MACHINE LEARNING-BASED PREDICTIVE MODELLING OF LAMINATED COMPOSITES

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259132" target="_blank" >RIV/61989100:27230/25:10259132 - isvavai.cz</a>
Result on the web
<a href="https://www.mmscience.eu/journal/issues/march-2025/articles" target="_blank" >https://www.mmscience.eu/journal/issues/march-2025/articles</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17973/MMSJ.2025_03_2025007" target="_blank" >10.17973/MMSJ.2025_03_2025007</a>

Result language
angličtina
Original language name
MACHINE LEARNING-BASED PREDICTIVE MODELLING OF LAMINATED COMPOSITES
Original language description
Laminated composite plates and shells are widely used in aerospace, marine, and automotive industries. Their structural response can be tuned by modifying the stacking sequence, but accurate modelling requires computationally expensive finite element (FE) analysis. This study develops machine learning-based predictive models as surrogates for FE analysis to predict the first natural frequency of laminated composites. Two problems are considered, a 2-variable low-dimensional (LD) problem and a 16-variable high-dimensional (HD) problem. Six machine learning models were trained and evaluated. For the LD problem, support vector regression (SVR) performed best (R² = 0.9972, MSE = 0.0097). For the HD problem, Gaussian process regression (GPR) outperformed others (R² = 1.000, MSE ≪ 0.0001), effectively handling complex nonlinearities. The results highlight SVR’s suitability for simpler cases and GPR’s superior predictive accuracy for high-dimensional design spaces.
Czech name
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Czech description
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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
20301 - Mechanical engineering

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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