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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/49777513:23210/21:43959715

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/GA17-01641S" target="_blank" >GA17-01641S: Zlepšení vlastností a komplexní charakterizace nové generace oxidy precipitačně vytvrzených ocelí na bázi Fe-Al-O</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Materials Today Communications

ISSN

2352-4928

e-ISSN

2352-4928

Svazek periodika

26

Číslo periodika v rámci svazku

MAR

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

101806

Kód UT WoS článku

000634320600004

EID výsledku v databázi Scopus

2-s2.0-85095758378