Potential role of machine learning techniques for modeling the hardness of OPH steels

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F21%3A00560556" target="_blank" >RIV/68081723:_____/21:00560556 - isvavai.cz</a>

Alternative codes found

RIV/49777513:23210/21:43959715

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2352492820328178?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352492820328178?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.mtcomm.2020.101806" target="_blank" >10.1016/j.mtcomm.2020.101806</a>

Result language

angličtina

Original language name

Potential role of machine learning techniques for modeling the hardness of OPH steels

Original language description

Oxide Precipitation Hardened (OPH) alloys are a new generation of Oxide Dispersion Strengthened (ODS) alloys which have outstanding mechanical properties based on using appropriate heat treatment (HT). The production consists of mechanical alloying, which leads to a ductile matrix and hard oxide dispersion, however, the initial state shows a fine grain structure and basic mechanical properties. The composition, production process parameters, and HT affect the hardness of the OPH. In order to obtain a better understanding of the hardness of OPH alloys, three machine learning techniques were developed using ANN, ANFIS and SVMR to simulate the hardness. Moreover, the importance and intensity of the impact of each parameter on the hardness of OPH alloys were discussed. Based on the experimental results achieved by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and Mechanical Alloying (MA) was used to train the models as inputs. The validity of the models was measured by different statistical criteria such as R, k, k', m, n, and Rm. The mean absolute error for prediction of the hardness values at the test set was about 32 HV (ANFIS model), 37 HV (ANN model), and 44 HV (SVMR model). The results demonstrated that the ANFIS model predicts more accurately than the other methods. The sensitivity analysis and the influence of valid parameters were studied for the ANFIS model. It was revealed that HT temperature has a great effect on the hardness of the OPH alloys.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

<a href="/en/project/GA17-01641S" target="_blank" >GA17-01641S: Improvement of Properties and Complex Characterization of New Generation Fe-Al-O Based Oxide Precipitation Hardened Steels</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Materials Today Communications

ISSN

2352-4928

e-ISSN

2352-4928

Volume of the periodical

26

Issue of the periodical within the volume

MAR

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

101806

UT code for WoS article

000634320600004

EID of the result in the Scopus database

2-s2.0-85095758378