Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

• Published in: International journal on interactive design and manufacturing Vol. 19; no. 3; pp. 2049 - 2060
• Main Authors: Sen, Binayak, Dasgupta, Archisman, Bhowmik, Abhijit
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.03.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; CAE) and Design; Computer-Aided Engineering (CAD; Cost analysis; Cost control; Cost estimates; EDM electrodes; Efficiency; Electric discharge machining; Electronics and Microelectronics; Energy consumption; Engineering; Engineering Design; Evolutionary algorithms; Genetic algorithms; Industrial Design; Instrumentation; Load; Manufacturing; Mechanical Engineering; Multiple objective analysis; Optimization; Original Article; Particle swarm optimization; Process parameters; Product design; Product development; Productivity; Response surface methodology; Wire; Genetic algorithm (GA); Inconel 800; Particle swarm optimization (PSO); Response surface methodology (RSM); Wire electric discharge machining
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-024-02001-y

In the rapidly evolving landscape of technology, controlling costs in the Wire Electrical Discharge Machining (WEDM) process has emerged as a pivotal concern in the manufacturing industry. The overall machining cost in WEDM hinges on factors such as power consumption, wire electrode usage, and dielectric fluid consumption to complete the machining process. This paper endeavors to present a model for estimating machining costs by establishing correlations between cost calculations and machining parameters. The model provides a comprehensive breakdown of the cost components associated with machining each specimen. Utilizing Response Surface Methodology (RSM), a meta-model was constructed, expressing the machining cost as a function of WEDM process parameters ( T ON , T OFF , IP , and SV ). Subsequently, this mathematical model underwent optimization using two widely adopted multi-objective optimization techniques: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The aim was to derive an optimal set of solutions for machining cost minimization. The findings of the study indicated that both PSO and GA are effective in the realm of process parameter optimization. However, PSO exhibits greater promise than GA, as it converges to the objective with fewer generations. This suggests that PSO could be a more efficient and practical choice for machining parameter optimization.