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Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
https://orcid.org/http://orcid.org/0000-0003-2345-7209
https://orcid.org/http://orcid.org/0000-0002-3730-6316
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.
Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
https://orcid.org/http://orcid.org/0000-0003-2345-7209
https://orcid.org/http://orcid.org/0000-0002-3730-6316
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.
Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
https://orcid.org/http://orcid.org/0000-0003-2345-7209
https://orcid.org/http://orcid.org/0000-0002-3730-6316
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.
Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
Int J Interact Des Manuf
Muthuswamy, Padmakumar (author) / K, Shunmugesh (author)
2023-06-01
21 pages
Article (Journal)
Electronic Resource
English
Condition monitoring , Smart factory , Intelligent cutting tools , Digital twins , Sensors , Automation , Industry 4.0 , Artificial Intelligence Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
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