Comparison of Neural Networks and Regression Analysis to Predict In-process Straightness in CNC Turning

Abstract

The objective of this research is to predict the in-process straightness of aluminum (Al 6063) and carbon steel (S45C) by monitoring the in-process cutting forces during CNC turning. The Fast Fourier Transform (FFT) is adopted to prove the relation between cutting force and straightness in frequency domain, which appear the same frequency. The cutting force ratio is proposed and normalized to predict the in-process straightness regardless of the cutting conditions. Firstly, the straightness is calculated by employing the two-layer feed-forward neural networks. The Levenberg-Marquardt backpropagation algorithm is utilized to train the system. Secondly, the multiple regression analysis has been applied to model the in-process prediction of straightness and the cutting force ratio under various cutting conditions with the use of least square method at 95% confidence level. Finally, the experimentally obtained results from the neural networks are compared with the ones obtained from the developed straightness model. It had been proved that the in-process straightness can be well predicted under various cutting conditions by using the trained neural networks.