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, includi...
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| Format: | Article |
| Language: | English |
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Qubahan
2024-03-01
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| Series: | Qubahan Academic Journal |
| Online Access: | https://journal.qubahan.com/index.php/qaj/article/view/465 |
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| author | Nurezayana Zainal Mohanavali Sithambranathan Umar Farooq Khattak Azlan Mohd Zain Salama A. Mostafa Ashanira Mat Deris |
| author_facet | Nurezayana Zainal Mohanavali Sithambranathan Umar Farooq Khattak Azlan Mohd Zain Salama A. Mostafa Ashanira Mat Deris |
| author_sort | Nurezayana Zainal |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-0024f7dde34e4fa78872230337614970 |
| institution | Kabale University |
| issn | 2709-8206 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Qubahan |
| record_format | Article |
| series | Qubahan Academic Journal |
| spelling | doaj-art-0024f7dde34e4fa788722303376149702025-02-03T10:12:09ZengQubahanQubahan Academic Journal2709-82062024-03-014110.48161/qaj.v4n1a465465Optimization of Electrical Discharge Machining Process by Metaheuristic AlgorithmsNurezayana Zainal0Mohanavali Sithambranathan1Umar Farooq Khattak2Azlan Mohd Zain3Salama A. Mostafa4Ashanira Mat Deris5Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia;Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia; School of Information Technology, UNITAR International University, Kelana Jaya, 47301 Petaling Jaya, Selangor, Malaysia; School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Sekudai Johor, Malaysia; Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia; Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; 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. https://journal.qubahan.com/index.php/qaj/article/view/465 |
| spellingShingle | Nurezayana Zainal Mohanavali Sithambranathan Umar Farooq Khattak Azlan Mohd Zain Salama A. Mostafa Ashanira Mat Deris Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms Qubahan Academic Journal |
| 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 |
| url | https://journal.qubahan.com/index.php/qaj/article/view/465 |
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