Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft
A multi-objective grasshopper optimization algorithm (MOGOA) with an adaptive curve c(t) and the enhanced Levy fight strategy (CLMOGOA) was proposed to optimize the process parameters of rotary screen coating, setting the thickness and uniformity of the adhesive layer on the structural plates in spa...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-0825/13/12/469 |
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| author | Yanhui Guo Yanpeng Chen Peibo Li Xinfu Chi Yize Sun |
| author_facet | Yanhui Guo Yanpeng Chen Peibo Li Xinfu Chi Yize Sun |
| author_sort | Yanhui Guo |
| collection | DOAJ |
| description | A multi-objective grasshopper optimization algorithm (MOGOA) with an adaptive curve c(t) and the enhanced Levy fight strategy (CLMOGOA) was proposed to optimize the process parameters of rotary screen coating, setting the thickness and uniformity of the adhesive layer on the structural plates in spacecraft as its optimization objectives. The adaptive curve strikes a balance between global exploration and local development and accelerates the convergence speed. The enhanced Levy strategy helps the algorithm to escape local optimizations, increases the population diversity, and possesses dual searching capabilities. After multiple runs, the average values of the CLMOGOA’s reverse generation distance were 0.0288, 0.0233, and 0.1810 on the test sets, which were less than those of the MOGOA. The best Pareto-optimal front obtained by the CLMOGOA had a higher accuracy and better coverage compared to that of the MOGOA. Thus, it is indicated that the CLMOGOA managed to outperform the MOGOA on the test functions. In order to solve the optimization problem, 108 sets of process experiments were designed, and then the experimental data were used to train a Back Propagation Neural Network (BPNN), a Least Squares Support Vector Machine (LSSVM), and Random Forest (RF) to obtain the best prediction model for the process parameters. Considering the thickness and uniformity of the adhesive layer as the objectives, the improved algorithm was used to optimize the prediction model to obtain the optimal process parameters. The actual coating effect showed that the optimization algorithm improved the efficiency and qualification rate of the product. |
| format | Article |
| id | doaj-art-20054d03f3c54a0e8aa5df4361eeb56e |
| institution | DOAJ |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Actuators |
| spelling | doaj-art-20054d03f3c54a0e8aa5df4361eeb56e2025-08-20T02:53:38ZengMDPI AGActuators2076-08252024-11-01131246910.3390/act13120469Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in SpacecraftYanhui Guo0Yanpeng Chen1Peibo Li2Xinfu Chi3Yize Sun4College of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaShanghai Institute of Spacecraft Equipment, Shanghai 200240, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaA multi-objective grasshopper optimization algorithm (MOGOA) with an adaptive curve c(t) and the enhanced Levy fight strategy (CLMOGOA) was proposed to optimize the process parameters of rotary screen coating, setting the thickness and uniformity of the adhesive layer on the structural plates in spacecraft as its optimization objectives. The adaptive curve strikes a balance between global exploration and local development and accelerates the convergence speed. The enhanced Levy strategy helps the algorithm to escape local optimizations, increases the population diversity, and possesses dual searching capabilities. After multiple runs, the average values of the CLMOGOA’s reverse generation distance were 0.0288, 0.0233, and 0.1810 on the test sets, which were less than those of the MOGOA. The best Pareto-optimal front obtained by the CLMOGOA had a higher accuracy and better coverage compared to that of the MOGOA. Thus, it is indicated that the CLMOGOA managed to outperform the MOGOA on the test functions. In order to solve the optimization problem, 108 sets of process experiments were designed, and then the experimental data were used to train a Back Propagation Neural Network (BPNN), a Least Squares Support Vector Machine (LSSVM), and Random Forest (RF) to obtain the best prediction model for the process parameters. Considering the thickness and uniformity of the adhesive layer as the objectives, the improved algorithm was used to optimize the prediction model to obtain the optimal process parameters. The actual coating effect showed that the optimization algorithm improved the efficiency and qualification rate of the product.https://www.mdpi.com/2076-0825/13/12/469multi-objective optimization algorithmoptimal process parametersrotary screen coatingprediction modelspacecraft structural plates |
| spellingShingle | Yanhui Guo Yanpeng Chen Peibo Li Xinfu Chi Yize Sun Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft Actuators multi-objective optimization algorithm optimal process parameters rotary screen coating prediction model spacecraft structural plates |
| title | Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft |
| title_full | Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft |
| title_fullStr | Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft |
| title_full_unstemmed | Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft |
| title_short | Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft |
| title_sort | multi objective parameter optimization of rotary screen coating process for structural plates in spacecraft |
| topic | multi-objective optimization algorithm optimal process parameters rotary screen coating prediction model spacecraft structural plates |
| url | https://www.mdpi.com/2076-0825/13/12/469 |
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