Tool Wear Condition Monitoring Using Emitted Sound Signals By Simple Machine Learning Technique-Reference-Cited by-同舟云学术

Tool Wear Condition Monitoring Using Emitted Sound Signals By Simple Machine Learning Technique

Published:2022-06-01 Issue: Volume:2 Page:168-172
ISSN:2732-9984
Container-title:DESIGN, CONSTRUCTION, MAINTENANCE
language:en
Short-container-title:

Author:

Perumal C. Logesh ,¹,Bhadrinathan S. B. ,¹,Samraj Andrews²

Affiliation:

1. Department of Artificial Intelligence and Data Science, Mahendra Engineering College, Namakkal,Tamil Nadu, INDIA

2. Department of Computer Science and Engineering, Mahendra Engineering College Namakkal,Tamil Nadu, INDIA

Abstract

As a continuous enhancement to the tool wear monitoring using non-disturbing method of sound wave analysis, a simple machine learning technique enhances the prediction to better levels and reduces the procedures. A simple linear regression Algorithm was used to train and predict the trends of various degrees of tool wear to distinguish them from each other. The results based on this simple linear regression were successful in showing the difference of sound patterns and are reported.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference13 articles.

1. E. Dimla, and S. Dimla , “Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods”, International Journal of Machine Tools & Manufacture, vol. 40, pp. 1073- 1098, 2000.

2. R. G. Silva, R. L. Reuben, K. J. Baker, and S. J. Wilcox, “Tool wear monitoring of turning operations by neural network and expert systemClassification of a feature set generated from multiple sensors,” Mechanical Systems and Signal Processing, vol. 12, pp. 319–332, November 1998.

3. F. J. Alonso and D. R. Salgado, “Application of singular spectrum analysis to tool wear detection using sound signals,” Proc. IME, Part B: Journal of Engineering Manufacture, vol. 219, no. 9, pp. 703-710, 2005

4. Sadettin, O., S. Orhan, A. Osman Er, N. Camus-cu, and E. Aslan, “Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness,” NDT&E International, vol. 40, pp. 121–126, 2007.

5. M. Chyuan Lu, E. Kannatey-Asibu, “Analysis of Sound Signal Generation Due to Flank Wear in Turning,” Journal of Manufacturing Science and Engineering, vol. 124, no. 4, doi:10.1115/1.1511177, pp. 799-808 , November 2002.