Manufacturers of cast metal parts are interested in the development of a feedback control system for use with the Precision Sand-Casting (PSC) process. As industry demands the ability to cast more complex geometries, there are a variety of challenges that engineers have to address. Certain characteristics of the mold, such as thick-to-thin transitions, extensive horizontal or flat surfaces, and sharp corners increase the likelihood of generating defective casts due to the turbulent metal-flow during fills. Consequently, it is critical that turbulent flow behavior within the mold be minimized as much as possible. One way to enhance the quality of the fill process is to adjust the flow rate of the molten metal as it fills these critical regions of the mold. Existing systems attempt to predict the position of the metal level based on elapsed time from the beginning of the fill stage. Unfortunately, variability in several aspects of the fill process makes it very difficult to consistently predict the position of the metal front. A better approach would be to embed a sensor that can detect the melt through a lift-off distance and determine the position of the metal-front. The information from this sensor can then be used to adjust the flow rate of the aluminum as the mold is filled. This thesis presents the design of a novel non-invasive sensor monitoring system. When deployed on the factory floor, the sensing system will provide all necessary information to allow process engineers to adjust the metal flow-rate within the mold and thereby reduce the amount of scrap being produced. Moreover, the system will exhibit additional value in the research and development of future mold designs.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-1049 |
| Date | 10 January 2009 |
| Creators | Foley, Brian M. |
| Contributors | Reinhold Ludwig, Advisor, Scott W. Biederman, Committee Member, Hossein Hakim, Committee Member |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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