{"title":"一种新的基于xr的工业4.0实时机器交互系统:学习型工厂的可用性评估","authors":"Kaveh Amouzgar , Justus Willebrand","doi":"10.1016/j.jmsy.2025.05.019","DOIUrl":null,"url":null,"abstract":"<div><div>Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced, data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine interaction and operational efficiency in Industry 4.0 framework. While previous research has demonstrated the effectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limited attention has been given to developing and evaluating XR systems for real-time machine data visualization. Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations against other XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. This study addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimental design, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from 22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance. The findings show that the XR system significantly improves alarm response times, increases frequency of preventive refills, and enhances usability compared to traditional HMIs. However, challenges related to ergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing by showcasing the potential of XR to improve human–machine interfaces and foster better interaction between machines and operators.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 254-283"},"PeriodicalIF":14.2000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory\",\"authors\":\"Kaveh Amouzgar , Justus Willebrand\",\"doi\":\"10.1016/j.jmsy.2025.05.019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced, data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine interaction and operational efficiency in Industry 4.0 framework. While previous research has demonstrated the effectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limited attention has been given to developing and evaluating XR systems for real-time machine data visualization. Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations against other XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. This study addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimental design, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from 22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance. The findings show that the XR system significantly improves alarm response times, increases frequency of preventive refills, and enhances usability compared to traditional HMIs. However, challenges related to ergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing by showcasing the potential of XR to improve human–machine interfaces and foster better interaction between machines and operators.</div></div>\",\"PeriodicalId\":16227,\"journal\":{\"name\":\"Journal of Manufacturing Systems\",\"volume\":\"82 \",\"pages\":\"Pages 254-283\"},\"PeriodicalIF\":14.2000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0278612525001451\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525001451","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory
Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced, data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine interaction and operational efficiency in Industry 4.0 framework. While previous research has demonstrated the effectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limited attention has been given to developing and evaluating XR systems for real-time machine data visualization. Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations against other XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. This study addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimental design, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from 22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance. The findings show that the XR system significantly improves alarm response times, increases frequency of preventive refills, and enhances usability compared to traditional HMIs. However, challenges related to ergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing by showcasing the potential of XR to improve human–machine interfaces and foster better interaction between machines and operators.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.