Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F24%3A10256144" target="_blank" >RIV/61989100:27360/24:10256144 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85212550946&origin=resultslist" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85212550946&origin=resultslist</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmes.2024.055219" target="_blank" >10.32604/cmes.2024.055219</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

Popis výsledku v původním jazyce

In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression (GPR) are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel. Although the GPR method has not been used for such a regression task before, the results showed that its performance is the most favorable and practically unrivaled; neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Název v anglickém jazyce

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

Popis výsledku anglicky

In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression (GPR) are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel. Although the GPR method has not been used for such a regression task before, the results showed that its performance is the most favorable and practically unrivaled; neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20500 - Materials engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

CMES - Computer Modeling in Engineering and Sciences

ISSN

1526-1492

e-ISSN

1526-1506

Svazek periodika

142

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

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Kód UT WoS článku

001351048500001

EID výsledku v databázi Scopus

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