基于神经网络的车辆悬架运动学与顺应性验证及转向运动学优化

IF 0.6 4区 工程技术 Q4 MECHANICS
Mechanika Pub Date : 2023-06-17 DOI:10.5755/j02.mech.31983
Gurur Ağaki̇şi̇, F. Öztürk
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引用次数: 0

摘要

通过寻找最佳变量,如硬点位置或衬套刚度,进行物理和虚拟K&C分析,以实现车辆动力学目标。然而,找到满足所有目标的适当设计变量是一项挑战。本文基于一辆参考紧凑型原型车,通过实验、神经网络和遗传算法的设计,评估了一种实现悬架K&C特性目标的硬点优化方法。提供MBD模型相关性来优化难点,以改善车辆在基线悬架中超出目标的Ackerman误差和外倾角变化的转向运动学。结果表明,在有限的设计空间内,与传统的响应面方法相比,基于神经网络的硬点定义优化策略显著改善了目标特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kinematics & Compliance Validation of a Vehicle Suspension and Steering Kinematics Optimization Using Neural Networks
Physical and virtual K&C analyses are performed to achieve the vehicle dynamics targets by finding the optimum variables such as the position of hardpoints or stiffnesses of bushings. However, finding appropriate design variables that meet all the aims is challenging. This paper evaluates a hardpoint optimization approach to attain suspension K&C characteristic objectives with the design of experiments, neural networks, and genetic algorithm, based on a reference compact-sized prototype vehicle. The MBD model correlation is provided to optimize the hardpoints to improve the vehicle's steering kinematics concerning Ackerman error and camber angle variation that are out of target in baseline suspension. The results showed that NN based optimization strategy to define the hardpoints has significantly improved targeted characteristics compared to conventional response surface methods in the limited design space.
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来源期刊
Mechanika
Mechanika 物理-力学
CiteScore
1.30
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: The journal is publishing scientific papers dealing with the following problems: Mechanics of Solid Bodies; Mechanics of Fluids and Gases; Dynamics of Mechanical Systems; Design and Optimization of Mechanical Systems; Mechanical Technologies.
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