Prediction of Optimal Cutting States during Inward Turning: An Experimental Approach

Abstract

This article proposes a neurogenetic based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool–overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms (Gas). Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminium operated over a high speed steel (HSS) tool.

Keywords:

• Critical chatter length
• Neural networks
• Optimum parameters
• Orthogonal turning
• Tool overhang