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Geometry-Based Machining Feature Retrieval with Inductive Transfer Learning

Publication Type : Book Chapter

Publisher : In Intelligent Data Engineering and Analytics

Source : In Intelligent Data Engineering and Analytics, pp. 31-39. Springer, Singapore, 2022.

Url : https://paperswithcode.com/paper/geometry-based-machining-feature-retrieval

Campus : Coimbatore

School : School of Engineering

Department : Center for Computational Engineering and Networking (CEN)

Year : 2022

Abstract : 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.

Cite this Research Publication : Kamal, N. S., H. B. Barathi Ganesh, V. V. Sajith Variyar, V. Sowmya, and K. P. Soman. "Geometry-Based Machining Feature Retrieval with Inductive Transfer Learning." In Intelligent Data Engineering and Analytics, pp. 31-39. Springer, Singapore, 2022.

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