Metastable eutectoid transformation in spheroidal graphite cast iron: Modeling and validation

This paper presents a new microstructural model of the metastable eutectoid transformation in spheroidal graphite cast irons. The model takes into account the nucleation and growth of pearlite nodules. The nucleation is assumed to be continuous and dependent on the metastable undercooling associated...

Descripción completa

Detalles Bibliográficos
Autores: Carazo, Fernando Diego, García, Laura Noel, Celentano, Diego Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/156761
Acceso en línea:http://hdl.handle.net/11336/156761
Access Level:acceso abierto
Palabra clave:SPHEROIDAL CAST IRON
METASTABLE EUTECTOID TRANSFORMATION
THERMO-METALLURGICAL MODELING
https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
Descripción
Sumario:This paper presents a new microstructural model of the metastable eutectoid transformation in spheroidal graphite cast irons. The model takes into account the nucleation and growth of pearlite nodules. The nucleation is assumed to be continuous and dependent on the metastable undercooling associated with the upper limit of the three-phase field, while the growth rate is considered to be ruled by the silicon partitioning between ferrite and cementite at the pearlite/austenite front. The initial conditions for the metastable transformation are obtained from a microstructural simulation of solidification, graphite growth, and stable eutectoid transformation. These microstructural models are coupled with the thermal balance solved at a macroscopic level via the finite element method. The experimental validation of the metastable eutectoid model achieved by comparison with measured values of ferrite, graphite, and pearlite fractions at the end of the cooling process demonstrates the sound predictive capabilities of the proposed model.