An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting
Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resou...
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MDPI AG
2025-04-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/5/273 |
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| author | Qin Yang Xinning Li Teng Yang Hu Wu Liwen Zhang |
| author_facet | Qin Yang Xinning Li Teng Yang Hu Wu Liwen Zhang |
| author_sort | Qin Yang |
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| description | Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of “low-carbon consumption and emission-reduced production”. A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry. |
| format | Article |
| id | doaj-art-4d9bd256a8ff417eb58948ce32a0f5ba |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-4d9bd256a8ff417eb58948ce32a0f5ba2025-08-20T03:14:39ZengMDPI AGBiomimetics2313-76732025-04-0110527310.3390/biomimetics10050273An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body PaintingQin Yang0Xinning Li1Teng Yang2Hu Wu3Liwen Zhang4School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Intelligent Manufacturing, Zibo Polytechnic University, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaResearch on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of “low-carbon consumption and emission-reduced production”. A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry.https://www.mdpi.com/2313-7673/10/5/273sprayingproductionalgorithmoptimization |
| spellingShingle | Qin Yang Xinning Li Teng Yang Hu Wu Liwen Zhang An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting Biomimetics spraying production algorithm optimization |
| title | An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting |
| title_full | An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting |
| title_fullStr | An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting |
| title_full_unstemmed | An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting |
| title_short | An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting |
| title_sort | improved whale optimization algorithm for the clean production transformation of automotive body painting |
| topic | spraying production algorithm optimization |
| url | https://www.mdpi.com/2313-7673/10/5/273 |
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