Tool wear evaluation by stereo vision and prediction by artificial neural network

The main objective of this work is to develop a method to study the contour of crater wear and measure it in three dimensions. A new technique for the measurement and visualization of toot wear has been presented in this pape r, This method provides visualization of the tool wear geometry using a pai r of stereo images. In addition, prediction of tool wear using artificial n eural network is presented. A multilayered perceptron with back-propagation algorithm has been used for tool wear estimation, which could be trained u sing much less data than that is required in a normal mathematical simulati on. Speed, feed, depth of cut and cutting time were used as input parameter s and flank wear width and crater wear depth were output parameters. Traini ng and testing of the network were carried out and the results are presente d and analyzed in this work. (C) 2001 Elsevier Science B.V. All rights rese rved.