A Review on Automatic Fault Detection and Diagnosis in a Single Point Cutting Tool Using Wavelet Analysis

Abstract

Tool wears monitoring continuous to be a major area of concern in machining. In order to produce quality products at reasonable cost tool condition monitoring becomes an important study for all the researchers. Another drawback of most of these works is that constant cutting parameters are used for entire tool life. The Monitoring system can detect tool breakage and tool wear conditions using very simple triaxial sensors, surface finish of machined parts and dimensional accuracy dependent on tool condition. This paper reviews in eight categories in TCM: singularity analysis for tool state estimation, Artificial Neural Network, signal denoising, time- frequency analysis, feature extraction, density estimation of tool wear classification, k-star algorithm, histogram feature of vibration signal. This paper provides a comprehensive survey of the current research on wavelet analysis to TCM and also some new novel techniques for future studies in this area.