Enhanced CAD Integration Using Deep Learning

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.May 19 2024

In a paper published in the journal Scientific Reports, researchers enhanced computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) integration using deep learning (DL)-based automatic feature recognition (AFR) methods that outperformed traditional approaches for intersecting features. They addressed the loss of geometric and topological information in voxelized or point-cloud representations and the limitations of supervised learning in recognizing new features.

The team introduced the multidimensional attributed face-edge graph (maFEG) and Sheet-metalnet, a graph neural network for maFEGs. They proposed a three-component incremental learning strategy involving pre-training, prototype sampling-based replay, and knowledge distillation. It enabled Sheet-metalNet to improve feature recognition accuracy and adapt to evolving datasets.

Related Work

Past work on AFR methods evolved from rule-based approaches, which struggled with inefficiency and recognizing intersecting features, to learning-based methods using artificial neural networks (ANNs) and DL, which initially didn't outperform due to immature technology. Recent advances focused on graph-based AFR methods, with graph neural networks (GNNs) like hierarchical CADNet improving recognition rates but facing memory and adaptability issues. 

Proposed Methodology Overview

The methodology section delineates the proposed approach for enhancing AFR in CAD models. Initially, it explores the concept of representing CAD models in graph structures inspired by traditional graph-based AFR methods. While effective in some aspects, these methods face challenges such as computational complexity and difficulty handling intersecting features.

The study introduces Sheet-metalNet, a novel GNN designed to accurately predict machining feature classes for each face in a CAD model. This innovative approach integrates recent advancements in graph neural network technologies and leverages maFEGs to encapsulate both topological and geometric attributes of CAD models within the graph domain. By incorporating maFEGs, the proposed Sheet-metalNet achieves a richer representation of CAD models than traditional graph-based methods, facilitating more accurate feature recognition.

The methodology elaborates on the construction and attributes of maFEGs, which extend beyond the topological structure to include the geometric characteristics of CAD models. Attributes such as surface area, surface normal vector coordinates, and curvature information are assigned to nodes and edges in the graph, enriching the representation of each element. This comprehensive representation enables Sheet-metalNet to leverage broader features for machining feature identification. Additionally, normalization techniques are applied to ensure consistency and comparability across different attribute values within maFEGs, enhancing the model's effectiveness in processing diverse CAD data.

The methodology delves into the symbolic power of sheet-metalnet, elucidating its iterative node attribute update process based on a message-passing mechanism. Drawing inspiration from the Weisfeiler-Lehman graph isomorphism test, Sheet-metalNet adopts the graph isomorphism Network (GIN), incorporating injective aggregation functions to enhance discriminative power. Furthermore, integrating residual connections facilitates deeper network architectures, addressing challenges associated with vanishing gradients and over-smoothing in GNNs.

Finally, the methodology outlines the incremental learning strategy to adapt Sheet-metalNet to evolving datasets of real industrial part CAD models. This strategy encompasses pretraining and fine-tuning mechanisms, prototype sampling-based replay, and parameter regularization through knowledge distillation. By continuously learning from expanding datasets while retaining and optimizing old knowledge, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, ensuring robust performance in real-world industrial scenarios.

Sheet-metalNet Experimental Analysis

The experimental section provides insights into developing and evaluating Sheet-metalNet, a novel graph neural network designed for feature recognition in CAD models. Utilizing the PyTorch Geometric library, Sheet-metalNet's architecture comprises 13 residual blocks, with specific aggregation equations and optimization techniques employed.

Evaluation is conducted on the sheet metal CAD (SMCAD) and mechanical feature CAD (MFCAD++) datasets, showcasing Sheet-metalNet's superior accuracy, F1 score, and training efficiency compared to existing models. These results underscore Sheet-metalNet's potential for practical application in real-world industrial scenarios.

The methodology includes a detailed analysis of the attributes and mechanisms contributing to Sheet-metalNet's effectiveness. Ablation experiments on maFEG attribute vectors highlight the significance of attributes such as surface normal vectors and convexity/concavity information.

Additionally, the choice of aggregation function is crucial, with GIN demonstrating superior performance due to its ability to incorporate edge attribute information. Furthermore, introducing residual connections enhances network depth and performance, providing valuable insights into architectural modifications that improve Sheet-metalNet's performance.

The study explores incremental learning strategies to adapt Sheet-metalNet to evolving datasets of CAD models. Comparing various strategies against baseline approaches illustrates the effectiveness of techniques such as replay-based incremental learning and parameter regularization through knowledge distillation.

Strategies leveraging prototype sampling and fine-tuning demonstrate superior performance, effectively mitigating catastrophic forgetting and class imbalance issues. These findings underscore the importance of adaptive learning mechanisms in enhancing Sheet-metalNet's robustness and generalization capabilities in dynamic industrial environments.

Conclusion

In summary, this study introduced maFEG, a graph structure language, and Sheet-metalNet, a GNN, for accurately classifying machining features in CAD models. Comparative experiments demonstrated Sheet-metalNet's superior performance and efficiency over existing methods.

An incremental learning strategy was also proposed to adapt Sheet-metalNet to evolving datasets, although further improvements and real-world validation are needed. Future work will focus on refining recognition accuracy, integrating fuzzy logic-based decision-making, and collaborating with industry partners to enhance performance and applicability.