Digital Data Processing and Computational Design for Large Area Maskless Photopolymerization

By Rudraraju, Anirudh V.

Georgia Institute of Technology

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Abstract

Advisors: Suman Das, David Rosen, Yan Wang, John Halloran, Massimo Ruzzene, Suresh K. Sitaraman

Large Area Maskless Photopolymerization (LAMP) is a novel additive manufacturing technology currently being developed at Georgia Tech in collaboration with the University of Michigan at Ann Arbor and PCC Airfoils. It is intended for the fabrication of integrally cored ceramic molds for the investment casting of precision components such as high-pressure turbine blades. This dissertation addresses the digital data processing and computational design needs for this technology. Several data processing schemes like direct slicing, STL slicing, post-processing schemes like error checking, part placement and tiling etc. were developed in order to enable the basic functionality of the LAMP process. A detailed overview of these schemes and their implementation details are given in this dissertation. Several computational schemes to improve the quality and accuracy of parts produced through the LAMP process were also implemented. These include a novel volume deviation based adaptive slicing method to adaptively slice native CAD models, a gray scaling and dithering approach to reduce stair stepping effect on downward facing surfaces and a preliminary experimental study to characterize the side curing behavior of the LAMP photo-curable suspension for pre-build image compensation. The implementation details and a discussion of the results obtained using these schemes are given. A novel approach for addressing the "floating island" problem encountered in additive manufacturing was also developed. The need for supports specific to the kind of parts being built through LAMP is evaluated and a support generation strategy different from the previously reported approaches in the literature is presented. Finally, a few novel film cooling schemes that are extremely challenging to fabricate using existing manufacturing technologies but possible to fabricate using LAMP are chosen and analyzed for their cooling performance. It is shown that such novel schemes perform much better in cooling the blade surface than the conventionally implemented schemes and hence this final component of work gives a better appreciation of the impact LAMP technology has in disrupting the state of the art in turbine blade manufacturing and truly taking the blade designs to the next level.

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Researchers should cite this work as follows:

  • Rudraraju, Anirudh V. (2015), "Digital Data Processing and Computational Design for Large Area Maskless Photopolymerization," https://matin.gatech.edu/resources/2242.

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