Integrated optimization methodology for intelligent machining of inconel 825 and its shop-floor application

Abstract

The machining optimization is one of the most essential tasks for economic production of components in manufacturing industries. The intelligent machining process aims to produce the components for desired surface quality at minimum machining time∕cost. The innovative aspect of this work is the development of an integrated optimization methodology used for optimizing the process parameters in machining Inconel 825 aerospace alloy. Turning experiments have been conducted with spindle speed (N), feed rate (f), and depth of cut (d) that are considered as process parameters and the centre line average value of surface roughness (R a) as response. The experimental study shows that R a is influenced by spindle speed followed by feed rate. An artificial neural network (ANN) model has been developed for predicting R a. The ANN architecture having 3–12–1 is found to be optimum network and model predicts unseen data sets with an average percentage error of 6.51 %. The predictive model is integrated with particle swarm optimization (PSO) approach to optimize the process parameters for desired surface roughness of jobs to be produced at minimum time. The robustness of the method shows its superiority and taken less than ten iterations. For shop-floor implementation of the approach, MATLAB-GUI (graphical user interface)-based interactive screens are developed that help the machine tool operator to obtain optimum process parameters.