constraint-based redundancy resolution for nozzle orientation in 8-axis robot-assisted DED: A case on revolved components","authors":"Jiale Wu , Qi Liu , Jiachen Ye , Kai Ren , Yanlong Cao","doi":"10.1016/j.rcim.2026.103248","DOIUrl":"10.1016/j.rcim.2026.103248","url":null,"abstract":"<div><div>Process planning is critical for robot-assisted Directed Energy Deposition (DED) on curved component manufacturing, which involves various factors such as laser parameters, nozzle orientation, velocity strategy, and non-printing paths. For the DED process with coaxial powder feeding, an 8-axis robot system consisting of a 6-axis robotic arm and a 2-axis positioner has 3 functionally redundant degrees of freedom, including axial rotation of the nozzle, tilt and rotation of the positioner. This research aims to achieve the optimal nozzle orientation for deposition by utilizing the redundancy of the 8-axis robot system. A nozzle orientation evaluation metric is initially proposed to evaluate that the nozzle axis vector is opposite to the gravity and aligned with the build surface normal vector. Subsequently, robot trajectory planning strategies with different nozzle orientation constraints are designed for revolved blades. Finally, actual printing was performed based on numerical simulation. The developed theoretical optimal robot trajectory planning strategies have achieved geometric accuracy primarily within ±1 mm. The method can be adapted to the fabrication of more complex curved components by appropriately relaxing the nozzle orientation constraints.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103248"},"PeriodicalIF":11.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.rcim.2026.103250
Zhongqun Li , Qunli Shen , Wenjing Wu , Hailu Fan
Heavy-duty industrial robots are increasingly applied in machining due to their large workspace and flexible posture adjustment capabilities. However, their inherent low stiffness makes them highly susceptible to chatter during machining, which significantly restricts their further development and application. Conducting high-precision dynamic modeling for chatter prediction and adopting active/ passive chatter suppression techniques are crucial to achieving chatter-free machining. This paper systematically reviews the global research progress in the dynamics of heavy-duty robotic machining systems. Firstly, it outlines the core technologies and typical applications of heavy-duty robots in machining. Secondly, it comprehensively compares various modeling and prediction methods for the dynamic characteristics of heavy-duty robot end-effectors. Thirdly, it deeply analyzes the two main chatter mechanisms in robotic machining—regenerative chatter and modal coupling chatter—and their corresponding analytical and prediction methods. Subsequently, several representative chatter suppression technologies are summarized. Finally, conclusions are drawn based on the above analysis, and key directions for future research are proposed. Through a comprehensive review and in-depth exploration of the dynamics research of heavy-duty robot machining, this paper aims to provide valuable references and guidance for scholars in related fields.
{"title":"Research progress in the dynamics of heavy-duty robots from the perspective of machining process","authors":"Zhongqun Li , Qunli Shen , Wenjing Wu , Hailu Fan","doi":"10.1016/j.rcim.2026.103250","DOIUrl":"10.1016/j.rcim.2026.103250","url":null,"abstract":"<div><div>Heavy-duty industrial robots are increasingly applied in machining due to their large workspace and flexible posture adjustment capabilities. However, their inherent low stiffness makes them highly susceptible to chatter during machining, which significantly restricts their further development and application. Conducting high-precision dynamic modeling for chatter prediction and adopting active/ passive chatter suppression techniques are crucial to achieving chatter-free machining. This paper systematically reviews the global research progress in the dynamics of heavy-duty robotic machining systems. Firstly, it outlines the core technologies and typical applications of heavy-duty robots in machining. Secondly, it comprehensively compares various modeling and prediction methods for the dynamic characteristics of heavy-duty robot end-effectors. Thirdly, it deeply analyzes the two main chatter mechanisms in robotic machining—regenerative chatter and modal coupling chatter—and their corresponding analytical and prediction methods. Subsequently, several representative chatter suppression technologies are summarized. Finally, conclusions are drawn based on the above analysis, and key directions for future research are proposed. Through a comprehensive review and in-depth exploration of the dynamics research of heavy-duty robot machining, this paper aims to provide valuable references and guidance for scholars in related fields.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103250"},"PeriodicalIF":11.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.rcim.2026.103246
Yue Teng , Tianhong Wang , Xinyu Li , Chunjiang Zhang , Liang Gao , Ziyue Wang , Weiming Shen
Mass customization represents a critical evolution in modern manufacturing. To achieve efficient large-scale production of low-volume and high-variety products, designing optimized robot cells for flexible automation has become a universal challenge for manufacturers. While our prior research has effectively addressed scheduling problem in robot cells with discrete processing machines (DPMs, each processing one job at a time), the integration of both DPMs and batch processing machines (BPMs, each process multiple jobs simultaneously) introduces significant complexity for fully utilizing productive capacities. This paper investigates the Multi-Type Machine Robot Cell Scheduling Problem (MRCSP) incorporating both DPMs and BPMs and the objective is to minimize makespan. Firstly, a mixed-integer linear programming (MILP) model is formulated to describe MRCSP exactly. Recognizing the challenge of converting batch-aware two-vector encoding into feasible schedules, an adaptive active decoding strategy termed selective insertion batch decoding (SIBD) is proposed. An improved genetic algorithm (IGA) is then developed integrating this tailored encoding/decoding approach and a novel disjunctive graph. Furthermore, a batch neighborhood structure (BN) leveraging problem-specific characteristics is designed. The proposed MILP and IGA were validated on three FJSP-BPM benchmarks. Computational results demonstrate that IGA outperforms existing methods across all instances. In real-world production case studies, the approach achieved a 15.02 % average makespan reduction compared to prior methods, significantly improving resource utilization at a robot cell in southern China.
{"title":"Adaptive active decoding and novel disjunctive graph-based improved genetic algorithm for multi-type machine robot cell scheduling in mass customization","authors":"Yue Teng , Tianhong Wang , Xinyu Li , Chunjiang Zhang , Liang Gao , Ziyue Wang , Weiming Shen","doi":"10.1016/j.rcim.2026.103246","DOIUrl":"10.1016/j.rcim.2026.103246","url":null,"abstract":"<div><div>Mass customization represents a critical evolution in modern manufacturing. To achieve efficient large-scale production of low-volume and high-variety products, designing optimized robot cells for flexible automation has become a universal challenge for manufacturers. While our prior research has effectively addressed scheduling problem in robot cells with discrete processing machines (DPMs, each processing one job at a time), the integration of both DPMs and batch processing machines (BPMs, each process multiple jobs simultaneously) introduces significant complexity for fully utilizing productive capacities. This paper investigates the Multi-Type Machine Robot Cell Scheduling Problem (MRCSP) incorporating both DPMs and BPMs and the objective is to minimize makespan. Firstly, a mixed-integer linear programming (MILP) model is formulated to describe MRCSP exactly. Recognizing the challenge of converting batch-aware two-vector encoding into feasible schedules, an adaptive active decoding strategy termed selective insertion batch decoding (SIBD) is proposed. An improved genetic algorithm (IGA) is then developed integrating this tailored encoding/decoding approach and a novel disjunctive graph. Furthermore, a batch neighborhood structure (BN) leveraging problem-specific characteristics is designed. The proposed MILP and IGA were validated on three FJSP-BPM benchmarks. Computational results demonstrate that IGA outperforms existing methods across all instances. In real-world production case studies, the approach achieved a 15.02 % average makespan reduction compared to prior methods, significantly improving resource utilization at a robot cell in southern China.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103246"},"PeriodicalIF":11.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.rcim.2026.103243
Jiaqi Sun , Ze’an Liu , Yanfang Feng , Feng Pan , Ke Xiang , Xuanyin Wang
The welding robot system plays a crucial role in welding production in the construction industry. However, existing welding trajectory planning methods face challenges in achieving both accuracy and efficiency when dealing with large-scale steel structure workpieces that feature numerous weld seams with complex spatial distributions. Inspired by human welders, who rely on visual perception to assemble components and determine welding trajectories based on feature edges and relative component placement, a novel and flexible welding trajectory planning system based on vision-guided virtual assembly is proposed. The proposed system consists of three core tasks. Firstly, a process for extracting candidate weld seams from CAD mesh models is proposed to delineate potential welding areas for each component. Secondly, a point cloud registration process with multi-scale feature focusing is proposed to achieve accurate vision-guided virtual assembly of the workpiece. Finally, based on the virtual assembly result, a novel process for robotic welding trajectory planning based on the joint analysis of geometric feature edge clusters and spatial mesh distribution is proposed to generate safe and effective robot programs. According to an extensive series of experiments, the proposed robotic welding trajectory planning system effectively overcomes the limitations of existing methods, and achieves accurate and efficient welding of steel structure workpieces in the construction industry.
{"title":"A novel robotic welding trajectory planning system for steel structure workpieces based on vision-guided virtual assembly","authors":"Jiaqi Sun , Ze’an Liu , Yanfang Feng , Feng Pan , Ke Xiang , Xuanyin Wang","doi":"10.1016/j.rcim.2026.103243","DOIUrl":"10.1016/j.rcim.2026.103243","url":null,"abstract":"<div><div>The welding robot system plays a crucial role in welding production in the construction industry. However, existing welding trajectory planning methods face challenges in achieving both accuracy and efficiency when dealing with large-scale steel structure workpieces that feature numerous weld seams with complex spatial distributions. Inspired by human welders, who rely on visual perception to assemble components and determine welding trajectories based on feature edges and relative component placement, a novel and flexible welding trajectory planning system based on vision-guided virtual assembly is proposed. The proposed system consists of three core tasks. Firstly, a process for extracting candidate weld seams from CAD mesh models is proposed to delineate potential welding areas for each component. Secondly, a point cloud registration process with multi-scale feature focusing is proposed to achieve accurate vision-guided virtual assembly of the workpiece. Finally, based on the virtual assembly result, a novel process for robotic welding trajectory planning based on the joint analysis of geometric feature edge clusters and spatial mesh distribution is proposed to generate safe and effective robot programs. According to an extensive series of experiments, the proposed robotic welding trajectory planning system effectively overcomes the limitations of existing methods, and achieves accurate and efficient welding of steel structure workpieces in the construction industry.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"100 ","pages":"Article 103243"},"PeriodicalIF":11.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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