https://escholarship.org/content/qt4h56k1ch/qt4h56k1ch.pdf

Advanced monitoring of machining operations

Advances in Modeling and Simulation of Grinding Processes

On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research

This paper presents a summary of the monitoring methods, signal analysis and diagnostic techniques for tool wear and failure monitoring in drilling that have been tested and reported in the literature. The paper covers only indirect monitoring methods such as force, vibration and current measurements, i.e. direct monitoring methods based on dimensional measurement etc. are not included. Signal analysis techniques cover all the methods that have been used with indirect measurements including e.g. statistical parameters and Fast Fourier and Wavelet Transform. Only a limited number of automatic diagnostic tools have been developed for diagnosis of the condition of the tool in drilling. All of these rather diverse approaches that have been available are covered in this study. In the reported material there are both success stories and also those that have not been so successful. Only in a few of the papers have attempts been made to compare the chosen approach with other methods. Many of the papers only present one approach and unfortunately quite often the test material of the study is limited especially in what comes to the cutting process parameter variation, i.e. variation of cutting speed, feed rate, drill diameter and material and also workpiece material.

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A summary of methods applied to tool condition monitoring in drilling

Excessive wear on cutting tools give rise to distortions in dimension of manufactured components, sometimes increasing scrapped levels thereby incurring additional costs. Methods for detecting and monitoring the wear on a cutting tool is therefore crucial in most metal cutting processes and several research efforts have striven to develop on-line tool condition monitoring systems. This paper describes an experimental and analytical method for one such technique involving the use of three mutually perpendicular components of the cutting forces (static and dynamic) and vibration signature measurements. The ensuing analyses in time and frequency domains showed some components of the measured signals to correlate well to the accrued tool wear.

E. Govekar

Papers

Analysis of acoustic emission signals and monitoring of machining processes

Using coarse-grained entropy rate to detect chatter in cutting

A New Method for Chatter Detection in Grinding

Time Series Analysis in Metal Cutting: Chatter Versus Chatter-Free Cutting

Self-Organizing Neural Network Application to Drill Wear Classification