Artificial neural network to predict the chemical compositiondependence of stacking fault energy in austenitic stainless steels

Authors

  • Alfonso M. Román Instituto de Ciencias Físicas, UNAM, México
  • Bernando Campillo Facultad de Química, UNAM, México
  • Arturo Molina Centro de Investigación en Ingeniería y Ciencias Aplicadas, UAEM, México.
  • Horacio Martínez Instituto de Ciencias Físicas, UNAM, México
  • Itzel Reyes Facultad de Química, UNAM, México
  • Osvaldo Flores Instituto de Ciencias Físicas, UNAM, México

DOI:

https://doi.org/10.30973/progmat/2020.12.2/7

Keywords:

Artificial Neural network, stacking fault energy, austenitic stainlesssteel

Abstract

Stacking fault energy (SFE) is an important parameter to be considered in the design of austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order changes and intergranular corrosion resistance. An extensive review of specialized literature was examined in order to understand the different methods that have been developed for the calculation of SFE. Characterization by transmission electron microscopy (TEM), linear expressions from data processing and first-principles quantum mechanics approximations are some techniques that have been used for the calculation of SFE. In the present work a feed forward backpropagation artificial neural network (ANN) was developed to predict the SFE within given specific ranges of chemical compositions for austenitic SS. The experimental data were extracted from a research work reported by Yonezawa et al [1], and then were analyzed for three different heat treatment conditions. The present model predicts SFE values with a correlation coefficient of 0.99, which is a minor error when is compared with other works in the literature.

Author Biographies

Alfonso M. Román, Instituto de Ciencias Físicas, UNAM, México

Ingeniero Mecánico, egresado de la Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma del Estado de Morelos. Actual estudiante de maestría en el Centro de Investigación en Ingeniería y Ciencias Aplicadas, UAEM. Algunas áreas de interés: ciencia de materiales, diseño de materiales protésicos, aplicación de tecnología aditiva al área biomecánica y energía e inteligencia artificial.

Bernando Campillo, Facultad de Química, UNAM, México

Dr. en química metalúrgica, egresado de la Universidad Nacional Autónoma de México. Actual investigador del Instituto de Ciencias Físicas y coordinador de posgrado de la Facultad de Química de la UNAM en el Instituto de Ciencias Físicas. Algunas áreas de interés: Ciencia de materiales, plasma a baja temperatura, materiales intermetálicos, tratamientos térmicos y técnicas espectroscópicas.

Arturo Molina, Centro de Investigación en Ingeniería y Ciencias Aplicadas, UAEM, México.

Dr. en ingeniería, egresado de la Universidad Nacional Autónoma de México. ProfesorInvestigador del Centro de Investigación en Ingeniería y Ciencias Aplicadas, UAEM. Áreas de interés: análisis de microestructuras, propiedades mecánicas, procesos termo-mecánicos, tratamientos térmicos, metalurgia de polvos y aleado mecánico.

Horacio Martínez, Instituto de Ciencias Físicas, UNAM, México

Dr. en física, egresado de la Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM). Investigador del Instituto de Ciencias Físicas, UNAM, jefe del laboratorio de espectroscopia. Algunas áreas de interés: fenómenos de plasma, plasma a bajas temperaturas, modificaciones de material protésico mediante plasma, y técnicas.

Itzel Reyes, Facultad de Química, UNAM, México

Ingeniera Química Metalúrgica, egresada de la Facultad de Química, Universidad, Nacional Autónoma de México. Profesora de la F.Q. (UNAM) y estudiante de maestría en Metalurgia. Algunas áreas de interés: Ciencia de materiales, técnicas de caracterización de materiales, (SEM, EBSD, TEM, Absorción atómica, AFM, XRD), materiales intermetálicos y corrosión.

Osvaldo Flores, Instituto de Ciencias Físicas, UNAM, México

Ing. Química Metalúrgica, egresado de la Universidad Nacional Autónoma de México. Actualmente en el Instituto de Ciencias Físicas y profesor de posgrado de la Facultad de Química de la UNAM. Algunas áreas de interés: Ciencia de materiales, plasma a baja temperatura, hidrogeno en metales, materiales intermetálicos, tratamientos térmicos, técnicas espectroscópicas, tecnología aditiva al área biomecánica y energía, y diseño de materiales protésicos.

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Published

2020-06-30

How to Cite

Román, A. M., Campillo, B., Molina, A., Martínez, H., Reyes, I., & Flores, O. (2020). Artificial neural network to predict the chemical compositiondependence of stacking fault energy in austenitic stainless steels . Programación Matemática Y Software, 12(2), 65–74. https://doi.org/10.30973/progmat/2020.12.2/7

Issue

Vol. 12 No. 2 (2020): June, 2020

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Copyright (c) 2020 Alfonso M. Román, Bernando Campillo, Arturo Molina, Horacio Martínez, Itzel Reyes, Osvaldo Flores

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