Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main ste...

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Autores principales:	Omid Khalaj, Moslem Ghobadi, Ehsan Saebnoori, Alireza Zarezadeh, Mohammadreza Shishesaz, Bohuslav Mašek, Ctibor Štadler, Jiří Svoboda
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Oxide Precipitation-Hardened (OPH) alloys tensile test toughness artificial neural network (ANN) particle swarm optimization ANFIS Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/b0a1633d0b4c4ec6938a6845154e3dba
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spelling	oai:doaj.org-article:b0a1633d0b4c4ec6938a6845154e3dba2021-11-11T18:12:33ZDevelopment of Machine Learning Models to Evaluate the Toughness of OPH Alloys10.3390/ma142167131996-1944https://doaj.org/article/b0a1633d0b4c4ec6938a6845154e3dba2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6713https://doaj.org/toc/1996-1944Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.Omid KhalajMoslem GhobadiEhsan SaebnooriAlireza ZarezadehMohammadreza ShishesazBohuslav MašekCtibor ŠtadlerJiří SvobodaMDPI AGarticleOxide Precipitation-Hardened (OPH) alloystensile testtoughnessartificial neural network (ANN)particle swarm optimizationANFISTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6713, p 6713 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Oxide Precipitation-Hardened (OPH) alloys tensile test toughness artificial neural network (ANN) particle swarm optimization ANFIS Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	Oxide Precipitation-Hardened (OPH) alloys tensile test toughness artificial neural network (ANN) particle swarm optimization ANFIS Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Omid Khalaj Moslem Ghobadi Ehsan Saebnoori Alireza Zarezadeh Mohammadreza Shishesaz Bohuslav Mašek Ctibor Štadler Jiří Svoboda Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
description	Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.
format	article
author	Omid Khalaj Moslem Ghobadi Ehsan Saebnoori Alireza Zarezadeh Mohammadreza Shishesaz Bohuslav Mašek Ctibor Štadler Jiří Svoboda
author_facet	Omid Khalaj Moslem Ghobadi Ehsan Saebnoori Alireza Zarezadeh Mohammadreza Shishesaz Bohuslav Mašek Ctibor Štadler Jiří Svoboda
author_sort	Omid Khalaj
title	Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
title_short	Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
title_full	Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
title_fullStr	Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
title_full_unstemmed	Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys
title_sort	development of machine learning models to evaluate the toughness of oph alloys
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b0a1633d0b4c4ec6938a6845154e3dba
work_keys_str_mv	AT omidkhalaj developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT moslemghobadi developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT ehsansaebnoori developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT alirezazarezadeh developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT mohammadrezashishesaz developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT bohuslavmasek developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT ctiborstadler developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys AT jirisvoboda developmentofmachinelearningmodelstoevaluatethetoughnessofophalloys
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Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

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