CPS: Medium: Collaborative Research: Srch3D: Efficient 3D Model Search via Online Manufacturing-specific Object Recognition and Automated Deep Learning-Based Design Classification
Lead PI:
Raheem Beyah
Co-Pi:
Abstract

Rapid growth in additive manufacturing (AM) has improved the accessibility, customizability and affordability of making products using personal printers. Designs can be developed by consumers, if they have enough knowledge of mechanical design and 3D modeling, or they can be obtained from third parties. However, the process of translating a design to a program that can be successfully executed by a 3D printer often requires specialized domain knowledge that many end-users currently lack. In the meantime, lots of objects, which may be very similar or identical to what the non-technical user aims to design and print, have been produced by experts in industry and, hence, millions of proven part designs already exist. This research aims to fill the above-mentioned gap by developing a theoretically sound and practically deployable, domain-specific online search engine, called Srch3D, for 3D models. Srch3D will provide the non-technical end-users with a user-friendly solution to efficiently search for their components in a large repository of existing proven part designs.

Raheem Beyah
Performance Period: 09/01/2019 - 08/31/2024
Institution: Georgia Tech Research Corporation
Sponsor: NSF
Award Number: 1931977