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3D convolutional neural network for machining feature recognition with gradient-based visual explanations from 3D CAD

Jinwon Lee1, Hyunoh Lee1, Duhwan Mun2

  • 1School of Mechanical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

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This study introduces a deep learning model for recognizing machining features in 3D CAD models, achieving 98.81% accuracy. The model also detects feature areas, enhancing manufacturing applications.

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Area of Science:

  • Manufacturing Engineering
  • Computer-Aided Design
  • Artificial Intelligence

Background:

  • Three-dimensional (3D) computer-aided design (CAD) models are central to product manufacturing, storing geometric and topological data.
  • Current 3D CAD models lack explicit machining feature information, limiting their direct use in manufacturing processes.

Purpose of the Study:

  • To develop a novel deep learning model for automated recognition of machining features directly from 3D CAD models.
  • To enable precise detection of machining feature areas within these models.

Main Methods:

  • A deep learning network with 12 layers was designed and trained on a custom dataset of single and multi-feature 3D models.
  • Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize and detect the areas of recognized machining features.

Main Results:

  • The deep learning model achieved a high classification accuracy of 98.81% for machining features on the generated datasets.
  • Grad-CAM successfully estimated the spatial extent of machining features within the 3D CAD models.

Conclusions:

  • The proposed deep learning approach effectively recognizes machining features from 3D CAD data.
  • This technology has significant potential for applications in 3D model simplification, computer-aided engineering, and mechanical part retrieval.