Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material
Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2126 |
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| author | Mahdi S. Alajmi Abdullah M. Almeshal |
| author_facet | Mahdi S. Alajmi Abdullah M. Almeshal |
| author_sort | Mahdi S. Alajmi |
| collection | DOAJ |
| description | Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R<sup>2</sup> = 0.95 that closely matches the actual surface roughness experimental values. |
| first_indexed | 2024-03-09T06:16:56Z |
| format | Article |
| id | doaj.art-a4dfe36ffd8540c8acad602fd9b30736 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T06:16:56Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a4dfe36ffd8540c8acad602fd9b307362023-12-03T11:52:12ZengMDPI AGApplied Sciences2076-34172021-02-01115212610.3390/app11052126Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum MaterialMahdi S. Alajmi0Abdullah M. Almeshal1Department of Manufacturing Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, KuwaitDepartment of Electronic Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, KuwaitSurface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R<sup>2</sup> = 0.95 that closely matches the actual surface roughness experimental values.https://www.mdpi.com/2076-3417/11/5/2126machine learningoptimizationquantum computingface millingsurface roughnessensemble learning |
| spellingShingle | Mahdi S. Alajmi Abdullah M. Almeshal Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material Applied Sciences machine learning optimization quantum computing face milling surface roughness ensemble learning |
| title | Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material |
| title_full | Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material |
| title_fullStr | Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material |
| title_full_unstemmed | Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material |
| title_short | Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material |
| title_sort | least squares boosting ensemble and quantum behaved particle swarm optimization for predicting the surface roughness in face milling process of aluminum material |
| topic | machine learning optimization quantum computing face milling surface roughness ensemble learning |
| url | https://www.mdpi.com/2076-3417/11/5/2126 |
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