Examination and prediction of process parameters for Surface roughness and MRR in VMC-five axis machining of D3 steel by using RSM and MTLBO

Abstract

VMC five-axis is modern machining method, used for milling operations that focus on rotary cutters to extract surplus material from a work piece. To achieve machining economics in VMC-5-axis machining, choice of optimum process variables is crucial. Surface roughness and material removal rate are significant response variables, and analyzing and optimizing both the responses combinedly is an important research area in milling. Current research is planned to analyze, model and predict the optimum milling parametric condition of VMC-5-axis machining of D3 tool steel to anticipate surface roughness and MRR simultaneously by applying response surface methodology and multi-objective teaching learning-based optimization (MTLBO). The experiments had been planned as per Box-Behnken design of response surface methodology. Statistical analysis of variance (ANOVA) is applied to determine the significances of milling conditions on milling performance characteristics. ANOVA results revels that milling processing conditions are more significant for MRR as compared to surface roughness. Modeling of responses is made by RSM. Contour plots are drawn from developed mathematical models for visualizing the direct and combined effects of milling conditions on both the responses. Multi-responses are solved by MTLBO technique. Predicted milling condition is verified by confirmatory test. The concluding remarks have been drawn from the study. From the present research work, it is found that milling responses are improved at predicted condition obtained by hybrid RSM and MTLBO approach.