Pub Date : 2024-07-05DOI: 10.1080/0951192x.2024.2372273
Jiongqi Li, Xuhan Chen, Mingxin Huang, Kangjie Ye, Zhiwei Lin, Jianzhong Fu
Carbon Fiber Reinforced Polymer (CFRP) is commonly employed in advanced industries, acclaimed for their outstanding mechanical properties. The inherent anisotropy of CFRP necessitates careful consi...
Pub Date : 2024-07-05DOI: 10.1080/0951192x.2024.2372252
Van-Hai Nguyen, Tien-Thinh Le
This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse ...
{"title":"Predicting surface roughness in machining aluminum alloys taking into account material properties","authors":"Van-Hai Nguyen, Tien-Thinh Le","doi":"10.1080/0951192x.2024.2372252","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2372252","url":null,"abstract":"This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse ...","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-03DOI: 10.1080/0951192x.2024.2372262
Chiara Cimino, Federico Terraneo, Gianni Ferretti, Alberto Leva
The optimised design, operation and management of complex, large-size Cyber Physical Systems (CPSs) – like modern manufacturing and logistic assets – calls for Digital Twins (DTs) in which dynamic ...
{"title":"Scalable and effіciant digital twins for model-based design of cyber-physical systems","authors":"Chiara Cimino, Federico Terraneo, Gianni Ferretti, Alberto Leva","doi":"10.1080/0951192x.2024.2372262","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2372262","url":null,"abstract":"The optimised design, operation and management of complex, large-size Cyber Physical Systems (CPSs) – like modern manufacturing and logistic assets – calls for Digital Twins (DTs) in which dynamic ...","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-06-30DOI: 10.1080/0951192x.2024.2372272
Francesco Pilati, Andrea Sbaragli, Tamás Ruppert, János Abonyi
Modern internal logistic systems face several challenges, from supply chain disruption to mass customization of marketed products. In such a highly dynamic scenario, Internet of Things technologies...
{"title":"Goal-oriented clustering algorithm to monitor the efficiency of logistic processes through real-time locating systems","authors":"Francesco Pilati, Andrea Sbaragli, Tamás Ruppert, János Abonyi","doi":"10.1080/0951192x.2024.2372272","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2372272","url":null,"abstract":"Modern internal logistic systems face several challenges, from supply chain disruption to mass customization of marketed products. In such a highly dynamic scenario, Internet of Things technologies...","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-06-29DOI: 10.1080/0951192x.2024.2372281
Nailiang Li, Dan Zhang, Yicong Li, Qi Zhang
Researchers extensively use deep learning for assembly task action recognition due to its superior feature representation. However, current methods fail to integrate assembly actions with basic hum...
{"title":"Repetitive assembly basic action detection and standard work measurement based on deep learning","authors":"Nailiang Li, Dan Zhang, Yicong Li, Qi Zhang","doi":"10.1080/0951192x.2024.2372281","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2372281","url":null,"abstract":"Researchers extensively use deep learning for assembly task action recognition due to its superior feature representation. However, current methods fail to integrate assembly actions with basic hum...","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-06-10DOI: 10.1080/0951192x.2024.2358058
Wenmin Chu, Jie Huang, Hongsheng Yan, Shuanggao Li, Xiang Huang, Gen Li
{"title":"Optimization of pose samples for calibration of large component docking mechanisms","authors":"Wenmin Chu, Jie Huang, Hongsheng Yan, Shuanggao Li, Xiang Huang, Gen Li","doi":"10.1080/0951192x.2024.2358058","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2358058","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a two-phase data science framework for the friction force and parameter estimation of the hysteresis effect segment in the servo-control systems of precision machines. The first...
{"title":"Two-phase data science framework for compensation of the friction force in CNC machines","authors":"Yu-Hsiang Cheng, Chia-Yen Lee, Ching-Hsiung Tsai, Jia-Ming Wu","doi":"10.1080/0951192x.2024.2358033","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2358033","url":null,"abstract":"This study proposes a two-phase data science framework for the friction force and parameter estimation of the hysteresis effect segment in the servo-control systems of precision machines. The first...","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-05-21DOI: 10.1080/0951192X.2024.2358042
M. Babcinschi, F. Cruz, N. Duarte, S. Santos, S. Alves, P. Neto
Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.
{"title":"Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding","authors":"M. Babcinschi, F. Cruz, N. Duarte, S. Santos, S. Alves, P. Neto","doi":"10.1080/0951192X.2024.2358042","DOIUrl":"https://doi.org/10.1080/0951192X.2024.2358042","url":null,"abstract":"Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-05-13DOI: 10.1080/0951192x.2024.2350539
Zhiqiang Sun, Hangbin Zheng, Chaofan Lv, Jingsong Bao
{"title":"A fast scene geometric modeling approach for digital twins combining neural rendering and model retrieval","authors":"Zhiqiang Sun, Hangbin Zheng, Chaofan Lv, Jingsong Bao","doi":"10.1080/0951192x.2024.2350539","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2350539","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-05-13DOI: 10.1080/0951192x.2024.2351529
Le Toan Duong, A. Subias, Louise Travé-Massuyès, Christophe Merle
{"title":"Big data analytics for quality variation over work shifts in manufacturing systems","authors":"Le Toan Duong, A. Subias, Louise Travé-Massuyès, Christophe Merle","doi":"10.1080/0951192x.2024.2351529","DOIUrl":"https://doi.org/10.1080/0951192x.2024.2351529","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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