In the Manufacturing Industry there is a subset of technologies referred to as Rapid Technologies which are those technologies that create the ability to compress the time to market for new products under development . Of this subset, Additive Fabrication (AF), or more commonly known as Rapid Prototyping (RP), acquires much attention due to its unique and futuristic approach to the production of physical parts directly from 3D CAD data, CT or MRI scans, or data from laser scanning systems by utilizing various techniques to consecutively generate cross-sectional layers of a given thickness upon the previous layer to form 3D objects. While Rapid Prototyping is the most common name for the production technology it is also referred to as Additive Manufacturing, Layer Based Manufacturing, Direct Digital Manufacturing, Free-Form Fabrication, and 3-Dimensional Printing. With over 35 manufacturers of Additive Fabrication equipment in 2006 , the selection of an AF process and material for a specific application can become a significant task, especially for those with little or no technical experience with the technology and to add to this challenge, many of the various processes have multiple material options to select from . This research was carried out in order to design and construct a system that would allow a person, regardless of their level of technical knowledge, to quickly and easily filter through the large number of Additive Fabrication processes and their associated materials in order to find the most appropriate processes and material options to create physical reproductions of any part. The selection methodology used in this paper is a collection of assumptions and rules taken from the author's viewpoint of how, in real world terms, the selection process generally takes place between a consumer and a service provider. The methodology uses those assumptions in conjunction with a set of expert based rules to direct the user to a best set of qualifying processes and materials suited for their application based on as many or as few input fields the user may be able to complete.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5086 |
| Date | 01 January 2009 |
| Creators | Palmer, Andrew |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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