LAGRANGE - triboLogical Analysis usinG aRtificiAl iNtelliGencE

• Grant number: 24/01785-6
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2024
• End date: May 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
• Principal Investigator: João Paulo Papa
• Grantee: João Paulo Papa
• Principal researcher abroad: Christian Greiner
• Institution abroad: Karlsruhe Institute of Technology (KIT) , Germany
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• City of the host institution: Bauru
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Tribology stands for a research area that studies the process of friction in mechanical components. Since friction demands more energy, exploring the elements' behavior when interacting with each other is crucial to minimize such losses. Despite their usage in many areas, machine learning techniques have also been considered to deal with tribological studies, ranging from condition monitoring for designing material compositions to lubricant formulations. However, most techniques have some contact with the materials involved to capture information, which may degrade the area of interest. In this proposal, we intend to use a contactless approach based on X-ray diffraction (XDR) images to capture information from the area involved in the friction process and use machine learning (ML) techniques to further correlate data with tribological parameters. We hypothesize that XDR and ML are powerful tools to advance the study of tribology, obtaining more precise correlations than approaches that must be in contact with the affected region. (AU)