An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes
It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) i...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3094670 |
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| author | Juan Lu Xiaoping Liao Steven Li Haibin Ouyang Kai Chen Bing Huang |
| author_facet | Juan Lu Xiaoping Liao Steven Li Haibin Ouyang Kai Chen Bing Huang |
| author_sort | Juan Lu |
| collection | DOAJ |
| description | It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε-insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes. |
| format | Article |
| id | doaj-art-11775f41b82f4ded8832bc4ad22b195a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-11775f41b82f4ded8832bc4ad22b195a2025-08-20T03:54:11ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/30946703094670An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing ProcessesJuan Lu0Xiaoping Liao1Steven Li2Haibin Ouyang3Kai Chen4Bing Huang5Guangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning 530004, ChinaGraduate School of Business and Law, RMIT University, Melbourne 3000, AustraliaSchool of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning 530004, ChinaIt is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε-insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.http://dx.doi.org/10.1155/2019/3094670 |
| spellingShingle | Juan Lu Xiaoping Liao Steven Li Haibin Ouyang Kai Chen Bing Huang An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes Complexity |
| title | An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes |
| title_full | An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes |
| title_fullStr | An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes |
| title_full_unstemmed | An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes |
| title_short | An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes |
| title_sort | effective abc svm approach for surface roughness prediction in manufacturing processes |
| url | http://dx.doi.org/10.1155/2019/3094670 |
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