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Research Article Volume 9 Issue 1
Multi-objective optimization of near-dry electric discharge machining process using genetic algorithm and scalarization
Rhushikesh Deshpande, Krishnakant Dhakar, Sumit Dwivedi, OB Bembalge, TS Srivatsan
Received: April 11, 2025 | Published: June 4, 2025
Citation: Deshpande R, Dhakar K, Dwivedi S, et al. Multi-objective optimization of near-dry electric discharge machining process using genetic algorithm and scalarization. Material Sci & Eng. 2025;9(1):20-25. DOI: 10.15406/mseij.2025.09.00251
Abstract
Near Dry Electric Discharge Machining (NDEDM) represents a significant advancement in advanced manufacturing by replacing the conventional dielectric pool with a spraybased dielectric delivery system, thereby enhancing both the portability and practicality of the process. This study focuses on optimizing the key process parameters: (i) Current (A), (ii) Pulse-on Time (µs), (iii) Gap V oltage (V), (iv) Liquid Flow Rate (ml/min), and (v) Workpiece Feed Rate (mm/min) for the purpose of achieving a balanced outcome in terms of (i) Material Removal Rate (MRR), (ii) Tool Wear Rate (TWR), and (iii) Surface Roughness (SR). The primary objective was to maximize MRR while minimizing both TWR and SR. A multi-objective Genetic Algorithm was used for optimization, utilizing predictive regression models developed through multiple regression analysis of the data obtained from experiments that were designed using the Taguchi Design of Experiments approach. Scalarization techniques were applied to incorporate weighted preferences for each response, thereby enabling in an exploration of the trade-offs among MRR, TWR, and SR. The study did reveal a valuable insight into the interdependencies of machining parameters, with the multi-objective genetic algorithm demonstrating high predictive accuracy—as was evident from minimal deviation between the predicted outcome and experimental outcome. The optimized results highlight a favorable balance, enhancing productivity while concurrently maintaining quality, and contribute meaningfully to the field of machining optimization.
Keywords: near-dry EDM, optimization, genetic algorithm and scalarization
©2025 Deshpande, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.