Experimental investigation and TLBO-based multi-objective optimization of EDM machining of duplex stainless steel AISI 2205

Le résumé fourni par la source

Abstract Duplex Stainless steel (DSS) has two phases in its structure: ferrite and austenite. The combination of these phases results in good toughness and strength, and better corrosion resistance when exposed to harsh environments. Owing to these properties, the applications of DSS have increased in various industries. These increasing applications draw researchers’ attention to this material and its processing. Processing or manufacturing of these materials requires high precision, accuracy, and efficiency within a minimal time. Achieving these goals requires a comprehensive understanding of machine process parameters for specific materials. The present study investigates the influence of electro-discharge machining (EDM) parameters on DSS 2205. Taguchi and response surface methodology experimental designs are employed to optimize the EDM process parameters. During machining, optimal parameters may reduce the chances of material wastage and improve product quality. Therefore, it is crucial to enhance the process’s performance and efficiency by analyzing its parameters. In this study, the Taguchi method, coupled with teaching learning-based optimization (TLBO) analysis, is employed to optimize the EDM process parameters, namely pulse-on time, pulse-off time, peak current, and gap voltage. The performance of machining is evaluated in terms of material removal rate (MRR) and surface roughness (SR). The empirical model generated for MRR & SR has R 2 values of 0.96 & 0.94, respectively. The results highlight the optimal set of parameters that enhance machining performance, providing valuable insights for efficient and precise machining of DSS in advanced manufacturing applications, with improvements of 4% in MRR & 3.758% in SR using the TLBO algorithm. This suggests that the integration of a population-based metaheuristic optimization algorithm improves the efficiency of complex Manufacturing Systems.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.