基于深度学习方法的含间隙铰链多刚体系统的动力学建模与分析

doi:10.3901/JME.2022.09.136

机械工程学报 ›› 2022, Vol. 58 ›› Issue (9): 136-146.doi: 10.3901/JME.2022.09.136

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基于深度学习方法的含间隙铰链多刚体系统的动力学建模与分析

刘臻^1,2, 胡三宝^1,2, 胡军华³, 向超³

1. 武汉理工大学现代汽车零部件技术湖北省重点实验室武汉 430070;
2. 武汉理工大学汽车零部件技术湖北省协同创新中心武汉 430070;
3. 武汉第二船舶设计研究所武汉 430064

收稿日期:2021-05-07 修回日期:2022-01-08 出版日期:2022-05-05 发布日期:2022-06-23
作者简介:刘臻，男，1997年出生。主要研究方向为机械系统动力学。E-mail：liuzhen_auto@whut.edu.cn;胡三宝，男，1979年出生，博士，副教授，硕士生研究导师。主要研究方向为汽车动力学及CAE分析。E-mail：husanbao@whut.edu.cn
基金资助:
国家自然科学基金资助项目(51305314)

Dynamic Modeling and Analysis of Rigid Multi-body System with Clearance Joints Based on Deep Learning

LIU Zhen^1,2, HU Sanbao^1,2, HU Junhua³, XIANG Chao³

1. Hubei Provincial Key Laboratory of Advanced Technology of Automotive Components, Wuhan University of Technology, Wuhan 430070;
2. Hubei Provincial Collaborative Center for Automotive Component Technology, Wuhan University of Technology, Wuhan 430070;
3. Wuhan Second Ship Design and Research Institute, Wuhan 430064

Received:2021-05-07 Revised:2022-01-08 Online:2022-05-05 Published:2022-06-23

摘要/Abstract

摘要： 机械系统中，铰接处接触力受铰间间隙量大小、运动副各部件的材料属性、运动过程中的接触状态等因素的影响，表现出很强的非线性。传统的间隙铰摩擦理论模型主要关注对摩擦现象描述的普适性，而难以精确地描述摩擦过程中摩擦力的非线性特征。基于物理样机试验获得的数据，使用深度学习方法建立了间隙铰非线性接触力神经网络模型，通过摩擦试验生成接触摩擦力数据集，结合旋转铰间隙接触碰撞力混合模型生成接触碰撞力数据集，对模型进行训练和测试，得到了旋转间隙铰的神经网络动力学模型。在此基础上，结合拉格朗日方程对含间隙铰的曲柄滑块机构进行建模，建立了“多刚体系统-间隙铰-多刚体系统”的动力学模型，通过仿真分析得到系统关键参数的动力学响应，并与物理试验结果进行了对比，验证了基于深度学习方法获得的间隙铰模型的正确性，为深度学习方法在非线性系统动力学建模方向上的应用提供了一个可行的思路。

关键词: 运动副间隙, 摩擦试验, 非线性接触力模型, 多刚体系统, 动力学, 深度学习

Abstract: The contact force of the joint in the mechanical system is strongly nonlinear, which is influenced by the amount of clearance in the joint, the material property of the parts of the kinematic pair and the contact state during movement. Traditional theoretical models of friction mainly focus on the universality of the description of friction phenomenon, but it is difficult to accurately describe the nonlinear characteristics of friction in the motion. Based on the data obtained from the physical prototype test, the neural network model of the nonlinear contact force of the clearance joint is established by using the deep learning method. The contact friction data set is generated by the friction experiment, and the contact impact force data set is generated by the mixed model of revolute hinge clearance. After training and testing, a neural network model describing the contact dynamics of revolute clearance hinge is obtained. Combined with Lagrange equation, a "rigid-body-to-clearance-to-rigid-body" dynamic model of a slider crank mechanism with clearance joint is established. The dynamic responses of the key parameters of the system are obtained through simulation and compared with the results of physical tests. The correctness of the clearance joint model based on deep learning method is verified, which provided a feasible idea for the application of deep learning method in the direction of nonlinear system dynamics modelling.

Key words: joint clearance, friction experiment, nonlinear contact force model, multi-body system, dynamics, deep learning

中图分类号:

TH113

刘臻, 胡三宝, 胡军华, 向超. 基于深度学习方法的含间隙铰链多刚体系统的动力学建模与分析[J]. 机械工程学报, 2022, 58(9): 136-146.

LIU Zhen, HU Sanbao, HU Junhua, XIANG Chao. Dynamic Modeling and Analysis of Rigid Multi-body System with Clearance Joints Based on Deep Learning[J]. Journal of Mechanical Engineering, 2022, 58(9): 136-146.

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基于深度学习方法的含间隙铰链多刚体系统的动力学建模与分析

Dynamic Modeling and Analysis of Rigid Multi-body System with Clearance Joints Based on Deep Learning

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