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<p>This study addresses the challenge of predicting stress values in glass fibre reinforced epoxy composites under tensile loading using advanced AI methods. Traditional experimental and finite element analysis (FEA) approaches are time consuming and costly. To overcome this, a dataset was generated from FEA simulations covering different lamination sequences and material properties. Three AI models-narrow neural network (NNN), squared exponential Gaussian process regression (GPR) and support vector machine (SVM)-were developed and evaluated. GPR and SVM achieved superior prediction accuracies of 96.83 % and 95.04 %, respectively, outperforming NNN. Experimental validation confirmed these results and demonstrated the robustness of the proposed models. This study provides a cost-effective framework for stress prediction in composites that reduces the reliance on extensive testing and simulation, and advances AI-driven solutions for materials design and analysis.</p>

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Artificial intelligence-based stress prediction in glass fiber reinforced composites

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Kajs Ferati,
  • Nurettin Gökhan Adar

摘要

This study addresses the challenge of predicting stress values in glass fibre reinforced epoxy composites under tensile loading using advanced AI methods. Traditional experimental and finite element analysis (FEA) approaches are time consuming and costly. To overcome this, a dataset was generated from FEA simulations covering different lamination sequences and material properties. Three AI models-narrow neural network (NNN), squared exponential Gaussian process regression (GPR) and support vector machine (SVM)-were developed and evaluated. GPR and SVM achieved superior prediction accuracies of 96.83 % and 95.04 %, respectively, outperforming NNN. Experimental validation confirmed these results and demonstrated the robustness of the proposed models. This study provides a cost-effective framework for stress prediction in composites that reduces the reliance on extensive testing and simulation, and advances AI-driven solutions for materials design and analysis.