Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models
Abstract In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models base...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89720-8 |
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| author | Xuan Zhou Renjie Zou Xigui Xie Yaoqing Liao |
| author_facet | Xuan Zhou Renjie Zou Xigui Xie Yaoqing Liao |
| author_sort | Xuan Zhou |
| collection | DOAJ |
| description | Abstract In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models based on the Multi-Species Approximation Model (HAM-MSAM) is proposed to meet the requirement for high fitting accuracy. Subsequently, this study introduces a HAM-MSAM-based Approximation-Based Global Multi-Objective Optimization Design (ABGMOOD) strategy. This strategy is employed in the multi-objective optimization of the rear seat of a passenger car. HAM-MSAM was constructed from an experimentally validated finite element model and a training set generated through experimental design. The advantages of HAM-MSAM in capturing the highly nonlinear response under seat crash conditions were validated through comparison with hybrid model construction methods reported in existing literature. Finally, the optimization results obtained by the ABGMOOD strategy were compared to those of the classical local multi-objective optimization strategy, demonstrating the substantial advantages of the ABGMOOD optimization scheme in economy and weight reduction. In addition, the safety of the rear seats is slightly lower than that of the local optimization scheme but remains in compliance with regulatory requirements. The final optimized rear seat demonstrates notable improvements in safety, economy, and weight reduction, validating the feasibility of the ABGMOOD strategy and providing valuable insights for similar engineering optimization challenges. |
| format | Article |
| id | doaj-art-3c0a8ed119c548a381faed5089de3dd2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3c0a8ed119c548a381faed5089de3dd22025-08-20T02:48:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111710.1038/s41598-025-89720-8Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation modelsXuan Zhou0Renjie Zou1Xigui Xie2Yaoqing Liao3College of Mechanical and Electrical Engineering, Wenzhou UniversityCollege of Mechanical and Electrical Engineering, Wenzhou UniversityZhejiang College of Security TechnologyZhejiang College of Security TechnologyAbstract In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models based on the Multi-Species Approximation Model (HAM-MSAM) is proposed to meet the requirement for high fitting accuracy. Subsequently, this study introduces a HAM-MSAM-based Approximation-Based Global Multi-Objective Optimization Design (ABGMOOD) strategy. This strategy is employed in the multi-objective optimization of the rear seat of a passenger car. HAM-MSAM was constructed from an experimentally validated finite element model and a training set generated through experimental design. The advantages of HAM-MSAM in capturing the highly nonlinear response under seat crash conditions were validated through comparison with hybrid model construction methods reported in existing literature. Finally, the optimization results obtained by the ABGMOOD strategy were compared to those of the classical local multi-objective optimization strategy, demonstrating the substantial advantages of the ABGMOOD optimization scheme in economy and weight reduction. In addition, the safety of the rear seats is slightly lower than that of the local optimization scheme but remains in compliance with regulatory requirements. The final optimized rear seat demonstrates notable improvements in safety, economy, and weight reduction, validating the feasibility of the ABGMOOD strategy and providing valuable insights for similar engineering optimization challenges.https://doi.org/10.1038/s41598-025-89720-8Approximation-based design optimizationHybrid approximation modelApproximation-based global multi-objective optimization designMulti-objective optimizationRear seat |
| spellingShingle | Xuan Zhou Renjie Zou Xigui Xie Yaoqing Liao Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models Scientific Reports Approximation-based design optimization Hybrid approximation model Approximation-based global multi-objective optimization design Multi-objective optimization Rear seat |
| title | Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models |
| title_full | Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models |
| title_fullStr | Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models |
| title_full_unstemmed | Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models |
| title_short | Enhanced predictive modeling framework for multi-objective global optimization of passenger car rear seat using hybrid approximation models |
| title_sort | enhanced predictive modeling framework for multi objective global optimization of passenger car rear seat using hybrid approximation models |
| topic | Approximation-based design optimization Hybrid approximation model Approximation-based global multi-objective optimization design Multi-objective optimization Rear seat |
| url | https://doi.org/10.1038/s41598-025-89720-8 |
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