Seam recognition in CAD models via spatial curve convolutional neural networks

In CAD models, defect recognition arising from surface gaps is essential. Traditional methods, which rely on manual feature extraction and geometric distance thresholds, exhibit notable limitations in multi-scale adaptability and topological discrimination accuracy, making it difficult to meet the demands of complex industrial inspection. To address this issue, a data-driven approach integrating geometric deformation augmentation and deep feature learning is adopted, and a neural network-based gap recognition method using spatial curve convolution operators is proposed. First, a distance-preserving data augmentation algorithm based on radial basis functions was designed. By applying locally rigid transformation constraints, high-quality synthetic samples were generated from the original dataset, alleviating the model generalization bottleneck in small-sample training scenarios. Then, a network incorporating multi-level convolution modules and multi-head self-attention mechanisms was constructed. This network dynamically enhances the geometric salience of gap features and captures long-range dependencies, enabling end-to-end high-precision defect recognition. The original 371 manually constructed samples were expanded to 4 081, which were then randomly divided into training and test sets. The model was trained on a set of 2,860 samples, and after training, performance was evaluated on the remaining test set. Experimental results demonstrate that the proposed method achieves an accuracy of 97.87% and a mean Intersection over Union (mIoU) of 96.42%, with a total testing time of 0.26 s. Its overall performance is significantly superior to that of traditional rule-based methods. This approach provides an efficient and reliable technical solution for intelligent geometric cleaning of CAD models and can be extended to other complex geometric defect recognition scenarios.