Geometry Based Machining Feature Retrieval with Inductive Transfer Learning
This work addresses the need for efficient and accurate retrieval of similar machining features in manufacturing industries to reduce costs and support sustainable practices, representing an incremental improvement in domain-specific methods.
The paper tackled the problem of identifying reusable machining features from CAD models by using fully convolutional geometric features with inductive transfer learning, resulting in a significant performance increase in feature retrieval when combined with a deep convolutional neural network and spatial pyramid pooling layer.
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.