Development of a Scalable Machining Feature Recognition System
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Date
2023-12-19
Authors
Lenover, Michael
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
In this thesis, various pre-processing and training techniques were applied to improve
the performance of a model trained with an existing machining feature recognition approach
by Yeo et al. using a smaller dataset that more effectively mimics the complexity of
CAD models used in industry.
A GUI tool was developed to tag faces in CAD models with the corresponding machining
features which would be necessary to resolve those faces. Using the encoding algorithm
outlined by Yeo et al., a tool was developed to generate feature vectors from tagged
CAD models. Two CAD datasets were compiled. First, a dataset of generic CAD models
was filtered from a larger dataset compiled by Koch et al., selecting those models which
could be manufactured using a 3-axis CNC machine. Second, a dataset of real-world CAD
files used in CNC manufacturing was compiled from models contributed by individuals
from the Unviersity of Waterloo, Hurco Inc. and Perfecto Inc.
Using the first dataset, three potential improvements to the feature recognition algo-
rithm developed by Yeo et al. were explored: the incorporation of dropout to improve model
stability and accuracy, the incorporation of ID3 tree pre-classification to reduce training
time by reducing the size of the deep learning dataset without impacting classification
accuracy, and the incorporation of crossover data generation to improve classification ac-
curacy by reducing overfitting due to insufficient training data. It was determined that
incorporating dropout improved the stability of the model and improved 5-fold cross val-
idation accuracy. Further, it was determined that incorporating a 2-deep ID3 decision
tree pre-classification marginally improved classification performance and was effective in
reducing the size of deep learning training dataset. Crossover data generation did not
improve model performance, and so was rejected. Using the model trained on the generic
CAD dataset, and incorporating 10% dropout and a 2-deep ID3 tree, models from the
real-world dataset were classified. This classifier was effective in classifying some simple
features, but had poor accuracy overall. To improve this accuracy, an incremental learning
technique was applied. The generic model was re-trained using samples from the real-world
dataset, which improved the classification accuracy of the system.
Description
Keywords
cnc, cam, machine learning, machining feature recognition