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- W744985991 abstract "Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent." @default.
- W744985991 created "2016-06-24" @default.
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- W744985991 date "2007-10-29" @default.
- W744985991 modified "2023-09-27" @default.
- W744985991 title "NEURAL NETWORK BURST PRESSURE PREDICTION IN COMPOSITE OVERWRAPPED PRESSURE VESSELS" @default.
- W744985991 hasPublicationYear "2007" @default.
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