Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20500 - Materials engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMES - Computer Modeling in Engineering and Sciences
ISSN
1526-1492
e-ISSN
1526-1506
Volume of the periodical
142
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
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UT code for WoS article
001351048500001
EID of the result in the Scopus database
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