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Details of Grant 

EPSRC Reference: GR/S69085/01
Title: Detection of Design Intent in Complex Approximate Geometric Models
Principal Investigator: Langbein, Dr FC
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: Cardiff University
Scheme: First Grant Scheme Pre-FEC
Starts: 12 July 2004 Ends: 11 July 2007 Value (£): 123,639
EPSRC Research Topic Classifications:
Design & Testing Technology
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
The project aims to investigate methods for the detection of design intent in complex geometric models in terms of approximate geometric regularities. Many CAD applications like reverse engineering, and data exchange create approximate models which do not explicitly contain information about their design intent. By detecting design intent automatically the approximate models can be improved and applications can handle the models on a higher abstraction level for modification and analysis. As the regularities may only be approximately present in the model it is likely that a set of detected regularities contains many inconsistencies and it may represent ambiguous interpretations of the model. Creating a hierarchical decomposition of the model into meaningful sub-parts (e.g. sets of congruent features) and detecting appropriate regularities in the decomposition is likely to reduce the ambiguity by considering the topological structure of the model. In order to make consistent decisions about which regularities should be used to describe the design intent intelligent selection methods are required. For this relatively simple probability distribution based methods and more advanced neuro-fuzzy systems with a more transparent decision process and the ability to learn about likely regularity selections will be considered.
Key Findings
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Summary
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Project URL: http://www.langbein.org/research/solids/did
Further Information:  
Organisation Website: http://www.cf.ac.uk