  Manufacturing industries have widely adopted the reuse of machine parts as a
method to reduce costs and as a sustainable manufacturing practice.
Identification of reusable features from the design of the parts and finding
their similar features from the database is an important part of this process.
In this project, with the help of fully convolutional geometric features, we
are able to extract and learn the high level semantic features from CAD models
with inductive transfer learning. The extracted features are then compared with
that of other CAD models from the database using Frobenius norm and identical
features are retrieved. Later we passed the extracted features to a deep
convolutional neural network with a spatial pyramid pooling layer and the
performance of the feature retrieval increased significantly. It was evident
from the results that the model could effectively capture the geometrical
elements from machining features.
