Hierarchical-CADNet

This repo provides a code of the neural network described in the paper: Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition.

It is a deep learning approach to learn machining features from CAD models. To achieve this, the geometry of the CAD models are described by discretising the surface of the CAD model into a mesh. This mesh can then be treated as a graph and operated on by a graph neural network. The overall topology of the CAD model can be described by the face adjacency matrix. A hierarchical graph structure can be constructed by between the B-Rep adjacency graph and the mesh. A STL mesh was chosen as the tessellation method due to its wide availability in CAD system and offers a concise representation. Each facet in the mesh denotes a vertex in a level of the hierarchical graph. Each of these vertices contain information of the facet’s planar equation, used to describe the surface. A second level of the hierarchical graph denotes the B-Rep adjacency graph. There exists persistent links between each B-Rep face vertex and their corresponding STL facet vertex. A B-Rep face vertex can have more than one STL facets adjacent to it. The goal of the approach is to be able to classify the machining feature of each B-Rep face vertex in the graph.

Requirements

• Python >= 3.8.5
• Tensorflow >= 2.2.0
• h5py >= 1.10.6
• Numpy >= 1.19.1
• Scikit-learn >= 0.23.2

Instructions

• Generate hierarchical B-Rep graphs and batches using code in this repo: https://github.com/wadaniel/hierarchical-brep-graphs/tree/main
• Place hdf5 dataset files in /data folder.
• To train Hierarchical CADNet (Edge) which uses edge convexity information run train_edge.py, set data type, dataloader file locations.
• To train Hierarchical CADNet (Adj) which uses only adjacency information run train_adj.py, set data type, dataloader file locations.
• To train Hierarchical CADNet (Single) which is a graph classification task run train_single_feat.py, set data type, dataloader file locations.

Visualization

There is a basic CAD viewer provided. To use it additional Python packages are required (PythonOCC):

• pythonocc-core = 7.7.2 (more info here: https://github.com/tpaviot/pythonocc-core, https://github.com/tpaviot/pythonocc-core/releases/tag/7.7.2)
• occt = 7.7.2 (more info here: https://github.com/tpaviot/pythonocc-core, https://github.com/tpaviot/pythonocc-core/releases/tag/7.7.2)

To test a single CAD model with a trained network model and save a STEP file with the predicted labels, the test_and_save.py script can be used. A directory of STEP files can be viewed using the visualizer.py script, in which each label has a unique color.

Test Cases

In the Hierarchical CADNet paper, Section 6.4 discussed results on more complex test cases. The STEP files for these CAD models can be found in the test_cases directory. These are labelled in the same way as the MFCAD++ dataset, with a label id being attributed to each B-Rep face in the CAD model.

Citation

Please cite this work if used in your research:

@article{hierarchicalcadnet2022,
  Author = {Andrew R. Colligan, Trevor. T. Robinson, Declan C. Nolan, Yang Hua, Weijuan Cao},
  Journal = {Computer-Aided Design},
  Title = {Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition},
  Year = {2022}
  Volume = {147}
  URL = {https://www.sciencedirect.com/science/article/abs/pii/S0010448522000240}
}