Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints
Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. Th...
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| Published in | Journal of cleaner production Vol. 245; p. 118714 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. |
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| AbstractList | Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. |
| ArticleNumber | 118714 |
| Author | Chen, Maoning Tao, Fei Tian, Guangdong Li, Zhiwu AI-Ahmari, Abdulraham Zhang, Chaoyong Jiang, Zhigang Wang, Wenjie |
| Author_xml | – sequence: 1 givenname: Wenjie surname: Wang fullname: Wang, Wenjie organization: School of Mechanical Engineering, Shandong University, Jinan, 250061, China – sequence: 2 givenname: Guangdong surname: Tian fullname: Tian, Guangdong email: tiangd2013@163.com organization: School of Mechanical Engineering, Shandong University, Jinan, 250061, China – sequence: 3 givenname: Maoning surname: Chen fullname: Chen, Maoning organization: Faculty of Robotics Science and Engineering, Northeastern University, Shenyang, 110004, China – sequence: 4 givenname: Fei surname: Tao fullname: Tao, Fei organization: Institute of Science and Technology, Beihang University, Beijing, 100191, China – sequence: 5 givenname: Chaoyong surname: Zhang fullname: Zhang, Chaoyong organization: State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan, Hubei, 430074, China – sequence: 6 givenname: Abdulraham surname: AI-Ahmari fullname: AI-Ahmari, Abdulraham organization: Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia – sequence: 7 givenname: Zhiwu surname: Li fullname: Li, Zhiwu organization: Institute of Systems Engineering, Macau University of Science and Technology, Taipa, 999078, Macau, China – sequence: 8 givenname: Zhigang surname: Jiang fullname: Jiang, Zhigang organization: College of Machinery and Automation, Wuhan University of Science & Technology, Wuhan, 430081, China |
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| Keywords | Scheduling and optimization Intelligent algorithm Milling process model Energy consumption model Dual-objective optimization |
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| Title | Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints |
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