1 May 1994 Approach to modeling the spray-forming process with artificial neural networks
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Abstract
In this study artificial neural networks were used to model the spray forming process. Networks were developed and trained using process parameter and product quality data collected from a series of five spray forming runs. Process parameters of time into run, melt temperature, and gas to metal ratio were used as inputs and the networks were trained to predict the corresponding values of exhaust gas temperature, preform surface roughness, and porosity in the product. These networks were then tested with actual and hypothetical data. The results of the study showed that the networks can determine relationships between process parameters and the end product quality. It was also shown that the networks can be used to predict the effect on product quality from changes in process parameters. Additional work is in progress to create a larger data set for training over a broader region of the operating envelope. The result of this ongoing work will provide greater reliability in network prediction.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Allen Matteson, M. Allen Matteson, R. D. Payne, R. D. Payne, Craig Madden, Craig Madden, A. L. Moran, A. L. Moran, } "Approach to modeling the spray-forming process with artificial neural networks", Proc. SPIE 2189, Smart Structures and Materials 1994: Smart Materials, (1 May 1994); doi: 10.1117/12.174079; https://doi.org/10.1117/12.174079
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