Cutting tool wear progression index via signal element variance

N. A. Kasim, Mohd. Zaki Nuawi, Jaharah A Ghani, M. Rizal, M. A.F. Ahmad, Che Hassan Che Haron

Research output: Contribution to journalArticle

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 R Z 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 R 2 = 0.8702 and the average value of R 2 = 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.

Original languageEnglish
Pages (from-to)4596-4612
Number of pages17
JournalJournal of Mechanical Engineering and Sciences
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

Cutting tools
Wear of materials
Disintegration
Feature extraction
Machining
Monitoring

Keywords

  • Flank wear
  • Force signal
  • I-kaz
  • Statistical analysis
  • Tool condition monitoring

ASJC Scopus subject areas

  • Computational Mechanics
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

Cutting tool wear progression index via signal element variance. / Kasim, N. A.; Nuawi, Mohd. Zaki; A Ghani, Jaharah; Rizal, M.; Ahmad, M. A.F.; Che Haron, Che Hassan.

In: Journal of Mechanical Engineering and Sciences, Vol. 13, No. 1, 01.03.2019, p. 4596-4612.

Research output: Contribution to journalArticle

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