A deep learning approach to the texture optimization problem for friction control in lubricated contacts

TL;DR

This paper presents a deep learning method to efficiently optimize surface textures for friction control in lubricated contacts, significantly improving prediction accuracy and speed over traditional methods.

Contribution

The study introduces a deep neural network that accurately predicts Stribeck curves, enabling effective texture optimization for friction control in lubricated contacts.

Findings

Deep neural network predicts Stribeck curves with high accuracy

Texture optimization becomes more feasible and efficient using machine learning

Potential applications in wear reduction and improved grip in various industries

Abstract

The possibility to control friction through surface micro texturing could offer invaluable advantages in many fields, from wear and pollution reduction in the transportation industry to improved adhesion and grip. Unfortunately, the texture optimization problem is very hard to solve using traditional experimental and numerical methods, due to the complexity of the texture configuration space. In this work, we apply machine learning techniques to perform the texture optimization, by training a deep neural network to predict, with extremely high accuracy and speed, the Stribeck curve of a textured surface in lubricated contact. The deep neural network was used to completely resolve the mapping between textures and Stribeck curves, enabling a simple method to solve the texture optimization problem. This work demonstrates the potential of machine learning techniques in texture optimization for friction control in lubricated contacts.