Management Systems in Production Engineering | 2026
Authors: Mohammed A.R.; Jabar M.S.; Jaber A.S.
DOI: 10.2478/mspe-2026-0016
Journal: Management Systems in Production Engineering
Year: 2026
Publisher: Sciendo
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
Electric Discharge Machining (EDM) is known for its ability to machine hard and brittle conductive materials, as it can melt any electrically conductive substance irrespective of its hardness and geometric intricacy. Machining technologies have progressively advanced from a basic tool and die manufacturing process. The purpose of this paper is to investigate the performance in achieving the maximum material removal rate (MRR) while minimizing surface roughness (Ra) and tool wear rate (TWR) together and to optimize the parameters' operation utilizing ANOVA, RSM, and the hybrid BPNN-PSO. The AISI 304 L stainless steel is machined under different parameters, such as peak current (IP), pulse-on time (Ton), and pulse-off time (Toff). The ANOVA was used to identify significant components in single-objective optimization, whereas the Response Surface Methodology (RSM) was used for multiple-objective optimization of the parameters. The hybrid BPNN-PSO algorithm was used in single and multiple-objective modes to improve the parameters that were worked on in two scenarios. The results showed that the electrical current had a 75% impact on the output. From the analysis, the hybrid BPNN-PSO result achieved significant improvement over both the ANOVA and the RSM algorithms. More specifically, IP was optimized by 1.25 times on the quantity of MRS technique, whereas Ton was 58.18 μs in the second scenario. Based on these data, the second scenario is capable of achieving even better results in terms of optimization. © 2026 Atheer R. Mohammed et al., published by STE Group sp. z.o.o.
electric discharge machining; hybrid algorithms; response surface methodology; taguchi