Optimization design for die-sinking EDM process parameters employing effective intelligent method
AbstractElectrical discharge machining (EDM) is a highly regarded method for producing ultra-precise mechanical parts. In this study, the process parameters of die-sinking EDM using copper electrodes and American Iron and Steel Institute (AISI) P20 tool steel workpieces are optimized for various out...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2023.2264060 |
| _version_ | 1827315362970992640 |
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| author | Van Tron Tran Minh Huy Le Minh Thai Vo Quoc Trung Le Van Huong Hoang Ngoc-Thien Tran Van-Thuc Nguyen Thi-Anh-Tuyet Nguyen Hoai Nam Nguyen Van Thanh Tien Nguyen Thanh Tan Nguyen |
| author_facet | Van Tron Tran Minh Huy Le Minh Thai Vo Quoc Trung Le Van Huong Hoang Ngoc-Thien Tran Van-Thuc Nguyen Thi-Anh-Tuyet Nguyen Hoai Nam Nguyen Van Thanh Tien Nguyen Thanh Tan Nguyen |
| author_sort | Van Tron Tran |
| collection | DOAJ |
| description | AbstractElectrical discharge machining (EDM) is a highly regarded method for producing ultra-precise mechanical parts. In this study, the process parameters of die-sinking EDM using copper electrodes and American Iron and Steel Institute (AISI) P20 tool steel workpieces are optimized for various output responses. The study surveys three input parameters, including Current (I), Pulse on Time (Ton), and Pulse Off Time (Toff). Some statistical methods, such as Taguchi and Analysis of Variance (ANOVA), are applied to find the optimal set of parameters for the output responses, consisting of Material Removal Rate (MRR), Electrode Wear Rate (EWR), and Surface Roughness (SR), and determine the most influential input factor. With the L9 Orthogonal Array (OA), the analytical results demonstrate the optimal parameter set for MRR is I = 6 A, Ton = 120 µs, and Toff = 30 µs, while those optimal values for EWR and SR are I = 2 A, Ton = 120 µs, and Toff = 90 µs and I = 2 A, Ton = 60 µs, and Toff = 30 µs, respectively. The study also indicates that input factor I has the most effect on the output responses, followed by Ton and Toff. Moreover, Grey relational analysis in the Taguchi method is also employed for multi-response optimization. The optimal parameter set for the three output factors is I = 6 A, Ton = 120 µs, and Toff = 60 µs, respectively. In this research, the microstructure and recast layer of the machined surfaces are investigated using optical microscopy as well. |
| first_indexed | 2024-03-08T23:54:11Z |
| format | Article |
| id | doaj.art-e77d48a048e24e8ca673bee50e1019e6 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-04-24T22:53:05Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-e77d48a048e24e8ca673bee50e1019e62024-03-18T10:22:11ZengTaylor & Francis GroupCogent Engineering2331-19162023-12-0110210.1080/23311916.2023.2264060Optimization design for die-sinking EDM process parameters employing effective intelligent methodVan Tron Tran0Minh Huy Le1Minh Thai Vo2Quoc Trung Le3Van Huong Hoang4Ngoc-Thien Tran5Van-Thuc Nguyen6Thi-Anh-Tuyet Nguyen7Hoai Nam Nguyen8Van Thanh Tien Nguyen9Thanh Tan Nguyen10Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of High Quality Training, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of High Quality Training, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of High Quality Training, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, VietnamAbstractElectrical discharge machining (EDM) is a highly regarded method for producing ultra-precise mechanical parts. In this study, the process parameters of die-sinking EDM using copper electrodes and American Iron and Steel Institute (AISI) P20 tool steel workpieces are optimized for various output responses. The study surveys three input parameters, including Current (I), Pulse on Time (Ton), and Pulse Off Time (Toff). Some statistical methods, such as Taguchi and Analysis of Variance (ANOVA), are applied to find the optimal set of parameters for the output responses, consisting of Material Removal Rate (MRR), Electrode Wear Rate (EWR), and Surface Roughness (SR), and determine the most influential input factor. With the L9 Orthogonal Array (OA), the analytical results demonstrate the optimal parameter set for MRR is I = 6 A, Ton = 120 µs, and Toff = 30 µs, while those optimal values for EWR and SR are I = 2 A, Ton = 120 µs, and Toff = 90 µs and I = 2 A, Ton = 60 µs, and Toff = 30 µs, respectively. The study also indicates that input factor I has the most effect on the output responses, followed by Ton and Toff. Moreover, Grey relational analysis in the Taguchi method is also employed for multi-response optimization. The optimal parameter set for the three output factors is I = 6 A, Ton = 120 µs, and Toff = 60 µs, respectively. In this research, the microstructure and recast layer of the machined surfaces are investigated using optical microscopy as well.https://www.tandfonline.com/doi/10.1080/23311916.2023.2264060material removal rateelectrode wear ratesurface roughnessTaguchiANOVA analysisoptimization design |
| spellingShingle | Van Tron Tran Minh Huy Le Minh Thai Vo Quoc Trung Le Van Huong Hoang Ngoc-Thien Tran Van-Thuc Nguyen Thi-Anh-Tuyet Nguyen Hoai Nam Nguyen Van Thanh Tien Nguyen Thanh Tan Nguyen Optimization design for die-sinking EDM process parameters employing effective intelligent method Cogent Engineering material removal rate electrode wear rate surface roughness Taguchi ANOVA analysis optimization design |
| title | Optimization design for die-sinking EDM process parameters employing effective intelligent method |
| title_full | Optimization design for die-sinking EDM process parameters employing effective intelligent method |
| title_fullStr | Optimization design for die-sinking EDM process parameters employing effective intelligent method |
| title_full_unstemmed | Optimization design for die-sinking EDM process parameters employing effective intelligent method |
| title_short | Optimization design for die-sinking EDM process parameters employing effective intelligent method |
| title_sort | optimization design for die sinking edm process parameters employing effective intelligent method |
| topic | material removal rate electrode wear rate surface roughness Taguchi ANOVA analysis optimization design |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2023.2264060 |
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