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

• Autor(es): Silva, Alexandre Daniel Mendonça Faria ; Lenzi, Veniero ; Pyrlin, Sergey ; Carvalho, S. ; Cavaleiro, Albano ; Marques, L.
• Data: 2023
• Identificador Persistente: https://hdl.handle.net/1822/90301
• Origem: RepositóriUM - Universidade do Minho
• Tipo de Documento: Artigo científico
• Idioma: Inglês
• Contribuidor(es): Universidade do Minho

Descrição

The possibility to control friction through surface microtexturing can 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. Here, 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 is 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.