Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms
Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, includin...
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QAJ
2024
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| Online Access: | http://eprints.uthm.edu.my/12052/1/J17653_72080fc076ef7bb9b57bfd940a85f2ef.pdf http://eprints.uthm.edu.my/12052/ https://doi.org/10.48161/qaj.v4n1a465 |
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| author | Zainal, Nurezayana Sithambranathan, Mohanavali Khattak, Umar Farooq Mohd Zain, Azlan A. Mostafa, Salama Mat Deris, Ashanira |
| author_facet | Zainal, Nurezayana Sithambranathan, Mohanavali Khattak, Umar Farooq Mohd Zain, Azlan A. Mostafa, Salama Mat Deris, Ashanira |
| author_sort | Zainal, Nurezayana |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to
accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf
Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm. |
| format | Article |
| id | my.uthm.eprints-12052 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | QAJ |
| record_format | eprints |
| spelling | my.uthm.eprints-120522025-05-02T07:02:59Z http://eprints.uthm.edu.my/12052/ Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms Zainal, Nurezayana Sithambranathan, Mohanavali Khattak, Umar Farooq Mohd Zain, Azlan A. Mostafa, Salama Mat Deris, Ashanira TJ Mechanical engineering and machinery Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm. QAJ 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12052/1/J17653_72080fc076ef7bb9b57bfd940a85f2ef.pdf Zainal, Nurezayana and Sithambranathan, Mohanavali and Khattak, Umar Farooq and Mohd Zain, Azlan and A. Mostafa, Salama and Mat Deris, Ashanira (2024) Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms. QUBAHAN ACADEMIC JOURNAL, 4 (1). pp. 277-289. https://doi.org/10.48161/qaj.v4n1a465 |
| spellingShingle | TJ Mechanical engineering and machinery Zainal, Nurezayana Sithambranathan, Mohanavali Khattak, Umar Farooq Mohd Zain, Azlan A. Mostafa, Salama Mat Deris, Ashanira Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms |
| title | Optimization of Electrical Discharge Machining Process by
Metaheuristic Algorithms |
| title_full | Optimization of Electrical Discharge Machining Process by
Metaheuristic Algorithms |
| title_fullStr | Optimization of Electrical Discharge Machining Process by
Metaheuristic Algorithms |
| title_full_unstemmed | Optimization of Electrical Discharge Machining Process by
Metaheuristic Algorithms |
| title_short | Optimization of Electrical Discharge Machining Process by
Metaheuristic Algorithms |
| title_sort | optimization of electrical discharge machining process by
metaheuristic algorithms |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/12052/1/J17653_72080fc076ef7bb9b57bfd940a85f2ef.pdf http://eprints.uthm.edu.my/12052/ https://doi.org/10.48161/qaj.v4n1a465 |
| url_provider | http://eprints.uthm.edu.my/ |