Texture Descriptors for Automatic Estimation of Workpiece Quality in Milling

Abstract

Milling workpiece present a regular pattern when they are correctly machined. However, if some problems occur, the pattern is not so homogeneous and, consequently, its quality is reduced. This paper proposes a method based on the use of texture descriptors in order to detect workpiece wear in milling automatically. Images are captured by using a boroscope connected to a camera and the whole inner surface of the workpiece is analysed. Then texture features are computed from the coocurrence for each image. Next, feature vectors are classified by 4 different approaches, Decision Trees, K Neighbors, Naïve Bayes and a Multilayer Perceptron. Linear discriminant analysis reduces the number of features from 6 to 2 without loosing accuracy. A hit rate of 91.8% is achieved with Decision Trees what fulfils the industrial requirements.