An Enhanced Glowworm Swarm Optimization for Minimizing Surface Roughness in Die Sinking Electrical Discharge Machining

Authors

• Nurezayana Zainal, Universiti Tun Hussein Onn Malaysia, Malaysia
• Muhammad Ammar S.M. Shahrom, ZEN Computer Systems Sdn Bhd, Malaysia
• Mohamad Firdaus Ab. Aziz, Universiti Tun Hussein Onn Malaysia, Malaysia
• Salama A. Mostafa, Al-noor University, Iraq
• Anis Farhan Kamaruzzaman, Universiti Teknologi Malaysia, Malaysia
• Nor Bakiah Abd. Warif, Universiti Tun Hussein Onn Malaysia, Malaysia

Corresponding Author ORCID Identifer

Nurezayana Zainal https://orcid.org/0000-0002-5424-8410
Salama A. Mostafa https://orcid.org/0000-0001-5348-502X

Section

(Thematic) Next Wave

Abstract

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in die-making, aerospace, automotive manufacturing, and medical manufacturing, because it can machine hard and complex materials with high precision. In this work, a Surface Roughness Optimization for EDM (SRO-EDM) model is proposed to investigate the machining performance of the die-sinking EDM process of titanium alloys. A regression-based combined approach of Glowworm Swarm Optimization (GSO) and a Two-Factor Interaction (2FI) model has been proposed to investigate the impact of four key process variables, namely voltage (V), peak current (Ip), pulse-on time (ton), and pulse-off time (toff) on surface roughness (Ra) at various locations on work surfaces. A Central Composite Design (CCD) was applied to systematically investigate parameter combinations. Statistical analysis was performed using analysis of variance (ANOVA), which confirmed the statistical significance of the selected parameters, and 2FI regression (R² = 0.60) with moderate-fit predictive accuracy was established. To enhance the quality of optimization, the Enhanced Glowworm Swarm Optimization (EGSO) algorithm is proposed by hybridizing the GSO with Artificial Fish Swarm (AFS) algorithm. The AFS module improves the exploration capability of the GSO and alleviates the local optima problem. For the experimental validation of the model, Response Surface Methodology (RSM) was used to generate the regression based on the developed model and as an objective function for optimization. Experimental results show that EGSO outperforms GSO in performance to achieve an optimized Ra ( ) compared to through conventional GSO. The results demonstrate that the EGSO model can improve convergence accuracy and speed and is a practical method for EDM surface quality optimization in the high-precision manufacturing industry.

Cite This Article

Zainal, N., Muhammad Ammar S.M. Shahrom, Mohamad Firdaus Ab. Aziz, Mostafa, S. A. M., Anis Farhan Kamaruzzaman, & Nor Bakiah Abd. Warif. (2026). An Enhanced Glowworm Swarm Optimization for Minimizing Surface Roughness in Die Sinking Electrical Discharge Machining . Journal of Informatics and Web Engineering, 5(1), 344–357. https://doi.org/10.33093/jiwe.2026.5.1.22

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