Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons

Metals

Nelly Abigaíl Rodríguez-Rosales¹  Félix Alan Montes-González¹  Daniel García-Navarro¹  Carlos Rodrigo Muñiz-Valdez²  Josué Gómez-Casas²  Juan Carlos Ortiz-Cuellar²  Jesús Fernando Martínez-Villafañe²  Oziel Gómez-Casas²  Jesús Salvador Galindo-Valdés² 

[1] Departamento de Metal Mecánica, Tecnológico Nacional de México/I.T. Saltillo, Saltillo 25280, Coahuila, Mexico;Facultad de Ingeniería, Universidad Autónoma de Coahuila, Arteaga 25350, Coahuila, Mexico;

关键词: austempered ductile iron;    heat treatments;    statistical model;   

DOI  :  10.3390/met12040676

来源: DOAJ

【 摘 要 】

This research evaluates the effect of temperature and time austempering on microstructural characteristics and hardness of ductile iron, validating the results by means of a statistical method for hardness prediction. Ductile iron was subjected to austenitization at 950 °C for 120 min and then to austempering heat treatment in a salt bath at temperatures of 290, 320, 350 and 380 °C for 30, 60, 90 and 120 min. By increasing austempering temperature, a higher content of carbon-rich austenite was obtained, and the morphology of the thin acicular ferrite needles produced at 290 °C turned completely feathery at 350 and 380 °C. A thickening of acicular ferrite needles was also observed as austempering time increased. An inversely proportional behavior of hardness values was thus obtained, which was validated through data analysis, statistical tools and a regression model taking temperature and time austempering as input variables and hardness as the output variable, which achieved a correlation among variables of about 97%. The proposal of a mathematical model for the prediction of hardness in austempered ductile iron represents a numerical approximation which validates the experimental results at 95.20%.

【 授权许可】

Unknown