Digital Twin-driven multi-scale characterization of machining quality: current status, challenges, and future perspectives

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

Robotics and Computer-integrated Manufacturing Pub Date : 2024-11-21 DOI:10.1016/j.rcim.2024.102902

Xiangfu Fu , Shuo Li , Hongze Song , Yuqian Lu

引用次数: 0

Abstract

The evolution of manufacturing towards intelligent and digital processes requires innovation in machining quality control. While current research primarily addresses single-scale quality control, it overlooks comprehensive multi-scale product quality characterization. Digital twin technology emerges as a potential solution. This review examines digital twin applications in machining quality control, highlighting limitations of traditional methods and exploring multi-scale quality characterization at macro, meso, and micro levels. It evaluates multi-scale quality changes during processing and summarizes comprehensive characterization methods across scales. The study concludes by discussing future prospects for digital twin technology in multi-scale machining quality control and optimization.

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数字孪生驱动的加工质量多尺度表征：现状、挑战和未来展望

制造业向智能化和数字化流程发展，需要在加工质量控制方面进行创新。目前的研究主要针对单一尺度的质量控制，而忽略了全面的多尺度产品质量表征。数字孪生技术是一种潜在的解决方案。本综述探讨了数字孪生技术在机械加工质量控制中的应用，强调了传统方法的局限性，并探索了宏观、中观和微观层面的多尺度质量表征。它评估了加工过程中的多尺度质量变化，并总结了跨尺度的综合表征方法。研究最后讨论了数字孪生技术在多尺度加工质量控制和优化方面的未来前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.