Friction modelling and the use of a physics-informed neural network for estimating frictional torque characteristics

Abstract

This paper presents an exploration of friction modeling encompassing theoretical and practical aspects, utilizing a planar or 2D contact system. Various white-box friction models, including static and dynamic variants, are introduced, highlighting the superior capability of dynamic models in comprehensively capturing friction effects, substantiated through numerical simulation. Practical aspects of friction measurement and data-driven friction modeling are elucidated. The discourse extends to the development of grey-box and black-box friction models. A significant contribution lies in the proposition of a physics-informed neural network-based friction modeling approach, presenting it as an advanced and preferable alternative for friction estimation. To exemplify its efficacy, a case study of a torsion-based frictional contact scenario, employing Physics-Informed Neural Network (PINN) and the Nelder–Mead (N–M) algorithm for concurrent dynamics and friction model identification, was examined. Experimental data from a double torsion pendulum system, characterized by discontinuous dynamics, is employed for training. Results demonstrate the PINN’s superiority, providing more accurate representation of stick–slip phases at the contact zone and exhibiting faster performance compared to the N–M algorithm. The paper concludes by deliberating on challenges, prospects, and future directions in friction modeling.

Simulation of spring-friction set of freight car truck, taking into account track profile

“…In order to solve the set applied task, we will use the most important provisions of theoretical mechanics: the principle of freedom from ties, the condition of balance of forces in statics, Coulomb's Law, and the axiom of equality of actio n a n d counteraction. Let us show, for example, matrix, symbolic, and numerical solution to the problem in the MathCAD environment [13,14].…”

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

“…The physics-informed neural network (PINN) combines deep learning with physical models and has been applied in many fields such as fluid mechanics [20,21], solid mechanics [22,23], and heat transfer [24,25]. The PINN is gradually developing in the field of tribology [26][27][28][29][30][31]. The PINN utilizes the robust fitting capacity of neural networks to represent physical variables within the constraints of governing equations, boundary conditions, and initial conditions.…”