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Hard turning of 4142 alloy steel with grinded tools for special edge preparation

Grant number: 23/06179-4
Support Opportunities:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): September 01, 2023
Effective date (End): November 30, 2023
Field of knowledge:Engineering - Mechanical Engineering - Manufacturing Processes
Principal Investigator:João Vitor de Carvalho Fontes
Grantee:Mateus Keniti Nakashima Sinzato
Supervisor: Joel Bernand Rech
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Research place: École Nationale d’Ingénieurs de Saint-Étienne (ENISE), France  
Associated to the scholarship:22/01711-7 - An approach for optimization of the insert grinding process using artificial intelligence, BP.MS


The aim of this project is to study the residual stresses generated in the 4142 alloy steel after straight turning with customized tungsten carbides. The main intent of this proposal is to predict the residual stress generated on the machined workpiece surface, studying the insert geometries influence on the machining behavior and modeling the project by an initial artificial intelligence algorithm. As a suggestion for following research, it will create possible discussions and further studies on this topic, determining the capacity and importance of this analysis. To list the stakeholders involved in this project, Mateus Keniti (master degree student), Armando Antonialli (associate professor), João Fontes (assistant professor) and other staff at Federal University of São Carlos (UFSCar), Brazil; Joel Rech (full professor) and other staff at École Nationale d'Ingénieurs de Saint-Étienne from the École Centrale de Lyon. This project is expected to occur in the months of September, October, and November 2023. It may be divided into two parts: i. Measurement of residual stress after straight turning in the workpiece; ii. Analysis of the variation on the residual stresses produced and application of an Artificial Intelligence (AI) capable of determining the best geometry parameters for this study. (AU)

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