Cutting tool wear progression index via signal element variance

  • N. A. Kasim Department of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA Phone: +60192613700; Fax: +60389252546
  • M. Z. Nuawi Department of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA Phone: +60192613700; Fax: +60389252546
  • J. A. Ghani Department of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA Phone: +60192613700; Fax: +60389252546
  • M. Rizal Department of Mechanical Engineering, Faculty of Engineering, Syiah Kuala University (UNSYIAH), 23111 Darussalam, Banda Aceh, INDONESIA
  • M. A. F. Ahmad Department of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA Phone: +60192613700; Fax: +60389252546
  • C. H. Che Haron Department of Mechanical & Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MALAYSIA Phone: +60192613700; Fax: +60389252546
Keywords: Statistical analysis, tool condition monitoring, force signal, flank wear, I-kaz

Abstract

This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition.

Published
2019-03-29
How to Cite
Kasim, N. A., Nuawi, M. Z., A. Ghani, J., Rizal, M., Ahmad, M. A. F., & Che Haron, C. H. (2019). Cutting tool wear progression index via signal element variance. Journal of Mechanical Engineering and Sciences, 13(1), 4596-4612. https://doi.org/10.15282/jmes.13.1.2019.17.0387