Learning localized features in 3D CAD models for manufacturability analysis of drilled holes

Sambit Ghadai, Iowa State University
Aditya Balu, Iowa State University
Soumik Sarkar, Iowa State University
Adarsh Krishnamurthy, Iowa State University

This is a manuscript of the article Ghadai, Sambit, Aditya Balu, Soumik Sarkar, and Adarsh Krishnamurthy. "Learning localized features in 3D CAD models for manufacturability analysis of drilled holes." Computer Aided Geometric Design (2018). DOI: 10.1016/j.cagd.2018.03.024. Posted with permission.


We present a novel feature identification framework to recognize difficult-to-manufacturedrilled holes in a complex CAD geometry using deep learning. Deep learning algorithms have been successfully used in object recognition, video analytics, image segmentation, etc. Specifically, 3D Convolutional Neural Networks (3D-CNNs) have been used for object recognition from 3D voxel data based on the external shape of an object. On the other hand, manufacturability of a component depends on local features more than the external shape. Learning these local features from a boundary representation (B-Rep) CAD model is challenging due to lack of volumetric information. In this paper, we learn local features from a voxelized representation of a CAD model and classify its manufacturability. Further, to enable effective learning of localized features, we augment the voxel data with surface normals of the object boundary. We train a 3D-CNN with this augmented data to identify local features and classify the manufacturability. However, this classification does not provide information about the source of non-manufacturability in a complex component. Therefore, we have developed a 3D-CNN based gradient-weighted class activation mapping (3D-GradCAM) method that can provide visual explanations of the local geometric features of interest within an object. Using 3D-GradCAM, our framework can identify difficult-to-manufacture features, which allows a designer to modify the component based on its manufacturability and thus improve the design process. We extend this framework to identify difficult-to-manufacture features in a realistic CAD model with multiple drilled holes, which can ultimately enable development of a real-time manufacturability decision support system.