A Mechanistic-Based Data-Driven Approach for General Friction Modeling in Complex Mechanical System

Abstract

The effect of friction is widespread around us, and most important projects must consider the friction effect. To better depict the dynamic characteristics of multibody systems with friction, a series of friction models have been proposed by scholars. Due to the complex and changeable working conditions, the contact surface is uncertain, and characterizing the friction properties is a challenging problem. Therefore, in this work, a mechanistic-based data-driven (MBDD) approach is proposed to establish a general friction model. According to the generalization ability of deep neural networks, the proposed strategy can handle the friction in multibody systems with different contact surfaces. Moreover, the proposed mechanistic-based data-driven approach can utilize both numerical data and experimental data, so it can achieve small data for the dynamic behavior prediction of complex mechanical systems. Eventually, the numerical simulation is compared with the experimental test. The results show that the proposed strategy can predict the dynamic behavior of a complex multibody system well and can reflect many important friction phenomena, such as the Stribeck effect, stiction, and viscous friction.