Neural networks in process tool wear prediction system has been proposed and evaluated in this study. A total of 100
experimental data have been received for training through a back-propagation neural networks model. The input
variables for the proposed neural networks system were feed rate, cutting speed from the cutting parameters, and the
force in the x,y-direction collected online using a dynamometer. After the proposed neural networks system had been
established, two experimental testing cuts were conducted to evaluate the performance of the system. From the test
results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm.