Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90301
Título: | 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: | Abr-2023 |
Editora: | American Physical Society |
Revista: | Physical Review Applied |
Citação: | Silva, A., Lenzi, V., Pyrlin, S., Carvalho, S., Cavaleiro, A., & Marques, L. (2023, May 24). Deep Learning Approach to the Texture Optimization Problem for Friction Control in Lubricated Contacts. Physical Review Applied. American Physical Society (APS). http://doi.org/10.1103/physrevapplied.19.054078 |
Resumo(s): | 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. |
Tipo: | Artigo |
Descrição: | All the data used in this work is available free of charge from https://doi.org/10.34622/datarepositorium/MUVOJD |
URI: | https://hdl.handle.net/1822/90301 |
DOI: | 10.1103/PhysRevApplied.19.054078 |
ISSN: | 2331-7019 |
Versão da editora: | https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.19.054078 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CDF - FCT - Artigos/Papers (with refereeing) |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Manuscript_PRA_Latest_2.pdf | 1,25 MB | Adobe PDF | Ver/Abrir |