Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms

Abstract

Abstract This paper explores use of Teaching Learning Based Optimization (TLBO), ‘JAYA’ (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both – roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, ‘JAYA’ and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and ‘JAYA’ algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and ‘JAYA’ performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. Highlights This paper Presents, implementation of Advanced Algorithms for multi objective optimization of cutting force and surface roughness in machining of difficult to cut Ti-6Al-4V. Two newly developed advanced algorithms such as JAYA and Teaching learning based optimization (TLBO) ‘without algorithm control’ parameters are used for machining response optimization. Objective functions for surface roughness and cutting forces are developed after actual face milling operation performed in sequential manner with response surface methodology. Developed models are verified with statistical test (ANOVA, residual plots) as well as confirmation experiments. It is concluded from the results that machining parameters can be optimize using advanced algorithms. This work can help machinists to select cutting parameters based on desired machining response.