Construction of Pattern Optimization Model Driven by Fabric Parameters in 3D Garment Development Using Artificial Neural Networks

Fabric properties significantly influence the accuracy of pattern dimensions derived from 3D scanned garment samples. To enhance the generated pattern accuracy, a novel predictive model was proposed to estimate the pattern dimension change ratio by integrating fabric parameters using an artificial neural network (ANN). Thirty fabrics were tested for making flared skirts. The pattern generation involves 3D scanned garment samples, the Bowyer–Watson algorithm for surface reconstruction, and an energy model for surface development. After the pattern’s dimension change ratio was obtained, principal component analysis (PCA) was applied to reduce dimensionality before correlation analysis. Results indicated that thickness, bending rigidity, drapability, and shear performance were the primary fabric parameters influencing dimensional accuracy. Backpropagation (BP) neural networks were constructed to predict the pattern size change ratio using both full fabric parameters or a PCA-reduced set, including a 9-parameter input layer, four hidden layers, and a 12-parameter output layer. The BP ANN models outperformed the radial basis function (RBF) ANN models, achieving accuracies of 96.67% and 96.02% for the full-factor and dimension-reduced models, respectively. After parameter optimization, the dimension-reduced BP ANN model enhanced pattern accuracy by 5.11%, achieving a final 97.73% accuracy. Results validate utilizing fabric parameters and BP neural networks as a sophisticated pattern optimization method.