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 tool wear has been presented in this paper. This method provides visualization of the tool wear geometry using a pair of stereo images. In addition, prediction of tool wear using artificial neural network is presented. A multilayered perceptron with back-propagation algorithm has been used for tool wear estimation, which could be trained using much less data than that is required in a normal mathematical simulation. Speed, feed, depth of cut and cutting time were used as input parameters and flank wear width and crater wear depth were output parameters. Training and testing of the network were carried out and the results are presented and analyzed in this work.