Numerical and experimental study of an industrial case for grain size evolution in bainitic steel in controlled hot forging and its influence on mechanical performance

• Autor(a) principal: Ivaniski, Thiago Marques
• Data de Publicação: 2022
• Outros Autores: Castro, Pedro José de, Rodrigues, Douglas Rodrigues de, Epp, Jérémy, Nunes, Rafael Menezes, Rocha, Alexandre da Silva
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/270789
• topic: Aço bainítico; Forjamento a quente; Microestrutura dos materiais; Granulometria; Hot Forging; Grain size; JMAK; Mechanical properties; Bainitic steel

Controlling the recrystallization is an important way to reach grain size refinement and outstanding strength and toughness on alloy metals. This study sets out the application and investigation of mathematical microstructure modeling of a newly designed bainitic steel for hot forging industrial applications. The macro-scale model was used to observe and predict the austenitic grain size behavior during the controlled forging of a gear. Arrhenius grain growth kinetic and recrystallization model for a new class of bainitic steel was established for the given strain rate ranges and temperatures. This model was calibrated through microscopic analysis and used to simulate the unpublished constants of low alloyed bainitic forging steel DIN 18MnCrSiMo6-4 microstructure module using DEFORM® commercial finite element code. The increased temperature due to the adiabatic effect was investigated by numerical analysis, demonstrating its influence on grain coarsening. Local tensile test and Charpy-V notch were compared at different industrial hot forging temperatures and local plastic strain. Changes in yield strength and ductility have demonstrated the grain size influence on the processing parameters. The employed numerical model was an efficient tool to predict and present an alternative path to develop robust industrial forging using semi-empirical models.