Robotics and Computer-integrated Manufacturing最新文献_第2页

Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-02-10 DOI: 10.1016/j.rcim.2025.102982

Chong Chen , Kuanhong Zhao , Jiewu Leng , Chao Liu , Junming Fan , Pai Zheng

In Industry 5.0, where human ingenuity is combined with cutting-edge technologies such as artificial intelligence (AI) and robotics to revolutionize manufacturing with a focus on sustainability and human well-being, Digital Twins (DT) have become essential to real-time optimization. However, the complexity of managing DT for large-scale systems poses challenges in terms of data transmission, analytics, and advanced applications, which can be potentially addressed by Large Language Model (LLM). This research firstly performs a literature review to study the roles and functions of LLM in DT in the context of Industry 5.0. Subsequently, we propose a framework named Interactive-DT for LLM-DT integration that reveals the technical pathway for how LLM can be effectively integrated and function within DT environments. Within this framework, the roles and functionalities of LLM at the edge layer, DT layer, and service layer are elaborated upon. Finally, the identified research gaps and prospects for the integration of LLM and DT are outlined and discussed. The research outcomes of this paper highlight the potential of LLM to augment DT capabilities through improved construction and operation, enhanced cloud-edge collaboration, and sophisticated data analytics, ultimately promoting industrial practices that are both efficient and aligned with human-centric and sustainability principles in Industry 5.0.

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引用次数: 0

Macro-mini collaborative manipulator system for welding in confined environments

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-02-09 DOI: 10.1016/j.rcim.2025.102975

Erhui Sun , Josue Camacho-Arreguin , Junfu Zhou , Max Liebenschutz-Jones , Tianyi Zeng , Max Keedwell , Dragos Axinte , Andy Norton , Abdelkhalick Mohammad

Welding plays an important role in a wide range of industries, including aviation, aerospace, automobile manufacturing, and nuclear and chemical plants, all of which contain critical industrial assets. However, confined spaces and complex structures in these environments severely restrict the accessibility and functionality of in-situ welding tasks. Therefore, to enable welding operations in constrained spaces, a macro-mini collaborative manipulator system with multiple Degrees of Freedom (multi-DoF) is proposed in this paper. The collaborative system consists of a 6-DoF macro robotic arm and a novel 2-DoF slim mini manipulator. The macro manipulator (i.e., the robotic arm) provides large-scale movement to position the slim mini manipulator within confined environments. The slim mini manipulator, which features a novel serial mechanism, then adjusts and controls the pose of the end-effector (welding torch) to perform welding tasks in spaces that the macro manipulator cannot access. Given the novel design of the mini manipulator, kinematic and Jacobian modelling have been developed to enable intimate and accurate control of the collaborative welding system. The collaboration between the macro and mini manipulators occurs not only for individual movements but also at the level when compensatory movements are performed on each system to enable error compensation for the end-effector (i.e., welding torch). Finally, validation experiments of the collaborative manipulator system have been conducted in confined scenarios to verify its functionality and performance.

{"title":"Macro-mini collaborative manipulator system for welding in confined environments","authors":"Erhui Sun , Josue Camacho-Arreguin , Junfu Zhou , Max Liebenschutz-Jones , Tianyi Zeng , Max Keedwell , Dragos Axinte , Andy Norton , Abdelkhalick Mohammad","doi":"10.1016/j.rcim.2025.102975","DOIUrl":"10.1016/j.rcim.2025.102975","url":null,"abstract":"<div><div>Welding plays an important role in a wide range of industries, including aviation, aerospace, automobile manufacturing, and nuclear and chemical plants, all of which contain critical industrial assets. However, confined spaces and complex structures in these environments severely restrict the accessibility and functionality of in-situ welding tasks. Therefore, to enable welding operations in constrained spaces, a macro-mini collaborative manipulator system with multiple Degrees of Freedom (multi-DoF) is proposed in this paper. The collaborative system consists of a 6-DoF macro robotic arm and a novel 2-DoF slim mini manipulator. The macro manipulator (i.e., the robotic arm) provides large-scale movement to position the slim mini manipulator within confined environments. The slim mini manipulator, which features a novel serial mechanism, then adjusts and controls the pose of the end-effector (welding torch) to perform welding tasks in spaces that the macro manipulator cannot access. Given the novel design of the mini manipulator, kinematic and Jacobian modelling have been developed to enable intimate and accurate control of the collaborative welding system. The collaboration between the macro and mini manipulators occurs not only for individual movements but also at the level when compensatory movements are performed on each system to enable error compensation for the end-effector (i.e., welding torch). Finally, validation experiments of the collaborative manipulator system have been conducted in confined scenarios to verify its functionality and performance.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102975"},"PeriodicalIF":9.1,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robotic grinding of complex surfaces with an internal structured compliant tool: Multi-performance optimization in confined spaces

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-30 DOI: 10.1016/j.rcim.2025.102974

Mingcong Li , Wenxi Wang , Lai Zou , Chong Lv , Junjie Zhang , Yun Huang

Robotic Compliant grinding of complex surfaces with limited space is a crucial and challenging process for the production of the high-performance components such as blisk. The trajectory-varying tool attitude, resulting from the complex structure, introduces an inherent complexity to the contact state for material removal. This, in turn, influences grinding performance and tool life, as well as robot kinematic performance. Thus, a quantitative model between the processing parameters and convoluted material removal process was established. A multi-indicator optimization (MIO) model considering robot kinematics, grit trajectory continuity and tool longevity was developed and solved by the adaptively controlled differential evolutionary (ACDE) algorithm. The results demonstrate that the root mean square error (RMSE) of the predicted contour is 6.45–9.48 μm with different dwell times featuring maximum depths from 47.62 to 158.31 μm. Meanwhile, the RMSE for the three-dimensional morphology with varying tool attitude was less than 12.75 μm. Furthermore, the tool attitudes employing MIO enabled the smoothing grinding of an blisk part featuring a channel as narrow as 23.41 cm, which avoids robot joint mutations and reduces the jerk variation from 0.81 to 0.04 rad/s³. The uniform material removal achieved over the entire annular root surface exhibited a mean error of 0.013–0.016 mm for a pre-set removal depth of 0.3 mm.

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引用次数: 0

Template concept for VR environments: A case study in VR-based safety training for human–robot collaboration

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-29 DOI: 10.1016/j.rcim.2025.102973

Morteza Dianatfar , Eeva Järvenpää , Niko Siltala , Minna Lanz

The industry 4.0 vision has accelerated technology development, particularly in the use of augmented reality (AR) and virtual reality (VR) in industry, such as the metaverse. However, creating VR environments is known to be a laborious task, which means their full potential is not yet fully utilized. There is a need for a reusable VR model that enables faster generation and population of VR environments. This research aims to find solutions for quicker development and deployment of VR environments in an industrial context. Specifically, this paper proposes one solution for creating and modifying these environments more efficiently. The focus of the research, along with the associated industrial use cases, is to develop safety training for human–robot collaboration in final assembly scenarios using VR environments. The paper will introduce a template concept, which includes individual templates and the full architecture to deploy these templates, allowing for faster modification of VR environments to meet specific use case needs. This template concept is developed using two separate use cases from academia and industry.

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引用次数: 0

Iterative offline trajectory correction based on dynamic model for compensating robot-dependent errors in robotic machining

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-29 DOI: 10.1016/j.rcim.2025.102960

Valentin Dambly , Bryan Olivier , Edouard Rivière-Lorphèvre , François Ducobu , Olivier Verlinden

As manufacturing demands shift towards enhanced part geometries and materials, the need for flexibility in production has driven interest in robotic machining. This fast-growing technology offers advantages like cost-effectiveness, adaptability, and easy deployment, making it suitable for agile production lines. However, robotic machining encounters accuracy challenges due to inherent robot flexibility, causing deviations and vibrations.

The positioning error along a robotic machining trajectory is composed of two contributions: the steady-state error and the transient. This research addresses these challenges through compensation methods based on a robotic cell equipped with a Stäubli TX200 and its digital shadow. By proposing trajectory corrections based on the results from virtual machining simulator including the robot dynamical model, the study aims to compensate the static and dynamic deviations, responsible for steady-state and transient errors respectively. To achieve this, the trajectory is discretised in elementary sections, modelled with Hermite splines and connected by nodes that are iteratively repositioned in space based on the error estimated from the dynamics simulation and weighted along the tool path.

Simulations and experiments are carried out in Aluminium 6082 to demonstrate the gain of iterative compensation algorithm. The error reduction encountered in simulation is successfully confirmed in experimental cases, within the repeatability tolerance of the robot, decreasing the steady-state error by 90% and about 60% in transient phases.

{"title":"Iterative offline trajectory correction based on dynamic model for compensating robot-dependent errors in robotic machining","authors":"Valentin Dambly , Bryan Olivier , Edouard Rivière-Lorphèvre , François Ducobu , Olivier Verlinden","doi":"10.1016/j.rcim.2025.102960","DOIUrl":"10.1016/j.rcim.2025.102960","url":null,"abstract":"<div><div>As manufacturing demands shift towards enhanced part geometries and materials, the need for flexibility in production has driven interest in robotic machining. This fast-growing technology offers advantages like cost-effectiveness, adaptability, and easy deployment, making it suitable for agile production lines. However, robotic machining encounters accuracy challenges due to inherent robot flexibility, causing deviations and vibrations.</div><div>The positioning error along a robotic machining trajectory is composed of two contributions: the steady-state error and the transient. This research addresses these challenges through compensation methods based on a robotic cell equipped with a Stäubli TX200 and its digital shadow. By proposing trajectory corrections based on the results from virtual machining simulator including the robot dynamical model, the study aims to compensate the static and dynamic deviations, responsible for steady-state and transient errors respectively. To achieve this, the trajectory is discretised in elementary sections, modelled with Hermite splines and connected by nodes that are iteratively repositioned in space based on the error estimated from the dynamics simulation and weighted along the tool path.</div><div>Simulations and experiments are carried out in Aluminium 6082 to demonstrate the gain of iterative compensation algorithm. The error reduction encountered in simulation is successfully confirmed in experimental cases, within the repeatability tolerance of the robot, decreasing the steady-state error by 90% and about 60% in transient phases.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102960"},"PeriodicalIF":9.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072415","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}

引用次数: 0

Towards a trusted synchronized decision-making method for social production logistics systems based on blockchain and digital twin

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-28 DOI: 10.1016/j.rcim.2025.102965

Zhongfei Zhang , Ting Qu , George Q. Huang , Yongheng Zhang , Kuo Zhao , Jun Zhang

With the growth of personalized demands, manufacturers need to dynamically collaborate with external resources, forming social production logistics systems (SPLS). The complexity and dynamic nature of these systems increase management difficulty, rendering traditional static decision-making methods unsuitable. This study proposes a reliable dynamic collaborative control method for SPLS in discrete manufacturing environments. It aims to provide a secure and controllable collaborative platform for multiple participants, enhancing the system's resilience to disturbances in dynamic environments. A blockchain and digital twin-based trusted synchronized decision-making framework is designed, enabling real-time and reliable acquisition of comprehensive information to support efficient decision-making. Simultaneously, a blockchain smart contract tree-based trusted synchronized decision-making mechanism is proposed to address dynamic disturbances. Utilizing a collaborative optimization algorithm, the "production-distribution-warehousing" collaborative decision model is optimally coordinated to achieve efficient resource allocation and process management. Using the home appliance manufacturing industry chain as a case study, results show that the proposed trusted synchronized control method outperforms the non-trusted synchronized control method, resulting in a 35.3 % reduction in total system costs and an enhancement in the collaborative operational efficiency of the production logistics system, and ensures reliable and efficient system operation in a dynamic demand environment. This research provides valuable references for the operational management of future production logistics systems.

{"title":"Towards a trusted synchronized decision-making method for social production logistics systems based on blockchain and digital twin","authors":"Zhongfei Zhang , Ting Qu , George Q. Huang , Yongheng Zhang , Kuo Zhao , Jun Zhang","doi":"10.1016/j.rcim.2025.102965","DOIUrl":"10.1016/j.rcim.2025.102965","url":null,"abstract":"<div><div>With the growth of personalized demands, manufacturers need to dynamically collaborate with external resources, forming social production logistics systems (SPLS). The complexity and dynamic nature of these systems increase management difficulty, rendering traditional static decision-making methods unsuitable. This study proposes a reliable dynamic collaborative control method for SPLS in discrete manufacturing environments. It aims to provide a secure and controllable collaborative platform for multiple participants, enhancing the system's resilience to disturbances in dynamic environments. A blockchain and digital twin-based trusted synchronized decision-making framework is designed, enabling real-time and reliable acquisition of comprehensive information to support efficient decision-making. Simultaneously, a blockchain smart contract tree-based trusted synchronized decision-making mechanism is proposed to address dynamic disturbances. Utilizing a collaborative optimization algorithm, the \"production-distribution-warehousing\" collaborative decision model is optimally coordinated to achieve efficient resource allocation and process management. Using the home appliance manufacturing industry chain as a case study, results show that the proposed trusted synchronized control method outperforms the non-trusted synchronized control method, resulting in a 35.3 % reduction in total system costs and an enhancement in the collaborative operational efficiency of the production logistics system, and ensures reliable and efficient system operation in a dynamic demand environment. This research provides valuable references for the operational management of future production logistics systems.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102965"},"PeriodicalIF":9.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072447","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}

引用次数: 0

Human-robot and robot-robot sound interaction using a 3-Dimensional Acoustic Ranging (3DAR) in audible and inaudible frequency

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-27 DOI: 10.1016/j.rcim.2025.102970

Semin Ahn , Jae-Hoon Kim , Jun Heo , Sung-Hoon Ahn

Highly reliable sound-based interaction in noisy and dynamic environments is a known challenge with simultaneous Human-Robot Interaction (HRI) and Robot-Robot Interaction (RRI). Here, we introduce 3-dimensional acoustic ranging (3DAR) using the meta-structured single microphone rotation, a compact system with a three-dimensional meta-structure with a phase-cancellation mechanism for enhanced beamforming across audible and inaudible frequencies. Inspired by dolphin communication, the 3DAR employs frequency modulation and separation of sound channels for seamless HRI and RRI. The system achieved over 90 % accuracy in multiple source localization for HRI and 99 % for RRI, even in challenging noise conditions, along with 94 % accuracy in tracking multiple sound sources. Furthermore, real-world tests in a factory demonstrated 95.6 % accuracy in multi-HRI localization and up to 93.8 % accuracy when human speech direction originated from an angle of 120° relative to the system. Real-world application of a collaborative rescue robot confirmed effectiveness of the 3DAR in various applications, highlighting its potential for robust, sound-based collaboration among humans and robots.

{"title":"Human-robot and robot-robot sound interaction using a 3-Dimensional Acoustic Ranging (3DAR) in audible and inaudible frequency","authors":"Semin Ahn , Jae-Hoon Kim , Jun Heo , Sung-Hoon Ahn","doi":"10.1016/j.rcim.2025.102970","DOIUrl":"10.1016/j.rcim.2025.102970","url":null,"abstract":"<div><div>Highly reliable sound-based interaction in noisy and dynamic environments is a known challenge with simultaneous Human-Robot Interaction (HRI) and Robot-Robot Interaction (RRI). Here, we introduce 3-dimensional acoustic ranging (3DAR) using the meta-structured single microphone rotation, a compact system with a three-dimensional meta-structure with a phase-cancellation mechanism for enhanced beamforming across audible and inaudible frequencies. Inspired by dolphin communication, the 3DAR employs frequency modulation and separation of sound channels for seamless HRI and RRI. The system achieved over 90 % accuracy in multiple source localization for HRI and 99 % for RRI, even in challenging noise conditions, along with 94 % accuracy in tracking multiple sound sources. Furthermore, real-world tests in a factory demonstrated 95.6 % accuracy in multi-HRI localization and up to 93.8 % accuracy when human speech direction originated from an angle of 120° relative to the system. Real-world application of a collaborative rescue robot confirmed effectiveness of the 3DAR in various applications, highlighting its potential for robust, sound-based collaboration among humans and robots.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102970"},"PeriodicalIF":9.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072448","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}

引用次数: 0

Energy consumption modeling based on operation mechanisms of industrial robots

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-25 DOI: 10.1016/j.rcim.2025.102971

Zuoxue Wang , Xiaobin Li , Pei Jiang , Xi Vincent Wang , Haitao Yuan

Industrial robots are widely used in manufacturing industries due to their high efficiency, flexibility, and ability to respond to diverse needs. However, the large-scale deployment of industrial robots has resulted in a significant increase in energy consumption. Therefore, it is crucial to develop an accurate modeling method for predicting the energy consumption of robotic systems, in order to optimize energy usage and achieve green and sustainable development of the manufacturing industry. Based on the analysis of temporal causal relationships between motion variables and the power of industrial robots, as well as spatial dependence between trajectory points, this study proposes a spatial-based torque prediction network and a temporal–spatial-based energy consumption prediction network by combining layer normalization with bidirectional long short-term memory neural network. This model achieves high-precision predictions of robot motion under variable motion modes, time scaling functions, and load conditions. Experimental results with KUKA KR210 and KR60 robots demonstrate that the model achieves the prediction accuracy of 99.01% for joint torque, 96.61% for total power, and 98.72% for total energy consumption under varying conditions.

{"title":"Energy consumption modeling based on operation mechanisms of industrial robots","authors":"Zuoxue Wang , Xiaobin Li , Pei Jiang , Xi Vincent Wang , Haitao Yuan","doi":"10.1016/j.rcim.2025.102971","DOIUrl":"10.1016/j.rcim.2025.102971","url":null,"abstract":"<div><div>Industrial robots are widely used in manufacturing industries due to their high efficiency, flexibility, and ability to respond to diverse needs. However, the large-scale deployment of industrial robots has resulted in a significant increase in energy consumption. Therefore, it is crucial to develop an accurate modeling method for predicting the energy consumption of robotic systems, in order to optimize energy usage and achieve green and sustainable development of the manufacturing industry. Based on the analysis of temporal causal relationships between motion variables and the power of industrial robots, as well as spatial dependence between trajectory points, this study proposes a spatial-based torque prediction network and a temporal–spatial-based energy consumption prediction network by combining layer normalization with bidirectional long short-term memory neural network. This model achieves high-precision predictions of robot motion under variable motion modes, time scaling functions, and load conditions. Experimental results with KUKA KR210 and KR60 robots demonstrate that the model achieves the prediction accuracy of 99.01% for joint torque, 96.61% for total power, and 98.72% for total energy consumption under varying conditions.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102971"},"PeriodicalIF":9.1,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031438","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}

引用次数: 0

Online positioning of thin-walled blade with small curvature for robotic flexible polishing based on optimal local feature matching

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-25 DOI: 10.1016/j.rcim.2025.102967

Ruipeng Pan , Zesheng Wang , Hui Wang , Dongbo Wu

The uncertainty of the blade's position and attitude in robotic flexible polishing leads to poor accuracy and stability of force-position coupling, resulting in potential issues like over-polishing or under-polishing, significantly impacting the consistency of final polishing quality. The study proposes an online positioning method of thin-walled blade with small curvature for robotic flexible polishing. The novelty of proposed method lies in that it is based on optimal local geometric feature matching between the actual workpiece and CAD model to obtain the actual position and attitude of thin-walled blade with small curvature and limited measurement area, with a positioning accuracy of 0.3164 mm, thus achieving the adaptive optimization of robotic movement trajectory. Firstly, a mathematical model for the adaptive optimization of robotic movement trajectory based on the actual posture of workpiece is established. The theoretical principles of spatial point cloud mapping based on the forward kinematics model of serial-robot, spatial point cloud registration based on dense and sparse point clouds, workpiece posture analysis based on reverse derivation of point cloud transformation are secondly studied to achieve an accurate positioning of workpiece in the robotic workspace. The error sources of proposed positioning method are analyzed and a quantitative mathematical model is established to characterize the positioning accuracy of workpiece. The feasibility and reliability of proposed positioning method are finally validated through a typical experiment. The results demonstrate that the proposed method can achieve an accurate positioning of thin-walled blade with small curvature and limited measurement area and thereby ensuring the consistency of final polishing quality.

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引用次数: 0

A generalized generation and evaluation method for cutting process parameter knowledge based on CTGAN

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing

Pub Date : 2025-01-23 DOI: 10.1016/j.rcim.2025.102963

Dan Li , Tianliang Hu , Lili Dong , Songhua Ma

The machining process knowledge base is a crucial tool in the decision-making process for cutting process parameters, as the diversity and accuracy of its stored process knowledge directly affect the decision effectiveness. To address the complex demands in actual production, it is necessary to adopt effective expansion methods to enrich the process knowledge base content and improve its generalizability. However, current expansion methods face limitations such as insufficient process knowledge coverage and the lack of an effective evaluation mechanism. In response to these issues, this paper proposes a generalized generation and evaluation method for cutting process parameter knowledge based on CTGAN. Firstly, a cutting process data acquisition platform is developed to serve as the basic data source. Then, Conditional Tabular Generative Adversarial Network (CTGAN) is used to construct a generalized generation model to learn the joint distribution law of real process parameter data and enable the intelligent generation of cutting process parameter cases. Finally, the accuracy and applicability of the generated cutting process parameter cases are evaluated through statistical indicator analysis and machine learning performance analysis. The proposed framework is validated using the external cylindrical turning process of a sleeve part as a test case. Results indicate that the generated process parameter data samples not only cover a broader range of machining scenarios but also maintain high quality, which can effectively support the autonomous expansion of machining process knowledge base, and enhance its generalization capability.

{"title":"A generalized generation and evaluation method for cutting process parameter knowledge based on CTGAN","authors":"Dan Li , Tianliang Hu , Lili Dong , Songhua Ma","doi":"10.1016/j.rcim.2025.102963","DOIUrl":"10.1016/j.rcim.2025.102963","url":null,"abstract":"<div><div>The machining process knowledge base is a crucial tool in the decision-making process for cutting process parameters, as the diversity and accuracy of its stored process knowledge directly affect the decision effectiveness. To address the complex demands in actual production, it is necessary to adopt effective expansion methods to enrich the process knowledge base content and improve its generalizability. However, current expansion methods face limitations such as insufficient process knowledge coverage and the lack of an effective evaluation mechanism. In response to these issues, this paper proposes a generalized generation and evaluation method for cutting process parameter knowledge based on CTGAN. Firstly, a cutting process data acquisition platform is developed to serve as the basic data source. Then, Conditional Tabular Generative Adversarial Network (CTGAN) is used to construct a generalized generation model to learn the joint distribution law of real process parameter data and enable the intelligent generation of cutting process parameter cases. Finally, the accuracy and applicability of the generated cutting process parameter cases are evaluated through statistical indicator analysis and machine learning performance analysis. The proposed framework is validated using the external cylindrical turning process of a sleeve part as a test case. Results indicate that the generated process parameter data samples not only cover a broader range of machining scenarios but also maintain high quality, which can effectively support the autonomous expansion of machining process knowledge base, and enhance its generalization capability.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"94 ","pages":"Article 102963"},"PeriodicalIF":9.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027342","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}

引用次数: 0