Optimization of Machining Parameters for Improving Energy Efficiency using Integrated Response Surface Methodology and Genetic Algorithm Approach

Machine tools consume enormous amount of energy during machining, build-up to machining, post machining and idling condition to drive motors and auxiliary equipments in the manufacturing system. Reduction of energy consumption during the machining phase is extremely important to improve the environmental performance over the entire life cycle. This paper presents a predictive and optimization model based on integrated response surface methodology and genetic algorithm approach to predict the energy consumption and the corresponding machining parameters during the turning of AISI 1045 steel with a tungsten carbide tool. Experiments using Taguchi design are performed to develop the predictive model. The developed predictive model is used to formulate the objective function for genetic algorithm. The confirmation experiments are performed to validate the developed model and the results are found within 4% error. The statistical significance of the developed model has been tested by the analysis of variance test. This research will be beneficial for a number of manufacturing industries for selection of machine tools on the basis of energy consumption. The reduction of peak load through optimization will results in lowering the energy consumption of the machine tools during non-cutting time. (C) 2017 The Authors. Published by Elsevier B.V.