Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel

A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the beh...

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Autores principales:	Omid Khalaj, Mohammad Behdad Jamshidi, Ehsan Saebnoori, Bohuslav Masek, Ctibor Stadler, Jiri Svoboda
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Oxide precipitation hardened (OPH) steels tensile strength artificial neural network (ANN) particle swarm optimization ANFIS Fe–Al–O Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e68dfb339b0a490da84e5fb1834aa15d
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spelling	oai:doaj.org-article:e68dfb339b0a490da84e5fb1834aa15d2021-12-02T00:00:27ZHybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel2169-353610.1109/ACCESS.2021.3129454https://doaj.org/article/e68dfb339b0a490da84e5fb1834aa15d2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9620029/https://doaj.org/toc/2169-3536A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the behavior of such materials requires the consideration of a large number of variables, nonlinearities, and uncertainties in the analyses, and the modeling of such alloys by analytical methods is not accurate enough. Therefore, artificial intelligence (AI) methods, such as machine learning (ML), can be beneficial to alleviate the problems associated with the complexity of these alloys. In this work, three different hybrid ML techniques have been employed to estimate the ultimate tensile strength (UTS) and elongation in these special alloys. The proposed methods include a feedforward artificial neural network (FF-ANN) trained using particle swarm optimization (PSO) and two adaptive neuro-fuzzy inference system (ANFIS) methods trained using both fuzzy C-means (FCM) clustering and subtractive clustering (SC). Since OPH alloys are mainly produced via mechanical alloying (MA) of a mixture of powder components followed by consolidation and hot rolling, a series of standard tensile tests were performed on the different variants of the OPH alloy. In this way, some critical parameters such as UTS and elongation could be extracted from the experimental results. The main contribution of the present study is to estimate these important parameters based on some material properties including Aluminum (Al), Molybdenum (Mo), Iron (Fe), Chromium (Cr), Tantalum (Ta), Yttrium (Y) and Oxygen (O), MA and the heat treatment conditions. The results show that the proposed strategies are not only able to accurately determine the complex behavior of OPH alloy with an accuracy of about 95%, but they can also help the designer to benefit from these powerful tools to design new versions of such materials without analytical calculations.Omid KhalajMohammad Behdad JamshidiEhsan SaebnooriBohuslav MasekCtibor StadlerJiri SvobodaIEEEarticleOxide precipitation hardened (OPH) steelstensile strengthartificial neural network (ANN)particle swarm optimizationANFISFe–Al–OElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156930-156946 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Oxide precipitation hardened (OPH) steels tensile strength artificial neural network (ANN) particle swarm optimization ANFIS Fe–Al–O Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Oxide precipitation hardened (OPH) steels tensile strength artificial neural network (ANN) particle swarm optimization ANFIS Fe–Al–O Electrical engineering. Electronics. Nuclear engineering TK1-9971 Omid Khalaj Mohammad Behdad Jamshidi Ehsan Saebnoori Bohuslav Masek Ctibor Stadler Jiri Svoboda Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
description	A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the behavior of such materials requires the consideration of a large number of variables, nonlinearities, and uncertainties in the analyses, and the modeling of such alloys by analytical methods is not accurate enough. Therefore, artificial intelligence (AI) methods, such as machine learning (ML), can be beneficial to alleviate the problems associated with the complexity of these alloys. In this work, three different hybrid ML techniques have been employed to estimate the ultimate tensile strength (UTS) and elongation in these special alloys. The proposed methods include a feedforward artificial neural network (FF-ANN) trained using particle swarm optimization (PSO) and two adaptive neuro-fuzzy inference system (ANFIS) methods trained using both fuzzy C-means (FCM) clustering and subtractive clustering (SC). Since OPH alloys are mainly produced via mechanical alloying (MA) of a mixture of powder components followed by consolidation and hot rolling, a series of standard tensile tests were performed on the different variants of the OPH alloy. In this way, some critical parameters such as UTS and elongation could be extracted from the experimental results. The main contribution of the present study is to estimate these important parameters based on some material properties including Aluminum (Al), Molybdenum (Mo), Iron (Fe), Chromium (Cr), Tantalum (Ta), Yttrium (Y) and Oxygen (O), MA and the heat treatment conditions. The results show that the proposed strategies are not only able to accurately determine the complex behavior of OPH alloy with an accuracy of about 95%, but they can also help the designer to benefit from these powerful tools to design new versions of such materials without analytical calculations.
format	article
author	Omid Khalaj Mohammad Behdad Jamshidi Ehsan Saebnoori Bohuslav Masek Ctibor Stadler Jiri Svoboda
author_facet	Omid Khalaj Mohammad Behdad Jamshidi Ehsan Saebnoori Bohuslav Masek Ctibor Stadler Jiri Svoboda
author_sort	Omid Khalaj
title	Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
title_short	Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
title_full	Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
title_fullStr	Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
title_full_unstemmed	Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel
title_sort	hybrid machine learning techniques and computational mechanics: estimating the dynamic behavior of oxide precipitation hardened steel
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e68dfb339b0a490da84e5fb1834aa15d
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Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel

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