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Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling

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Abstract

Due to the demands of Computer-Integrated Manufacturing (CIM), the Tool Condition Monitoring (TCM) system, as a major component of CIM, is essential to improve the production quality, optimize the labor and maintenance costs, and minimize the manufacturing loses with the increase in productivity. To look for a reliable, efficient, and cost-effective solution, various monitoring systems employing different types of sensing techniques have been developed to detect the tool conditions as well as to monitor the abnormal cutting states. This paper explores the use of audible sound signals as sensing approach to detect the cutting tool wear and failure during end milling operation by using the Support Vector Machine (SVM) learning model as a decision-making algorithm. In this study, sound signals collected during the machining process are analyzed through frequency domain to extract signal features that correlate actual cutting phenomenon. The SVM method seeks to provide a linguistic model for tool wear estimation from the knowledge embedded in this machine learning approach. The performance evaluation results of the proposed algorithm have shown accurate predictions in detecting tool wear under various cutting conditions with rapid response rate, which provides the good solution for in-process TCM. In addition, the proposed monitoring system trained with sufficient signals collected from different positions has been proved to be position independent to monitor the tool wear conditions.

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Acknowledgments

The authors gratefully acknowledge support from the Gleason Doctoral Fellowship program for this research, and would like to thank Ray Ptucha for his insightful suggestion into this work.

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Correspondence to Rui Liu.

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Kothuru, A., Nooka, S.P. & Liu, R. Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling. Int J Adv Manuf Technol 95, 3797–3808 (2018). https://doi.org/10.1007/s00170-017-1460-1

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Keywords

  • Tool condition monitoring
  • Tool wear
  • Audible sound
  • Machine learning
  • Support vector machine