{"title":"人机协作制造中以人为中心的质量监测诊断工具","authors":"E. Verna, Stefano Puttero, G. Genta, M. Galetto","doi":"10.1115/1.4063284","DOIUrl":null,"url":null,"abstract":"\n The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the “Human-Robot Collaboration Quality and Well-Being Assessment Tool” (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human-robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human-robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human-robot collaborative manufacturing.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Diagnostic Tool for Human-Centric Quality Monitoring in Human-Robot Collaboration Manufacturing\",\"authors\":\"E. Verna, Stefano Puttero, G. Genta, M. Galetto\",\"doi\":\"10.1115/1.4063284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the “Human-Robot Collaboration Quality and Well-Being Assessment Tool” (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human-robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human-robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human-robot collaborative manufacturing.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4063284\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4063284","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
A Novel Diagnostic Tool for Human-Centric Quality Monitoring in Human-Robot Collaboration Manufacturing
The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the “Human-Robot Collaboration Quality and Well-Being Assessment Tool” (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human-robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human-robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human-robot collaborative manufacturing.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining