Burst pressure prediction in fiberglass/epoxy pressure vessels using acoustic emission and neural networks
- Embry-Riddle Aeronautical Univ., Daytona Beach, FL (United States)
A burst pressure prediction model was generated from the acoustic emission (AE) amplitude distribution data taken during hydroproof testing for a set of eleven ASTM standard 5.75 inch diameter filament wound, fiberglass/epoxy bottles. The bottles were tested at three different temperatures - 32{degrees}F, 70{degrees}F, and 110{degrees}F - which were input as categorical variables, allowing the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Two of the bottles contained simulated manufacturing defects which lowered their burst pressures. Architecturally, the neural network consisted of 42 input neurons (one categorical variable for temperature plus forty-one amplitude frequencies), a 15 neuron hidden layer for mapping, and a single output neuron for the predicted burst pressure. Seven of the eleven bottles were used to train the network. The AE amplitude distribution data taken up to 25% of the expected burst pressure were used as network inputs, and the actual burst pressures were used as target values for the supervised training phase. The trained network was not able to predict burst pressures accurately on the two defective bottles with such a small test group; they were therefore not considered. The network then used six of the nine bottles for training and blind predicted on the remaining three bottles. The network was then able to predict burst pressures with a worst case error within the desired goal of {+-} 5%.
- OSTI ID:
- 481995
- Report Number(s):
- CONF-960503--
- Country of Publication:
- United States
- Language:
- English
Similar Records
Neural network burst pressure prediction in impact damaged Kevlar/epoxy bottles from acoustic emission amplitude data
Neural network/acoustic emission burst pressure prediction for impact damaged composite pressure vessels