A Comprehensive Review of Optimization Methods for WEDM of AISI D2 Steel

Abstract

WireElectricDischargeMachining(WEDM)isahighlyutilizedmachiningtechniqueacrossvariousindustries, particularly for die-punch fabrication and machining of hard, brittle materials. It is also extensively appliedin producing intricate and complex geometries with precision. The efficiency of the WEDM process largely depends onselecting appropriate machining parameters. In the manufacturing industry, optimization methods play a vital role indetermining the most effective machining conditions, enabling industries to manufacture high-quality components whileminimizing costs. However, identifying the ideal combination of process parameters to maximize the material removalrate in WEDM presents a significant challenge. This paper examines various optimization techniques used to improvematerialremovalratesbyrefiningkeymachining parameters.

Introduction

Electric Discharge Machining (EDM) is a non-traditional machining process used to shape hard, electrically conductive materials with precision, especially those difficult to machine conventionally. It works by generating controlled electrical sparks between the tool and workpiece, causing localized melting and vaporization to remove material. Wire EDM (WEDM) is a popular EDM variant that uses a thin charged wire and dielectric fluid to cut intricate metal parts accurately.

The literature review covers recent studies focusing on optimizing WEDM parameters like material removal rate (MRR), surface roughness (SR), and electrode wear rate (EWR) for various materials, including composites and tool steels. Methods such as genetic algorithms, artificial neural networks (ANN), Taguchi designs, response surface methodology (RSM), and grey relational analysis (GRA) have been applied to improve machining efficiency and precision.

The workpiece material of interest is AISI D2 tool steel, known for its high hardness, wear resistance, and moderate corrosion resistance, but with poor machinability. Experiments are conducted on a Wire-Cut EDM machine (Electronica Ultracut S2).

For optimizing the WEDM process on AISI D2 steel, several advanced techniques are used:

• Taguchi Method: For designing experiments and identifying optimal machining parameters.

• Response Surface Methodology (RSM): To model parameter interactions and optimize performance metrics.

• Grey Relational Analysis (GRA): For multi-response optimization balancing conflicting objectives.

• Genetic Algorithm (GA): An evolutionary approach to find the best parameter combinations.

• Artificial Neural Networks (ANN) and Machine Learning: For predictive modeling and real-time optimization.

• Particle Swarm Optimization (PSO): A swarm intelligence method for efficient multi-parameter optimization.

These approaches aim to enhance MRR, reduce surface roughness and tool wear, and improve overall machining precision and productivity for complex and hard materials like AISI D2 steel.

Conclusion

Different optimization techniques are used depending on the complexity of the WEDM process, number of parameters, andrequired accuracy. Traditional techniques like Taguchi and RSM are extensively used for experimental process optimization,whereas GA, ANN, and PSO are preferred for real-time and complex problem-solving. The Taguchi method is a practical andefficient approach for WEDM in AISI D2 steel, offering an organized method to identify optimal machining conditions. Byemploying orthogonal arrays (OA) and signal-to-noise (S/N) ratio analysis, this method improves material removal rate (MRR),surface quality, and dimensional accuracy while reducing inconsistencies. It minimizes experimental costs and ensures efficientparameter selectionwithfewertrials.Asaresult,theTaguchimethodiswidelyutilizedforenhancingprocessstability,increasingmachining efficiency,and ensuringhigh-quality output,making itakeytoolinWEDMprocessoptimization.

References

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Copyright

Copyright © 2025 Vishal Rajak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.