Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/113619
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Silva, Alexandre | - |
dc.contributor.author | Lenzi, Veniero | - |
dc.contributor.author | Pyrlin, Sergey | - |
dc.contributor.author | Carvalho, Sandra | - |
dc.contributor.author | Cavaleiro, Albano | - |
dc.contributor.author | Marques, Luís | - |
dc.date.accessioned | 2024-02-23T10:27:27Z | - |
dc.date.available | 2024-02-23T10:27:27Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2331-7019 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/113619 | - |
dc.description.abstract | 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. | pt |
dc.language.iso | eng | pt |
dc.publisher | American Physical Society | pt |
dc.relation | UIDB/04650/2020 | pt |
dc.relation | Project No. PTDC/EME-SIS/30446/2017 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.title | Deep Learning Approach to the Texture Optimization Problem for Friction Control in Lubricated Contacts | pt |
dc.type | article | - |
degois.publication.firstPage | 054078 | pt |
degois.publication.issue | 5 | pt |
degois.publication.title | Physical Review Applied | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1103/PhysRevApplied.19.054078 | pt |
degois.publication.volume | 19 | pt |
dc.date.embargo | 2023-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Physics Center of Minho and Porto Universities | - |
crisitem.author.researchunit | CEMMPRE - Centre for Mechanical Engineering, Materials and Processes | - |
crisitem.author.researchunit | CEMMPRE - Centre for Mechanical Engineering, Materials and Processes | - |
crisitem.author.orcid | 0000-0002-3643-4973 | - |
crisitem.author.orcid | 0000-0001-8251-5099 | - |
Appears in Collections: | FCTUC Eng.Mecânica - Artigos em Revistas Internacionais I&D CEMMPRE - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Deep Learning Approach to the Texture Optimization Problem for Friction Control in Lubricated Contacts.pdf | 5.88 MB | Adobe PDF | View/Open |
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This item is licensed under a Creative Commons License