Title:	Development of machine learning models to evaluate the toughness of OPH alloys
Authors:	Khalaj, Omid Ghobadi, Moslem Saebnoori, Ehsan Zarezadeh, Alireza Shishesaz, Mohammadreza Mašek, Bohuslav Štádler, Ctibor Svoboda, Jiří
Citation:	KHALAJ, O. GHOBADI, M. SAEBNOORI, E. ZAREZADEH, A. SHISHESAZ, M. MAŠEK, B. ŠTÁDLER, C. SVOBODA, J. Development of machine learning models to evaluate the toughness of OPH alloys. Materials, 2021, roč. 14, č. 21, s. 1-14. ISSN: 1996-1944
Issue Date:	2021
Publisher:	MDPI
Document type:	článek article
URI:	2-s2.0-85119253288 http://hdl.handle.net/11025/46706
ISSN:	1996-1944
Keywords in different language:	oxide precipitation-hardened (OPH) alloys;tensile test;toughness;artificial neural network (ANN);particle swarm optimization;ANFIS;Fe-Al-O
Abstract in different language:	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.
Rights:	© authors
Appears in Collections:	Články / Articles (KEI) OBD

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