Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

The monitoring of tool condition in machining processes has significant importance to control the quality of machined parts and to reduce equipment downtime. This study investigates the application of a special variant of artificial neural networks (ANNs), in particular, wavelet neural network (WNN) for tool wear monitoring in CNC end milling process of high-speed steel. A mixed level design of experiments with machining parameters of cutting speed, feed rate, cutting depth, and machining time is developed, from which 126 experiments are conducted. For each experiment, tool wear and surface roughness of machined workpiece are measured. The tool wear images are processed, and the descriptor of wear zone is extracted. The WNN is then applied to predict the flank wear of the cutting tool and compared with commonly used types of ANNs and the statistical model. Different input combinations with the inclusion of wear zone descriptor and surface roughness of machined parts are used to evaluate the performance of all models. Results show that the WNN with the input parameters of cutting speed, feed rate, depth of cut, machining time, and descriptor of wear zone predicts the degree of tool wear most accurately.