Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

2016/12/07 by Aditya Balu, Sambit Ghadai, Balu, Aditya +9
Earth and Planetary Sciences · Engineering · #3D Surveying and Cultural Heritage #Advanced Numerical Analysis Techniques #Advanced machining processes and optimization #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1612.02141

openalex publication_date 2016/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to capture the distinguishing information about such local features. To enable efficient learning, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D gradient-weighted class activation maps. An application of this feature identification framework is to recognize difficult-to-manufacture drilled hole features in a complex CAD geometry. The framework can be extended to identify difficult-to-manufacture features at multiple spatial scales leading to a real-time decision support system for design for manufacturability.

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