Residual stress prediction on laser powder bed fusion manufacturing

• Grant number: 23/09737-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: March 17, 2024
• End date: September 16, 2024
• Field of knowledge: Engineering - Mechanical Engineering - Manufacturing Processes
• Principal Investigator: Erik Gustavo Del Conte
• Grantee: Amanda Rossi de Oliveira
• Supervisor: Sylvain Lavernhe
• Host Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Institution abroad: Université Paris-Saclay, France
• Associated to the scholarship: 21/09890-5 - Modeling of roughness and surface residual stress behavior of maraging steel manufactured by powder bed fusion and post-processed with milling, BP.DR

Abstract

The laser powder bed fusion (L-PBF) technology deals with expressive efforts to improve the process predictability of key final properties, like residual stress. The presence of multiple influencing factors makes it more challenging but also gives the opportunity of improving interest properties. The effects of expressively large temperature gradients observed in the L-PBF process are expected to strongly influence the residual stresses because of the experienced thermal cycles. Modeling this correlation can demand exhausting computational capacity depending on the approach, so it is essential to investigate simpler strategies that are enough to represent the real-world thermal influences on experimentally validated residual stresses. This way, the proposed study aims to increment a flash-based thermal simulation with thermal dissipation differences between powder and solidified material for residual stress distribution prediction on Laser Powder Bed Fusion. A finite element analysis (FEA) will be developed for validation, as well as experimental measurements of melt pool metrics. Finally, residual stress will be systematically measured for correlation with the simulated temperature gradients to predict this property distribution within a layer. As a result, it is expected a contribution to understanding the mechanism of residual stress distribution generation due to the coupling of experiments and simulations. (AU)