Modeling of dimensional errors in slender bar turning using artificial neural networks

Abstract

Dimensional error is one of the most important product quality characteristics during slender bar turning operations. In this study, an artificial neural network model was developed to describe the relationship between cutting parameters and dimensional errors in slender bar turning process. For including the effect of part stiffness on dimensional errors, the diameter and the ratio of length to diameter of the machined parts were considered as independent input parameters of the model. Experimental results showed that the developed model had the ability to predict efficiently dimensional errors within the range in which they had been trained. Finally, the obtained model was used to investigate the influence of cutting parameters on dimensional errors.