An intuitionistic fuzzy extension to RAMS-RATMI methods for optimizing electrical discharge machining processes

The rising demand for materials with superior mechanical properties has motivated the engineering of several high-strength, heat-resistant alloys. To overcome the drawbacks of conventional machining methods, Electrical Discharge Machining (EDM) turns out to be a more feasible way of cutting such materials. However, improper setting of its different input parameters may severely affect the surface integrity of the machined parts and cause excessive tool wear. Multi-Criteria Decision-Making (MCDM) approaches have emerged as competent mathematical tools capable of handling multiple input factors and their interactions with numerous conflicting responses to figure out the ideal EDM process parameter values. In this paper, two recently introduced MCDM methods, namely Ranking Alternatives by Median Similarity (RAMS) and Ranking Alternatives by Trace to Median Index (RATMI), integrated with intuitionistic fuzzy sets (IFSs) for taking into account the uncertainty inherent in the opinions of different stakeholders, are proposed in a single framework to optimize two EDM processes. For the first EDM process, the ideal combination of different input factors is derived from discharge current = 3A, pulse-on time = 10 µs, pulse-off time = 5 µs, and copper as the tool material. On the other hand, for the second process, there is a tie between two combinations of EDM parameters, i.e. peak current = 10 A, pulse-on time = 500 µs, and gap voltage = 45 V; and peak current = 10 A, pulse-on time = 1000 µs, and gap voltage = 50 V. Furthermore, comparative analyses against other well-known MCDM tools and sensitivity analysis studies by varying the importance of responses are also conducted for both the processes validating reliability and consistency of the ranks obtained using the proposed IF-RAMS and IF-RATMI approaches.