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Volume 12
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10.3390/met12040676
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Article

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

by
Nelly Abigaíl Rodríguez-Rosales
1,
Félix Alan Montes-González
1,
Oziel Gómez-Casas
2,
Josué Gómez-Casas
2,
Jesús Salvador Galindo-Valdés
2,
Juan Carlos Ortiz-Cuellar
2,
Jesús Fernando Martínez-Villafañe
2,
Daniel García-Navarro
1 and
Carlos Rodrigo Muñiz-Valdez
2,*
1
Departamento de Metal Mecánica, Tecnológico Nacional de México/I.T. Saltillo, Saltillo 25280, Coahuila, Mexico
2
Facultad de Ingeniería, Universidad Autónoma de Coahuila, Arteaga 25350, Coahuila, Mexico
*
Author to whom correspondence should be addressed.
Metals 2022, 12(4), 676; https://doi.org/10.3390/met12040676
Submission received: 3 March 2022 / Revised: 6 April 2022 / Accepted: 9 April 2022 / Published: 15 April 2022
(This article belongs to the Special Issue Manufacture, Mechanical Properties and Metallurgy of Metallic Biomaterials)
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Abstract

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%.
Keywords: austempered ductile iron; heat treatments; statistical model austempered ductile iron; heat treatments; statistical model

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MDPI and ACS Style

Rodríguez-Rosales, N.A.; Montes-González, F.A.; Gómez-Casas, O.; Gómez-Casas, J.; Galindo-Valdés, J.S.; Ortiz-Cuellar, J.C.; Martínez-Villafañe, J.F.; García-Navarro, D.; Muñiz-Valdez, C.R. Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons. Metals 2022, 12, 676. https://doi.org/10.3390/met12040676

AMA Style

Rodríguez-Rosales NA, Montes-González FA, Gómez-Casas O, Gómez-Casas J, Galindo-Valdés JS, Ortiz-Cuellar JC, Martínez-Villafañe JF, García-Navarro D, Muñiz-Valdez CR. Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons. Metals. 2022; 12(4):676. https://doi.org/10.3390/met12040676

Chicago/Turabian Style

Rodríguez-Rosales, Nelly Abigaíl, Félix Alan Montes-González, Oziel Gómez-Casas, Josué Gómez-Casas, Jesús Salvador Galindo-Valdés, Juan Carlos Ortiz-Cuellar, Jesús Fernando Martínez-Villafañe, Daniel García-Navarro, and Carlos Rodrigo Muñiz-Valdez. 2022. "Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons" Metals 12, no. 4: 676. https://doi.org/10.3390/met12040676

APA Style

Rodríguez-Rosales, N. A., Montes-González, F. A., Gómez-Casas, O., Gómez-Casas, J., Galindo-Valdés, J. S., Ortiz-Cuellar, J. C., Martínez-Villafañe, J. F., García-Navarro, D., & Muñiz-Valdez, C. R. (2022). Statistical Data-Driven Model for Hardness Prediction in Austempered Ductile Irons. Metals, 12(4), 676. https://doi.org/10.3390/met12040676

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