Statistical process control of acoustic emission for cutting tool monitoring

Abstract

The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis. In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes. The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.

Integrated measurement and machining technology of complex surfaces

“…测刀具的磨损情况, 也可以通过检测切削力、振动、 噪声、扭矩 [18] 或电流 [19] [21,22] . 在目前已开发的刀具磨损检测 仪中, 主要是基于切削力、声发射或者同时检测振动 信号与声发射信号 [23]…”

A Comparative Study of Feature Selection for Hidden Markov Model-Based Micro-Milling Tool Wear Monitoring

“…Houshmand and Kannatey-Asibu (1989) reported a large improvement of classification rate after transformed the entire feature sets with PCA and then chose only the top 6 principal components of the 46-dimentional features for classification in TCM. Li and Elbestawi (1996) also applied the PCA for the feature dimension reduction for tool wear clustering in turning.…”

Statistical Determination of a Fretting-Induced Failure of an Electro-Deposited Coating

“…For monitoring the state of a cutting tool, acoustic emission signals were measured and analyzed with SPC tools [11]. SPC tools were used for predicting the formation of voids and defects in friction stir welding [12].…”