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An ever-increasing number of industries are adopting additive manufacturing (AM), also known as 3D printing, to their production lifecycles for manufacturing parts. A computer aided design (CAD) model is used to manufacture the part. The capability for efficient search and retrieval of the CAD models from the database has become an essential need for designers and users. However, traditional search techniques perform poorly in the context of searching CAD designs. In this paper, we propose Fourier Fingerprint Search (FFS), a retrieval framework for 3D models that deduces and leverages critical shape characteristics for search. FFS introduces a novel search methodology that incorporates these characteristics and uses two advanced matching techniques that operate at different granularities and take into account unique patterns associated with each design. In addition, FFS supports both exact and partial matching in order to provide helpful and robust search results for any scenario. We investigate a diverse set of features and enhancements for search that allows for high adaptability in all situations, such as dividing shapes into smaller parts, surface interpolation, and two different types of rotation. We evaluate FFS using the FabWave CAD dataset with approximately 3000 manufacturing models with different configurations. Our experimental results demonstrate the efficiency and high accuracy of our approach for both exact and partial matching, rendering FFS a powerful framework for CAD model search.

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