Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated, resulting in 912 sets of sample data. Voxel-based sampling and rasterization preprocessing were employed to prepare the data for model input. The CNN-SVM model was developed using TPMS type, lattice filling type, volume fraction, and cell length as input features, with wall thickness as the output variable. Subsequently, the MLR method was applied to quantify the influence weights of these parameters. Experimental results demonstrate that the CNN-SVM model achieves a mean squared error (MSE) of 0.0011 and a coefficient of determination (R2) of 0.92. Approximately 86.9% of the test samples exhibited prediction errors within 20%, representing performance improvements of 15.8%, 10.6%, and 18.5% over traditional MLR, CNN, and SVM models, respectively. The MLR analysis further indicates that the Sheet filling type exerts the most significant positive effect on wall thickness (0.45729), whereas the Diamond TPMS structure shows the most prominent negative impact (−0.23494). The proposed hybrid model provides an effective and reliable strategy for predicting wall thickness parameters in TPMS-based additive manufacturing designs.
Zhang et al. (Thu,) studied this question.