Study on Application of Surrogate Models to Lightweight Design of Automobile Seat
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摘要: 针对汽车座椅传统轻量化方法存在计算周期长, 提出一种基于多种代理模型的汽车座椅轻量化设计方法。首先, 建立某乘用车座椅子系统碰撞台车试验的仿真模型, 并经台车试验验证。然后, 结合优化拉丁超立方试验设计, 以6个座椅部件厚度和3种材料类型为设计变量, 通过多种代理模型对样本点进行近似拟合。最后, 以质量最小、1阶频率最大为目标, 以假人最大下潜量、靠背转角为约束, 采用粒子群算法进行多目标优化。结果表明: 相比Kriging代理模型优化结果, 基于多种代理模型优化, 汽车座椅多减重0.74 kg, 提高了36%, 1阶模态频率提高1.39 Hz, 计算周期缩短80%。Abstract: In view of the long calculation cycle of traditional lightweight methods of automobile seat, a lightweight design method of automobile seats based on the surrogate models is proposed. Firstly, the model for a passenger automobile seat collision test is built and verified by the collision test. Then combined with the optimized Latin hypercube experimental design, the thickness of six seat parts and three material types were taken as the design variables, and the sample points were approximately fitted based on the surrogate models. Finally, with the minimum mass, the maximum frequency of the first order as the target, the maximum dive volume of the dummy and the backrest Angle as the constraint, the particle swarm optimization algorithm was used for multi-objective optimization. The results show that comparing with the optimization results of Kriging model, based on the optimal surrogate models, the automobile seat weight reduced by 0.74 kg and 36%, the first-order mode frequency increased by 1.39 Hz, and the calculation cycle shortened by 80%.
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Key words:
- automobile seat /
- surrogate model /
- lightweight
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表 1 座椅骨架的模态频率
Hz 座椅模态频率 1阶模态 2阶模态 3阶模态 试验 16.93 17.72 37.58 仿真 16.98 18.40 41.23 表 2 材料参数表
材料编号Ai 材料名称 泊松比 弹性模量/GPa 密度/(kg·m-3) A1 铝合金 0.35 45 1 840 A2 镁合金 0.33 72 2 720 A3 高强度钢 0.28 195 7 830 表 3 零件的初始厚度及其上下限
零件 初始厚度/mm 上限/mm 下限/mm 坐盆前后横管 2 2.5 1.5 靠背侧边板 1.5 2.0 1.0 坐垫加强板 3.5 4.0 3.0 滑轨连接板 2.5 3.0 2.0 坐盆侧边板 2.0 2.5 1.5 座椅滑轨 1.65 2.0 1.0 表 4 设计要求及目标
参数 初始值 目标 座椅骨架质量m/kg 18.39 最小 1阶模态频率F/Hz 16.98 最大 假人最大下潜量Z/mm 35.10 < 40 靠背最大转角R/(°) 27.34 < 25 表 5 多个代理模型
编号 代理模型 1 PRS1(一次多项式响应曲面模型) 2 KRG-Matern linear(通过改变相关函数得到3种不同的克里金模型) 3 KRG-Exponential 4 KRG-Gauss 5 RBF-Gauss(径向基函数选择高斯函数) 表 6 粒子群算法参数设置
参数名称 数值 最大迭代 50 粒子数 10 惯性系数 0.9 粒子增量 0.9 整体增量 0.9 表 7 迭代过程中为每个响应选择代理模型
迭代次数 R Z F m 1 2 4 4 5 2 2 4 4 4 3 5 5 4 5 4 3 3 4 3 5 2 2 2 5 6 2 4 4 5 表 8 优化方案
零件 优化厚度/mm 优化材料 坐盆前后横管 2.5 A2 靠背侧边板 1.8 A2 坐垫加强板 3.4 A2 滑轨连接板 2.8 A2 坐盆侧边板 2.0 A3 座椅滑轨 1.9 - 表 9 优化结果
参数 优化值 骨架质量m/kg 15.60 1阶频率F/Hz 18.32 假人最大下潜Z/mm 29.33 靠背最大转角R/(°) 23.33 表 10 Kriging近似模型确定性系数
参数 R2 m 0.98 F 0.91 Z 0.92 R 0.91 表 11 汽车座椅骨架的优化解及响应
设计变量 方案1 方案2 方案3 材料类型A1 3 3 2 材料类型A2 3 3 1 材料类型A3 3 2 1 材料类型A4 2 2 2 材料类型A5 3 3 3 厚度T1/mm 2.2 2.5 2.1 厚度T2/mm 1.3 1.6 1.7 厚度T3/mm 3.2 3.1 3.2 厚度T4/mm 2.8 2.6 2.9 厚度T5/mm 2.4 2.2 2.2 厚度T6/mm 2.0 1.8 1.8 1阶频率F/Hz 18.15 18.17 18.34 骨架质量m/kg 17.60 17.02 16.34 靠背最大转角R/(°) 24.56 25.19 23.66 假人最大下潜Z/mm 24.34 26.44 39.12 表 12 两种优化方案与原始模型对比
参数 迭代优化 Kriging优化 原始模型 骨架质量m/kg 15.60 16.34 18.39 1阶频率F/Hz 18.37 18.34 16.98 靠背最大转角R/(°) 23.33 23.66 27.34 假人最大下潜Z/mm 29.33 39.12 35.10 表 13 计算周期对比
方案 迭代优化 Kriging优化 样本点数量 33 150 计算时间/h 70~90 350~370 -
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