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Optimizing the Transition: Replacing Conventional Lubricants with Biological Alternatives through Artificial Intelligence

dc.contributor.authorSoares, Gustavo [UNESP]
dc.contributor.authorChavarette, Fabio Roberto [UNESP]
dc.contributor.authorGoncalves, Aparecido Carlos [UNESP]
dc.contributor.authorFaria, Henrique Antonio Mendonca [UNESP]
dc.contributor.authorOuta, Roberto
dc.contributor.authorMishra, Vishnu Narayan
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionLins Coll Technol
dc.contributor.institutionIndira Gandhi Natl Tribal Univ
dc.date.accessioned2025-04-29T19:33:13Z
dc.date.issued2025-04-01
dc.description.abstractIn today's modern industry, artificial intelligence (AI) is revolutionizing the formulation, performance optimization, and monitoring of lubricants. By enabling the analysis of large datasets, AI facilitates the development of customized formulations and predictive maintenance strategies. Traditionally, synthetic lubricants have been widely used due to their superior performance characteristics; however, they pose significant environmental and health risks. In contrast, bio-based lubricants offer a sustainable and biodegradable alternative, aligning with growing environmental and health-conscious trends. This study aims to leverage AI to assess the feasibility of replacing conventional synthetic lubricants with bio-based lubricants in vibrating mechanical structures. By employing AI-driven analysis, the research investigates the performance characteristics of bio-greases compared to their synthetic counterparts, focusing on signal vibration responses. The findings demonstrate that AI can effectively optimize lubricant performance, reduce operational costs, and enhance sustainability in the lubricant industry. The present study underscores the critical importance of evaluating the differences between conventional commercial and bio-based lubricants in an innovative way through vibration signals, highlighting their potential applications across various industrial sectors. The integration of AI not only enhances performance and sustainability but also paves the way for innovative advancements in lubricant technology.en
dc.description.affiliationSao Paulo State Univ UNESP, Inst Chem, Dept Engn Phys & Math, Rua Prof Francisco Degni 55, BR-14800060 Araraquara, SP, Brazil
dc.description.affiliationSao Paulo State Univ UNESP, Dept Mech Engn, Brasil Sul 56, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationLins Coll Technol, Qual Management Dept, Estr Mario Covas Jr,Km 1, BR-16403025 Lins, SP, Brazil
dc.description.affiliationIndira Gandhi Natl Tribal Univ, Fac Sci, Dept Math, Amarkantak 484887, Madhya Pradesh, India
dc.description.affiliationUnespSao Paulo State Univ UNESP, Inst Chem, Dept Engn Phys & Math, Rua Prof Francisco Degni 55, BR-14800060 Araraquara, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Dept Mech Engn, Brasil Sul 56, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdCNPq: 301401/2022-5
dc.description.sponsorshipIdFAPESP: 2023/00861-8
dc.format.extent294-302
dc.identifierhttp://dx.doi.org/10.22055/jacm.2024.47162.4665
dc.identifier.citationJournal Of Applied And Computational Mechanics. Ahvaz: Shahid Chamran Univ Ahvaz, Iran, v. 11, n. 2, p. 294-302, 2025.
dc.identifier.doi10.22055/jacm.2024.47162.4665
dc.identifier.issn2383-4536
dc.identifier.urihttps://hdl.handle.net/11449/303851
dc.identifier.wosWOS:001454890400003
dc.language.isoeng
dc.publisherShahid Chamran Univ Ahvaz, Iran
dc.relation.ispartofJournal Of Applied And Computational Mechanics
dc.sourceWeb of Science
dc.subjectVibration
dc.subjectLubricants
dc.subjectArtificial Intelligence
dc.subjectArtificial Immune Systems
dc.subjectNegative Selection Algorithm
dc.titleOptimizing the Transition: Replacing Conventional Lubricants with Biological Alternatives through Artificial Intelligenceen
dc.typeArtigopt
dcterms.rightsHolderShahid Chamran Univ Ahvaz, Iran
dspace.entity.typePublication
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relation.isOrgUnitOfPublication85b724f4-c5d4-4984-9caf-8f0f0d076a19
relation.isOrgUnitOfPublication.latestForDiscoverybc74a1ce-4c4c-4dad-8378-83962d76c4fd
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Química, Araraquarapt
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Ilha Solteirapt

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