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Experimental modeling techniques in electrical discharge machining (EDM): A review

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Abstract

Electrical discharge machining (EDM) is a widely used non-conventional machining technique in manufacturing industries, capable of accurately machining electrically conductive materials of any hardness and strength. However, to achieve low production costs and minimal machining time, a comprehensive understanding of the EDM system is necessary. Due to the stochastic nature of the process and the numerous variables involved, it can be challenging to develop an analytical model of EDM through theoretical and numerical simulations alone. This paper conducts an extensive review of the various experimental (or empirical) modeling techniques used by researchers over the past two decades, including a geographic and temporal analysis of these approaches. The major methods employed to describe the EDM process include regression, response surface methodology (RSM), fuzzy inference systems (FIS), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS). Additionally, the optimization methods used in conjunction with these methods are also discussed. Although RSM is the most commonly used empirical modeling technique, recent years have seen an increase in the use of ANN for providing the most accurate predictions of EDM process responses. The review of the literature shows that most of the investigations on experimental EDM modeling were conducted in Asia.

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Acknowledgements

Acknowledgement: First of all, we thank Allah (SWT) for providing us with the ability to conduct this research. The authors also acknowledge the research support provided by the International Islamic University Malaysia. We also thank the Ministry of Higher Education Malaysia and Mikrotools Pte. Ltd. for their generous funding to carry out the research.

Funding

This research was supported by the fund provided by Mikrotools Pte Ltd. (SPI22-126–0126). The corresponding author also acknowledges the research support provided by the Ministry of Higher Education Malaysia (PRGS/1/2022/TK03/UIAM/02/1).

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Authors and Affiliations

  1. Autonomous Systems and Robotics Research Unit (ASRRU), Department of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, 53100, Kuala Lumpur, Malaysia

    Mohammad Mainul Hasan, Tanveer Saleh & Ali Sophian

  2. Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh

    M. Azizur Rahman

  3. Department of Mechanical Engineering, McMaster Manufacturing Research Institute (MMRI), McMaster University, Hamilton, ON, L8S4L7, Canada

    M. Azizur Rahman

  4. College of Science and Engineering, James Cook University, Smithfield Cairns, Queensland, 4878, Australia

    Tao Huang

  5. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Johor, Malaysia

    Mohamed Sultan Mohamed Ali

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All authors contributed to the study. Material preparation, data collection and analysis were performed by Mohammad Mainul Hasan. Tanveer Saleh provided the idea of the paper and guided the structure. The first draft of the manuscript was written by Mohammad Mainul Hasan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Hasan, M.M., Saleh, T., Sophian, A. et al. Experimental modeling techniques in electrical discharge machining (EDM): A review. Int J Adv Manuf Technol 127, 2125–2150 (2023). https://doi.org/10.1007/s00170-023-11603-x

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