Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms
In the aerospace industry, many important components are made of ring forgings with characteristics of multi-variety and multi-batch. Such components have many steps and complex parameters in the thermoforming process. The process orders are dynamic and time-varying, and, thus, optimizing the total...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/10/1631 |
| _version_ | 1797471453991403520 |
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| author | Yizhe Chen Beichen Xie Huijuan Ma Hui Wang Yulong Zhou Jie Chen Lin Hua |
| author_facet | Yizhe Chen Beichen Xie Huijuan Ma Hui Wang Yulong Zhou Jie Chen Lin Hua |
| author_sort | Yizhe Chen |
| collection | DOAJ |
| description | In the aerospace industry, many important components are made of ring forgings with characteristics of multi-variety and multi-batch. Such components have many steps and complex parameters in the thermoforming process. The process orders are dynamic and time-varying, and, thus, optimizing the total production time and energy consumption is difficult. To solve the mentioned troublesome and time-consuming problem, this work transformed the workpiece’s required heating temperature and time index into the furnace temperature change and holding time index. Based on a genetic algorithm, an integrated production scheduling optimization of ring forging heating and model forming was established. The genetic algorithm for model improvement was optimized. The optimization objective was changed by using different fitness calculation methods. A multi-time simulation algorithm was designed to calculate each heating furnace’s time and furnace temperature. The proposed optimization method was used for a thermoforming process of ring forgings. When the optimization objective was designed to consider energy consumption and time consumption comprehensively, the average time saving was 6.93%, and the average energy saving was 12.99%. When the optimization objective was designed to prioritize energy consumption, the average time saving was 3.89%, and the average energy saving was 16.53%. When the optimization objective was designed to prioritize time consumption, the average time saving was 10.35%, and the average energy saving was 10.63%. Using the scheduling results for production, compared with the practical factory data, the errors in the simulation time and energy consumption were 2.4% and 1.6%. The results show that the scheduling efficiency of integrated thermoforming production is significantly improved by using this optimization model, and the simulation results have high reliability. The energy consumption of orders is greatly reduced, and the total production time is greatly shortened. |
| first_indexed | 2024-03-09T19:48:43Z |
| format | Article |
| id | doaj.art-d8fcbf3ac6ad4950b7086b066e52b7bd |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T19:48:43Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-d8fcbf3ac6ad4950b7086b066e52b7bd2023-11-24T01:18:09ZengMDPI AGMetals2075-47012022-09-011210163110.3390/met12101631Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic AlgorithmsYizhe Chen0Beichen Xie1Huijuan Ma2Hui Wang3Yulong Zhou4Jie Chen5Lin Hua6Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaJiangsu Branch of China Academy of Machinery Science and Technology Group Co., Ltd., Changzhou 213000, ChinaJiangsu Branch of China Academy of Machinery Science and Technology Group Co., Ltd., Changzhou 213000, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaIn the aerospace industry, many important components are made of ring forgings with characteristics of multi-variety and multi-batch. Such components have many steps and complex parameters in the thermoforming process. The process orders are dynamic and time-varying, and, thus, optimizing the total production time and energy consumption is difficult. To solve the mentioned troublesome and time-consuming problem, this work transformed the workpiece’s required heating temperature and time index into the furnace temperature change and holding time index. Based on a genetic algorithm, an integrated production scheduling optimization of ring forging heating and model forming was established. The genetic algorithm for model improvement was optimized. The optimization objective was changed by using different fitness calculation methods. A multi-time simulation algorithm was designed to calculate each heating furnace’s time and furnace temperature. The proposed optimization method was used for a thermoforming process of ring forgings. When the optimization objective was designed to consider energy consumption and time consumption comprehensively, the average time saving was 6.93%, and the average energy saving was 12.99%. When the optimization objective was designed to prioritize energy consumption, the average time saving was 3.89%, and the average energy saving was 16.53%. When the optimization objective was designed to prioritize time consumption, the average time saving was 10.35%, and the average energy saving was 10.63%. Using the scheduling results for production, compared with the practical factory data, the errors in the simulation time and energy consumption were 2.4% and 1.6%. The results show that the scheduling efficiency of integrated thermoforming production is significantly improved by using this optimization model, and the simulation results have high reliability. The energy consumption of orders is greatly reduced, and the total production time is greatly shortened.https://www.mdpi.com/2075-4701/12/10/1631production scheduling optimizationthermoformingring forgingsgenetic algorithmmulti-objective optimizationmulti-time simulation |
| spellingShingle | Yizhe Chen Beichen Xie Huijuan Ma Hui Wang Yulong Zhou Jie Chen Lin Hua Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms Metals production scheduling optimization thermoforming ring forgings genetic algorithm multi-objective optimization multi-time simulation |
| title | Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms |
| title_full | Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms |
| title_fullStr | Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms |
| title_full_unstemmed | Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms |
| title_short | Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms |
| title_sort | production scheduling optimization during thermoforming of ring forgings based on genetic algorithms |
| topic | production scheduling optimization thermoforming ring forgings genetic algorithm multi-objective optimization multi-time simulation |
| url | https://www.mdpi.com/2075-4701/12/10/1631 |
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