Multi-objective optimization of turning titanium-based alloy Ti-6Al-4V under dry, wet, and cryogenic conditions using gray relational analysis (GRA)

In modern manufacturing industries, the importance of multi-objective optimization cannot be overemphasized particularly when the desired responses are differing in nature towards each other. With the emergence of new technologies, the need to achieve overall efficiency in terms of energy, output, and tooling is on the rise. Resultantly, endeavor is to make the machining process sustainable, productive, and efficient simultaneously. In this research, the effects of machining parameters (feed, cutting speed, depth of cut, and cutting condition including dry, wet, and cryogenic) were analyzed. Since sustainable production demands a balance between production quality and energy consumption, therefore, response parameters including specific cutting energy, tool wear, surface roughness, and material removal rate were considered. Taguchi-gray integrated approach was adopted in this study. Multi-objective function was developed using gray relational methodology, and its regression analysis was conducted. Response surface optimization was carried out to optimize the formulated multi-objective function and derive the optimum machining parameters. Concurrent responses were optimized with best-suited values of input parameters to make the most out of the machining process. Analysis of variance results showed that feed is the most effective parameter followed by cutting condition in terms of overall contribution in multi-objective function. The proposed optimum parameters resulted in improvement of tool wear and surface roughness by 30% and 22%, respectively, whereas specific cutting energy was reduced by 4%.