To facilitate the transition of Stereolithography (SLA) into the manufacturing domain and to increase its appeal to the micro manufacturing industry, process repeatability and surface finish need to be improved. Researchers have mostly focused on improving SLA surface finish within the capabilities of commercially available SLA machines. The capabilities of these machines are limited and a machine-specific approach for improving surface finish is based purely on empirical data. In order to improve surface finish of the SLA process, a more systematic approach that will incorporate process parameters is needed. To achieve this, the contribution of different laser and process parameters, such as laser beam angle, irradiance distribution, and scan speed, to SLA resolution and indirectly to surface finish, need to be quantified and incorporated into an analytical model.
In response, a dynamic analytical SLA cure model has been developed. This model has been applied to SLA geometries of interest. Using flat surfaces, the efficacy of the model has been computationally and experimentally demonstrated. The model has been applied to process planning as a computational inverse design method by using parameter estimation techniques, where surface finish improvement on slanted surfaces has been achieved. The efficacy of this model and its improvement over the traditional cure models has been demonstrated computationally and experimentally. Based on the experimental results, use of the analytical model in process planning achieves an order of magnitude improvement in surface roughness average of SLA parts. The intellectual contributions of this research are the development of an analytical SLA cure model and the application of this model to process planning along with inverse design techniques for parameter estimation and subsequent surface finish improvement.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/10527 |
| Date | 11 April 2006 |
| Creators | Sager, Benay |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 5495186 bytes, application/pdf |
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