Exploring the Benefits of Integrating Machine Learning and Tool Condition Monitoring for Manufacturing Applications

DOI

10.2991/978-94-6463-482-2_4How to use a DOI?

Keywords

Machine Learning; Tool Condition Monitoring; Manufacturing Applications; Process Control; Downtime; Integration

Abstract

This review article explores the potential benefits of integrating machine learning and tool condition monitoring for manufacturing applications. It first reviews the current state of machine learning and tool condition monitoring and their respective applications in manufacturing. It then discusses the potential benefits of combining the two, including improved process control and reduced downtime, as well as the challenges associated with integrating the two technologies. Finally, the review article provides an overview of existing approaches to integrating machine learning and tool condition monitoring, examining the advantages and drawbacks of each. The article concludes with a summary of the key findings and implications for the future of integrating machine learning and tool condition monitoring in manufacturing. The article provides a comprehensive overview of the benefits and challenges associated with integrating machine learning and tool condition monitoring, as well as a review of existing approaches and future implications.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Gokul
AU  - S. A. Vigneshvar
AU  - R. Ashwathi Krishna
AU  - K. Kabilan
AU  - P. Vijaya
PY  - 2024
DA  - 2024/08/23
TI  - Exploring the Benefits of Integrating Machine Learning and Tool Condition Monitoring for Manufacturing Applications
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 38
EP  - 53
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_4
DO  - 10.2991/978-94-6463-482-2_4
ID  - Gokul2024
ER  -