This thesis involves the development of digital algorithms for the microstructural analysis of metallic deposits produced through the use of Scanning Laser Epitaxy (SLE). SLE is a new direct digital manufacturing (DDM) technique which allows for the creation of three dimensional nickel-based superalloy components using an incremental layering system. Using a bed of powder placed on an underlying substrate and a laser propagating a melt-pool across the sample, a layer of material can be added and through the careful control of SLE settings various microstructures can be created or extended from the substrate. To create parts that are within specified microstructure tolerances the ideal SLE settings must be located through experimental runs, with each material needing different operating parameters. This thesis focuses on improving the microstructural analysis by use of a program that tracks various features found in samples produced through the SLE technique and a data analysis program that provides greater insights into how the SLE settings influence the microstructure. Using this program the isolation of optimal SLE settings is faster while also providing greater insights into the process than is currently possible. The microstructure recognition program features three key aspects. The first evaluates major characteristics that typically arise during the SLE process; such as sample deformation, the aspects of a single crystal deposit, and the total deposit height. The second saves the data and all relevant test settings in a format that will allow for future analysis and comparison to other samples. Finally, it features a robust yet rapid execution so it may be used for entire runs of SLE samples, which can number up to 25, within a week. The program is designed for the types of microstructure found in CMSX-4 and Rene-80, specifically single crystal and equiaxed regions. The data fitting program uses optimally piecewise-fitted equations to find relationships between the SLE settings and the microstructure traits. The data is optimally piecewise fitted as the SLE process is a two-stage procedure, establishing then propagating the melt-pool across a sample, which creates distinct microstructure transitions. Using the information gathered, graphs provide a visual aid to better allow the experimenter to understand the process and a DOE is performed using sequential analysis; allowing the previously run samples to influence the future trials, reducing the amount of materials used while still providing great insight into the parameter field. Having access to the microstructure data across the entire sample and an advanced data fitting program that can accurately relate them to the SLE settings allows the program to track and optimize features that were never before possible.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/50134 |
| Date | 14 November 2012 |
| Creators | Gambone, Justin J. |
| Contributors | Das, Suman |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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