MODELLING OF SURFACE ROUGHNESS AND TOOL WEAR DURING THE TURNING OF INCONEL 601 ALLOY USING ARTIFICIAL NEURAL NETWORKS

Abstract

In this paper, the turning process of Inconel 601 is modeled. Turning process was performed with various cutting speeds, feeds, insert shapes, corner radius, rake angles and approach angles. After turning, the arithmetic mean surface roughness and flank wear were measured. For the measured values, the process is modeled using artificial neural networks. The generation of models with different architectures of artificial neural networks, was carried out through three training algorithms in order to determine the most adequate one. Validation of the model was performed through additional confirmation experiments. Prediction and measurement results were compared using percentage and absolute errors. The obtained data indicate that it is best to use the Levenberg-Marquardt algorithm for modeling the turning process using artificial neural networks.