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Article

Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel

1
Laboratoire Génie de Production, INP/ENIT, Université de Toulouse, 47 Av d’Azereix, F-65016 Tarbes, France
2
Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre Dame St. W., Montréal, QC H3C 1K3, Canada
*
Author to whom correspondence should be addressed.
Metals 2023, 13(3), 633; https://doi.org/10.3390/met13030633
Submission received: 10 February 2023 / Revised: 16 March 2023 / Accepted: 20 March 2023 / Published: 22 March 2023
(This article belongs to the Special Issue Hot Deformation of Metal and Alloys)

Abstract

In the present work, a critical analysis of the most-commonly used analytical models and recently introduced ANN-based models was performed to evaluate their predictive accuracy within and outside the experimental interval used to generate them. The high-temperature deformation behavior of a medium carbon steel was studied over a wide range of strains, strain rates, and temperatures using hot compression tests on a Gleeble-3800. The experimental flow curves were modeled using the Johnson–Cook, Modified-Zerilli–Armstrong, Hansel–Spittel, Arrhenius, and PTM models, as well as an ANN model. The mean absolute relative error and root-mean-squared error values were used to quantify the predictive accuracy of the models analyzed. The results indicated that the Johnson–Cook and Modified-Zerilli–Armstrong models had a significant error, while the Hansel–Spittel, PTM, and Arrhenius models were able to predict the behavior of this alloy. The ANN model showed excellent agreement between the predicted and experimental flow curves, with an error of less than 0.62%. To validate the performance, the ability to interpolate and extrapolate the experimental data was also tested. The Hansel–Spittel, PTM, and Arrhenius models showed good interpolation and extrapolation capabilities. However, the ANN model was the most-powerful of all the models.
Keywords: artificial neural network; constitutive flow law; analytical flow law; interpolation; extrapolation; Gleeble artificial neural network; constitutive flow law; analytical flow law; interpolation; extrapolation; Gleeble

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MDPI and ACS Style

Tize Mha, P.; Dhondapure, P.; Jahazi, M.; Tongne, A.; Pantalé, O. Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel. Metals 2023, 13, 633. https://doi.org/10.3390/met13030633

AMA Style

Tize Mha P, Dhondapure P, Jahazi M, Tongne A, Pantalé O. Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel. Metals. 2023; 13(3):633. https://doi.org/10.3390/met13030633

Chicago/Turabian Style

Tize Mha, Pierre, Prashant Dhondapure, Mohammad Jahazi, Amèvi Tongne, and Olivier Pantalé. 2023. "Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel" Metals 13, no. 3: 633. https://doi.org/10.3390/met13030633

APA Style

Tize Mha, P., Dhondapure, P., Jahazi, M., Tongne, A., & Pantalé, O. (2023). Interpolation and Extrapolation Performance Measurement of Analytical and ANN-Based Flow Laws for Hot Deformation Behavior of Medium Carbon Steel. Metals, 13(3), 633. https://doi.org/10.3390/met13030633

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