Neural network modelling and multiobjective optimization of creep feed grinding of superalloys

Abstract

The grinding process is a very complex system for which analytical and empirical models have been developed to pursue a control strategy. This paper utilizes a new approach to model the creep feed grinding of superalloys, Ti-6Al-4V and Inconel 718, by using a neural network. A back-propagation learning algorithm is adopted to capture the system behaviour. The neural network learns to associate the inputs (feed rate, depth of cut and wheel bond type) with the outputs (surface finish, force and power) and predicts the systems outputs within the working conditions. Mathematical formulation of a multiobjective optimization problem is then carried out by utilizing the network models. The optimization study results are presented in the form of decision tables and value path diagrams to assist the decision-making process