ArticleScientific reports2025
Optimization of process parameter for green die sinking electrical discharge machining: a novel hybrid decision-making approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Performance evaluation and multi-objective optimization of EDM parameters for Ti6Al4V using different tool electrodes.Scientific reports · 2025Article
- Electrical discharge machining by rapid tools prepared by micro stereo-lithography process with copper metallization.Scientific reports · 2025Article
- Implementing a novel TOPSIS-sine cosine algorithm-based hybrid optimization in machining medium-hardened steel.Scientific reports · 2025Article
- Point cloud registration based on surface feature extraction and an improved Grey Wolf Optimization algorithm.Scientific reports · 2025Article
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Authors and funding
5 authors.
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Abstract
Electrical discharge machining (EDM) generates toxic emissions and hazardous waste, posing significant health risks for operators and environmental concerns. Aligning with the UN Sustainable Development Goals (SDGs) for Good Health & Well-being (SDG 3) and Responsible Consumption & Production (SDG 12), it is crucial to optimize EDM to reduce its environmental impact. This study introduces a Decision Support System (DSS) that uses a new approach to prospect theory based on exponential-logarithmic single-valued neutrosophic sets (± Log-SVNS) to find the best EDM parameters for "green" die-sinking EDM. Taguchi orthogonal array has been used to design the EDM experiments considering different levels of following process parameters: peak current, pulse duration, dielectric level, and flushing pressure. Log-SVNS structures expert evaluations on output responses which includes process time, tool wear ratio, energy consumption, aerosol concentration, and dielectric usage, and aggregates them with hybrid averaging and geometric operators. The proposed method employs prospect theory to finally determine the optimal machining parameters. The best performance is seen when the peak current is 2 A, the pulse duration is 520 µs, the dielectric level is 80 mm, and the flushing pressure is 0.5 kg/cm². This outcome is based on the µLog-SVNS hybrid average TODIM (TOmada de Decisao Interativa Multicriterio) method. The hybrid geometric TODIM method finds that experiment number 3 is the best and the parameters are as follows: peak current of 2 A, pulse duration of 261 µs, dielectric level of 60 mm, and flushing pressure of 0.7 kg/cm². Sensitivity analysis confirms the robustness of these results, and comparative analysis with existing methods demonstrates the effectiveness of the proposed method in establishing optimal parameters.
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