End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization
The end-milling procedure has been widely used for machining glass-fiber-reinforced polymer composite (GFRP) materials. A complex interaction of reinforcing glass fibers with each other as well as the matrix element during the end-milling process can result in high cutting force (CF), surface roughn...
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Universitas Indonesia
2025-01-01
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| Series: | International Journal of Technology |
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| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6321 |
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| author | Mohammad Khoirul Effendi Bobby O. P. Soepangkat Dinny Harnany Rachmadi Norcahyo |
| author_facet | Mohammad Khoirul Effendi Bobby O. P. Soepangkat Dinny Harnany Rachmadi Norcahyo |
| author_sort | Mohammad Khoirul Effendi |
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| description | The end-milling procedure has been widely used for machining glass-fiber-reinforced polymer composite (GFRP) materials. A complex interaction of reinforcing glass fibers with each other as well as the matrix element during the end-milling process can result in high cutting force (CF), surface roughness (SR), and delamination factor (DF) because of the anisotropic nature of GFRP. To reduce the three responses (CF, SR, and DF) at the same time, the end-milling cutting parameters, i.e., rotating speed (n), feed speed (Vf), and axial depth of cut (d), must carefully be determined. In this study, the end-milling of GFRP composites was investigated by utilizing a full factorial design of trials with three distinct values of n, Vf, and d. Also, a mix of genetic algorithms (GA) and backpropagation neural networks (BPNN) was administered to forecast the responses and obtain the optimized end-milling parameters. The firefly algorithm (FA), GA, and the integration of GA and the simulated annealing algorithm (SAA) were used to discover the best combination of end-milling parameter levels to reduce the responses' total variance. Later, the combination of BPNN and GA-SAA capable of accurately predicting multi-response characteristics and significantly improving multi-response characteristics was obtained through analyzing the confirmation experiment. |
| format | Article |
| id | doaj-art-c8294dfbdd39408fbac95abbcd1ca5f9 |
| institution | OA Journals |
| issn | 2086-9614 2087-2100 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Indonesia |
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| series | International Journal of Technology |
| spelling | doaj-art-c8294dfbdd39408fbac95abbcd1ca5f92025-08-20T02:29:19ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002025-01-011619711110.14716/ijtech.v16i1.63216321End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance OptimizationMohammad Khoirul Effendi0Bobby O. P. Soepangkat1Dinny Harnany2Rachmadi Norcahyo3Department of Mechanical Engineering, Faculty of Industrial Technology and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Mechanical Engineering, Faculty of Industrial Technology and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Mechanical Engineering, Faculty of Industrial Technology and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaThe end-milling procedure has been widely used for machining glass-fiber-reinforced polymer composite (GFRP) materials. A complex interaction of reinforcing glass fibers with each other as well as the matrix element during the end-milling process can result in high cutting force (CF), surface roughness (SR), and delamination factor (DF) because of the anisotropic nature of GFRP. To reduce the three responses (CF, SR, and DF) at the same time, the end-milling cutting parameters, i.e., rotating speed (n), feed speed (Vf), and axial depth of cut (d), must carefully be determined. In this study, the end-milling of GFRP composites was investigated by utilizing a full factorial design of trials with three distinct values of n, Vf, and d. Also, a mix of genetic algorithms (GA) and backpropagation neural networks (BPNN) was administered to forecast the responses and obtain the optimized end-milling parameters. The firefly algorithm (FA), GA, and the integration of GA and the simulated annealing algorithm (SAA) were used to discover the best combination of end-milling parameter levels to reduce the responses' total variance. Later, the combination of BPNN and GA-SAA capable of accurately predicting multi-response characteristics and significantly improving multi-response characteristics was obtained through analyzing the confirmation experiment.https://ijtech.eng.ui.ac.id/article/view/6321back propagation neural networkend-millinggenetic algorithm - simulated annealing algorithmglass-fiber-reinforced polymerfirefly algorithm |
| spellingShingle | Mohammad Khoirul Effendi Bobby O. P. Soepangkat Dinny Harnany Rachmadi Norcahyo End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization International Journal of Technology back propagation neural network end-milling genetic algorithm - simulated annealing algorithm glass-fiber-reinforced polymer firefly algorithm |
| title | End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization |
| title_full | End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization |
| title_fullStr | End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization |
| title_full_unstemmed | End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization |
| title_short | End-Milling of GFRP Composites with A Hybrid Method for Multi-Performance Optimization |
| title_sort | end milling of gfrp composites with a hybrid method for multi performance optimization |
| topic | back propagation neural network end-milling genetic algorithm - simulated annealing algorithm glass-fiber-reinforced polymer firefly algorithm |
| url | https://ijtech.eng.ui.ac.id/article/view/6321 |
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