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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorLins Coll Technol-
Autor(es): dc.contributorIndira Gandhi Natl Tribal Univ-
Autor(es): dc.creatorSoares, Gustavo-
Autor(es): dc.creatorChavarette, Fabio Roberto-
Autor(es): dc.creatorGoncalves, Aparecido Carlos-
Autor(es): dc.creatorFaria, Henrique Antonio Mendonca-
Autor(es): dc.creatorOuta, Roberto-
Autor(es): dc.creatorMishra, Vishnu Narayan-
Data de aceite: dc.date.accessioned2025-08-21T18:02:44Z-
Data de disponibilização: dc.date.available2025-08-21T18:02:44Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.22055/jacm.2024.47162.4665-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303851-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303851-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Chem, Dept Engn Phys & Math, Rua Prof Francisco Degni 55, BR-14800060 Araraquara, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Dept Mech Engn, Brasil Sul 56, BR-15385000 Ilha Solteira, SP, Brazil-
Descrição: dc.descriptionLins Coll Technol, Qual Management Dept, Estr Mario Covas Jr,Km 1, BR-16403025 Lins, SP, Brazil-
Descrição: dc.descriptionIndira Gandhi Natl Tribal Univ, Fac Sci, Dept Math, Amarkantak 484887, Madhya Pradesh, India-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Chem, Dept Engn Phys & Math, Rua Prof Francisco Degni 55, BR-14800060 Araraquara, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Dept Mech Engn, Brasil Sul 56, BR-15385000 Ilha Solteira, SP, Brazil-
Descrição: dc.descriptionCNPq: 301401/2022-5-
Descrição: dc.descriptionFAPESP: 2023/00861-8-
Formato: dc.format294-302-
Idioma: dc.languageen-
Publicador: dc.publisherShahid Chamran Univ Ahvaz, Iran-
Relação: dc.relationJournal Of Applied And Computational Mechanics-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectVibration-
Palavras-chave: dc.subjectLubricants-
Palavras-chave: dc.subjectArtificial Intelligence-
Palavras-chave: dc.subjectArtificial Immune Systems-
Palavras-chave: dc.subjectNegative Selection Algorithm-
Título: dc.titleOptimizing the Transition: Replacing Conventional Lubricants with Biological Alternatives through Artificial Intelligence-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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