制造业中数字孪生的工业见解:应用前景、当前实践和未来需求

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data and Cognitive Computing Pub Date : 2023-06-29 DOI:10.3390/bdcc7030126
R. D. D’Amico, S. Addepalli, J. Erkoyuncu
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引用次数: 0

摘要

数字孪生(DT)研究领域正在经历快速扩张;然而,对该领域工业实践的研究仍然知之甚少。本文旨在通过分享制造业的反馈和未来需求来解决这一知识差距。本研究采用的方法包括对一项调查的审查,该调查收到了99份回复,并采访了来自英国10个著名组织的14名专家,其中大多数组织都参与了英国的国防工业。调查和采访探讨了DT设计、投资回报、驱动因素、抑制剂以及制造业DT发展的未来方向等主题。这项研究的结果表明,DT应该具有适应性、可扩展性、互操作性以及在其整个生命周期中支持资产的能力。平均而言,已完成的DT项目在不到两年的时间内达到盈亏平衡点。DT开发背后的主要动力是自主性、客户满意度、安全性、意识、优化和可持续性。同时,主要障碍包括缺乏专业知识、资金和互操作性。这项研究得出结论,双胞胎的联合和产业思维的范式转变是DT未来发展的重要组成部分。
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Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs
The digital twin (DT) research field is experiencing rapid expansion; yet, the research on industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are involved in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. This study’s findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the breakeven point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. This study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
期刊最新文献
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