Development of machine learning models to evaluate the toughness of OPH alloys

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43963595" target="_blank" >RIV/49777513:23220/21:43963595 - isvavai.cz</a>

Alternative codes found

RIV/68081723:_____/21:00548681

Result on the web

<a href="https://www.mdpi.com/1996-1944/14/21/6713" target="_blank" >https://www.mdpi.com/1996-1944/14/21/6713</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma14216713" target="_blank" >10.3390/ma14216713</a>

Result language

angličtina

Original language name

Development of machine learning models to evaluate the toughness of OPH alloys

Original language 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GX21-02203X" target="_blank" >GX21-02203X: Beyond properties of current top performance alloys</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

—

Volume of the periodical

14

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000718800900001

EID of the result in the Scopus database

2-s2.0-85119253288