Multi-response Optimization of Hybrid Machining Processes Using Evaluation Based on Distance from Average Solution Method in Intuitionistic Fuzzy Environment

Hybrid non-traditional machining processes have established their dominance over the naive machining processes because of their ability to have better machining performance as compared with the individual constituent processes. However, efficiency of any machining process is supposed to be affected by the poor machining environment resulting in uncertain and vague performance measures. Hence, this paper contributes in finding out the optimal parametric mixes of two hybrid non-traditional machining processes, i.e. laser-assisted jet electrochemical machining and electrochemical discharge drilling processes using evaluation based on distance from average solution method while removing uncertainty and vagueness in the dataset under intuitionistic fuzzy environment. The derived optimal parametric mixes are then validated with the help of developed regression equations which indicate superiority of the adopted technique over the other popular optimization approaches. Analysis of variance results further help in singling out the most influential input parameters for the considered hybrid non-traditional machining processes. Finally, the corresponding surface plots are developed to assist the process engineers in selecting the most appropriate combination of process parameters for achieving the desired response values.