Tool condition monitoring in milling based on cutting forces by a neural network

Automated machining systems require reliable online monitoring processes. The application of a multilayered neural network for tool condition monitoring in face milling is introduced and evaluated against cutting force data. The work uses the back-propagation algorithm for training neural network of 5 x 10 x 2 architecture. An artificial neural network was used for feature selection in order to estimate flank wear (Vb) and surface roughness (Ra) during the milling operation. The relationship of cutting parameters with Vb and Ra was established. The sensor selection using statistical methods based on the experimental data helps in determining the average effect of each factor on the performance of the neural network model. This model, including cutting speed, feed rate, depth of cut and two cutting force components (feed force and vertical Z-axis force), presents a close estimation of Vb and Ra. Therefore, the neural network with parallel computation ability provides a possibility for setting up intelligent sensor systems.