A Machine Learning-Based Design Framework for Predicting the Minimum Laminate Configuration of Type IV Composite Overwrapped Pressure Vessels
Type IV composite hydrogen storage vessels must ensure structural safety under high internal pressure, and determining the optimal laminate configuration typically requires repetitive finite element analysis (FEA), leading to significant computational cost during the early design stage. To address this limitation, this study proposes a machine learning (ML)-based design-assistance framework for predicting the minimum laminate configuration required to satisfy structural safety based on the Tsai–Wu failure criterion. Three geometric design variables—liner radius, liner length, and polar hole radius—were used as inputs, and a dataset was generated using ANSYS Workbench-based FEA. Five ML models—SVR, GPR, RF, XGBoost, and MLP—were applied and evaluated using leave-one-out cross-validation (LOOCV) and an independent test set. All models achieved high predictive accuracy, with LOOCV R2 values ranging from 0.9801 to 0.9907 and test-set R2 values from 0.9866 to 0.9949, while maintaining MAPE below 6%. The consistent performance between LOOCV and the independent test set indicates stable predictive behavior. Learning curve analysis demonstrated stable convergence and a small performance gap between training and validation, suggesting stable predictive behavior within the considered design space. Feature importance analysis using the RF model identified the liner radius as the dominant parameter, while the influence of liner length was relatively minor. These results demonstrate that the proposed ML-based surrogate model can serve as an efficient tool for rapid design decision-making, reducing reliance on repetitive FEA in the early design stage.