META

Large amounts of 3D data are being generated daily from increasingly diverse sources. In applications ranging from medical imaging and archival scanning of archaeological artifacts through to engineering and design, valuable geometric information is being recorded and stored. The size of these 3D databases make manual indexing time consuming, ineffective and often simply impracticable. Because of this researchers have started to develop tools for automatically searching and organising these digital libraries of shape information. The most widespread and valuable forms of 3D data are the design models created by commercial manufacturing companies.

Engineering companies commonly have tens of thousands of 3D Computer-Aided Design models stored on their computer systems. These models are used to communicate the exact shape and dimensions of components to both customers and sub-contracting manufacturers. Consequently these models are of great value and importance to the companies.

Currently 3D models (like engineering drawings) are indexed by alpha-numeric "part numbers" with a format unique to each company. Although this system of indexing works well in the context of ongoing maintenance and development of individual parts, it offers little scope for "data mining" (i.e. exploration) of a company's inventory of designs.

The last five years have seen increasing academic research into 3D shape retrieval methods. The focus has largely been on models used in 3D graphics and animation. The work at DTMG focuses on the specific requirements of engineering models.

Project: Part-Sourcing in a Global Market

Description:

This research project aimed to investigate the feasibility of using a Internet-based sourcing system of mechanical components for automatically assessing the geometric similarity of 3D models to facilitate the re-use of existing production capability (i.e. tooling) and the identification of potential sub-contractors.

Web page: http://www.shapesearch.net/index.html

Project: 3D Data mining with Neural Networks

Description:

The broad goal of the research is to investigate computational methods for enabling internal company reuse of existing 3D product data and external sourcing of production expertise. Specifically the project's aim is to test if a combination of ANN and filtered shape distribution can be used to locate specific patterns of geometry, and to understand the limitations of the technique.