Utilize este identificador para referenciar este registo: https://hdl.handle.net/10316/113619
Título: Deep Learning Approach to the Texture Optimization Problem for Friction Control in Lubricated Contacts
Autor: Silva, Alexandre
Lenzi, Veniero
Pyrlin, Sergey
Carvalho, Sandra 
Cavaleiro, Albano 
Marques, Luís
Data: 2023
Editora: American Physical Society
Projeto: UIDB/04650/2020 
Project No. PTDC/EME-SIS/30446/2017 
Título da revista, periódico, livro ou evento: Physical Review Applied
Volume: 19
Número: 5
Resumo: 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.
URI: https://hdl.handle.net/10316/113619
ISSN: 2331-7019
DOI: 10.1103/PhysRevApplied.19.054078
Direitos: openAccess
Aparece nas coleções:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais
I&D CEMMPRE - Artigos em Revistas Internacionais

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