ENHANCING WELDING QUALITY THROUGH PREDICTIVE MODELLING — INSIGHTS FROM MACHINE LEARNING TECHNIQUES
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10256120" target="_blank" >RIV/61989100:27230/25:10256120 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001372371000001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001372371000001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17973/MMSJ.2024_12_2024124" target="_blank" >10.17973/MMSJ.2024_12_2024124</a>
Alternative languages
Result language
angličtina
Original language name
ENHANCING WELDING QUALITY THROUGH PREDICTIVE MODELLING — INSIGHTS FROM MACHINE LEARNING TECHNIQUES
Original language description
In this work, the application of various machine learning (ML) algorithms for predicting tensile strength based on welding parameters in AA2014-T6 aluminium alloy joints is studied. Six ML models namely linear regression, AdaBoost regression, random forest regression, support vector regression (SVR), multi-layer perceptron regression and gaussian process regression (GPR) are considered. The comprehensive analysis revealed that SVR exhibited superior generalization capabilities on unseen data, achieving an R2 of 0.89 and a low RMSE of 15.64. In contrast, GPR, despite its high training accuracy, showed significant overfitting. This work highlights the potential of ML in optimizing welding parameters and highlights the importance of model selection and tuning to prevent overfitting and ensure reliable predictions.
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
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MM Science Journal
ISSN
1803-1269
e-ISSN
1805-0476
Volume of the periodical
2024
Issue of the periodical within the volume
December
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
6
Pages from-to
7897-7902
UT code for WoS article
001372371000001
EID of the result in the Scopus database
2-s2.0-85212482418