Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
参考中译:用于数字孪生和工业4.0应用的基于人工智能的刀具状态监控


          

刊名：IJIDeM: International Journal on Interactive Design and Manufacturing
作者：Muthuswamy, Padmakumar（Kennamet India Ltd）
Shunmugesh, K.（Viswajyothi Coll Engn & Technol）
刊号：710F0045
ISSN：1955-2513
出版年：2023
年卷期：2023, vol.17, no.3
页码：1067-1087
总页数：21
分类号：TH12
关键词：Condition monitoring；Smart factory；Intelligent cutting tools；Digital twins；Sensors；Automation；Industry 4；0；Artificial Intelligence；SUPPORT VECTOR MACHINE；HIDDEN MARKOV-MODELS；ACOUSTIC-EMISSION；NEURAL-NETWORK；FLANK WEAR；TURNING OPERATIONS；SURFACE-ROUGHNESS；STATISTICAL-ANALYSIS；VIBRATION；PREDICTION
参考中译：状态监测；智能工厂；智能刀具；数字孪生；传感器；自动化；工业4；0；人工智能；支持向量机；隐马尔可夫模型；声发射；神经网络；后刀面磨损；车削操作；表面粗糙度；统计分析；振动；预测
语种：eng
文摘：The high demand for machining process automation has placed real-time tool condition monitoring as one of the top priorities of academic and industrial scholars in the past decade. But the presence of numerous known and unknown machining variables and challenging operating conditions such as high temperature and pressure makes it a daunting task. However, recent advancements in sensor and digital technologies have enabled in-process condition monitoring and real-time process optimization a highly accurate, robust, and effective process. Hence, the objective of the article is to provide a summary of the factors influencing the performance of cutting tools, critical machining variables to be monitored, techniques applied to monitor tool conditions, and artificial intelligence algorithms used to predict tool performance by analyzing and reviewing the literature. The future direction of intelligent cutting tools and how they would help in building the foundation for advanced smart factory ecosystems such as digital twins and Industry 4.0 are also discussed.
参考中译：在过去的十年里，对加工过程自动化的高要求使得刀具状态的实时监测成为学术界和工业界学者的首要任务之一。但是，大量已知和未知的加工变量以及具有挑战性的操作条件(如高温和压力)的存在使其成为一项艰巨的任务。然而，传感器和数字技术的最新进步使过程中的状态监控和实时过程优化成为一个高度准确、稳健和有效的过程。因此，本文的目的是通过分析和回顾文献，总结影响刀具性能的因素、需要监测的关键加工变量、应用于监测刀具状况的技术以及用于预测刀具性能的人工智能算法。还讨论了智能刀具的未来发展方向，以及它们将如何帮助为数字孪生和工业4.0等先进的智能工厂生态系统奠定基础。



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