Integration of material composition, microstructure, and mechanical properties with geometry information enables many product development activities, including design, analysis, and manufacturing. To address such needs, models of material composition have been integrated into CAD systems, creating systems called heterogeneous CAD modeling. In order to support the heterogeneous CAD system, extensive process-structure-property relationships have to be captured and integrated into current CAD system. A new method for reverse engineering of materials will be presented such that microstructure models can be constructed and used in the heterogeneous CAD system.
Reverse engineering of material consists of three parts: image analysis, structure-property-process relationship, and repository. In this research, an image processing method, which comprises the Radon transform and the wavelet transform, will be used in order to recognize geometric features from a microstructure image. Recognizing geometric features can be obtained by combinations of three techniques, masking, clustering, and high frequency component on wavelet transform, that are integrated with the Radon transform. Then, recognized geometric features can be used to construct an explicit geometric model of microstructure. The proposed work will provide an explicit mathematical method to recognize and to quantify microstructure features from an image. In addition, explicit geometric models of microstructure can be automatically constructed and utilized to get effective mechanical properties, establishing structure-property relationship of the material. In order to demonstrate this, polymer nano-composite sample and metal alloy sample will be used.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54438 |
| Date | 07 January 2016 |
| Creators | Jeong, Namin |
| Contributors | Rosen, David W. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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