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Basic info

  • Title: Learning localized features in 3D CAD models for manufacturability analysis of drilled holes
  • Author: Sambit Ghadai, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy
  • Affiliation: Department of Mechanical Engineering, Iowa State University, IA, USA
  • Publication: Journel of Computer Aided Geometric Design
  • Short name: DLDFM
  • Year: 2018

Score

  • Idea: 3
  • Usability: 3
  • Presentation: 3
  • Overall: 3

Contributions

Problem addressed / Motivation

  • Parts or products need to meet its specifications, while still being able to be manufactured.
  • Feedback to a designer on a product's manufacturing feasibility is only given after the final design is created.
  • This leads to a iterative process, leading to longer product development times and increased costs.
  • Voxel-based occupancy grid representation do not have information on the surfaces of objects without additional processing.
  • Also not robust enough to capture information about the location, size or shape of a feature within an object.

Idea / Observation / Contribution

  • Feature identification framework for recognising difficult-to-manufacture drilled holes.
  • Local features of 3D voxelised CAD models are recognised using a 3D-CNN.
  • Normal information of surfaces also used in addition to volume occupancy information.
  • 3D-GradCAM (3D-CNN based gradient-weighted class activation mapping) is used to provide visually display the difficult-to-manufacture features outputed by the network.
  • GPU-accelerated methods for converting CAD models to volume representations (voxelisation augmented with surface normals).

Formulation / Solver / Implementation

  • DLDFM (Deep Learning Design For Manufacturing) framework, used the following DFM rules to create synthetic dataset:
    1. Depth-to-diameter ratio: The depth-to-diameter ratio should be less than 5.0 for the machinability of the hole.
    2. Through holes: Since a through hole can be drilled from both directions, the depth-to-diamter ratio for a through hole should be less than 10.0 to be manufacturable.
    3. Holes close to the edges: A manufacturable hole should be surrounded with material of thickness at least equal to the half the diamter of the hole.
    4. Thin sections in the depth direction of the hole: A manufacturable hole should have material greater than half the diamter along the depth direction.
  • To create voxel model, a grid of voxels in the region occupied by the object are constructed.

Evaluation

Dataset

  • Automatically generated cubes with single hole features in each, with a label of manufacturable or non-manufacturable.
  • 9531 CAD models (75% -> training, 25% -> validation)
  • 675 CAD models (testing)

Metrics

  • 64 x 64 x 64 voxel grid
  • Batch size = 64
  • Optimizer = Adadelta
  • Loss Function = Binary Cross-Entropy
  • CNN (2 Convolutional layers, 2 max pooling layer & 1 fully connected layer)

Results

Quantitative performance of the DLDFM on test data sets

Test Data Type Model Description True Positive True Negative False Positive False Negative Accuracy
675 models In-outs 391 90 17 176 0.7136
408 manufacturable In-outs + surface normals 334 201 74 65 0.7938

DLDFM detection of manufacturability

Resource

Project page

https://www.sciencedirect.com/science/article/pii/S0167839618300384

Source code

For the creation of voxelised GPU-accelerated models https://github.com/idealab-isu/GPView

Questions

  • Can surface normals be used with CSGNet paper for more information.

Build upon

  • Train additional networks for different manufacturing processes.

Paper connections

  • 3D-CNN

Equipment & Software

  • Kera with Tensorflow
  • 128GB CPU RAM
  • NVIDIA Titan X GPU with 12GB GPU RAM