Frame Optimization Design Based on Improved Grey Relational Analysis

doi:10.1007/s12204-023-2605-z

Abstract

Abstract: For effectively improving the overall performance of fire truck frame structure, and solving the complexity of previous methods in the frame optimization design process, the traditional grey relational grade ranking needs to be improved. First, the first-order modal test was conducted to verify the validity of the initial frame model. Then, based on this model, a high-strength steel frame was designed to reduce deformation, maximum stress, and frame mass, and increase the fatigue life and the frequencies of the first bending modal and first torsional modal. Sixty groups of sample points were generated through Hammersley method. Subsequently, improved grey relational analysis with principal component analysis was proposed to realize the optimal design of the frame structure. Finally, the optimal combination of design parameters for the frame was obtained using the proposed method. Meanwhile, the optimized frame structure is found by comparing the models before and after optimization, and the mass is reduced by 14.8%. Moreover, the computational cost can be reduced by 135% when the proposed method is compared with the previous algorithm. Therefore, the proposed method can effectively improve the performance of the frame and improve the computational efficiency.

Key words: fire truck, frame structure, optimization design, improved grey relational analysis, principal component analysis

摘要： 为有效提升消防车车架结构整体性能，解决传统车架结构优化设计过程中方法的复杂性，同时需要对传统的灰色关联度进行改进，因此，提出了主成分分析与改进的灰色关联分析相结合的方法，实现车架的优化设计。首先，基于一阶模态试验验证初始模型的有效性，在此基础上，设计出新款车架，以变形量、最大应力及车架质量的降低和疲劳寿命、一阶弯和一阶扭模态频率的提升为目标，通过哈默斯雷方法生成60组样本点。随后，提出改进的灰色关联分析与主成分分析集成来实现车架优化设计，最后，获取车架结构的设计参数最佳组合。同时，通过优化前后模型的比较，发现质量降低了14.8%，此外，将该方法与传统方法比较，发现计算成本降低了135%。因此，该方法在改善车架轻量化性能的同时提升了计算效率。

关键词: 消防车，车架结构，优化设计，改进的灰色关联分析，主成分分析

CLC Number:

U462
TH128

WANG Shuang^1∗ (王爽), WANG Dengfeng² (王登峰), NING Zhanjin¹(宁占金), HU Zhongjian¹(胡中建). Frame Optimization Design Based on Improved Grey Relational Analysis[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1071-1080.

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