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Abstract The intent of this research project was to improve upon current nondestructive evaluation techniques for predicting the burst pressures of Kevlar/epoxy pressure vessels from acoustic emission (AE) data. AE data were recorded during the first step of hydro-burst testing of twelve unfilled and eleven inert-propellant-filled Kevlar/epoxy pressure vessels. Bang Bang Full Movie Free Download 300mb. Scan2cad V7 Keygen. These vessels were first impact damaged to varying degrees, after which the initial part of AE data up to 25% of the burst pressure was input to a back-propagation neural network (BPNN) capable of predicting the vessels ’ burst pressures. This raw AE amplitude distribution histogram data resulted in worst case predictions of 19.04% for the unfilled bottles and 5.70% for the inert propellant filled bottles.

A Kohonen self organizing map (SOM) neural network was subsequently used to classify the raw AE data into the four distinct failure mechanisms typical of filament-wound composite pressure vessels. Using the matrix cracking only amplitude histogram data as input to the BPNN improved the worst case burst pressure predictions from 19.04% to 8.81% for the unfilled bottles and 5.70% to 3.85% for the inert propellant filled bottles, respectively. The greater than ±5% worst case error for the unfilled bottles was attributed to the scarcity of AE data available up to 25% of the expected burst pressure for BPNN processing. Thus, by inputting the amplitude histogram for the matrix cracking only data into a BPNN rather than the entire amplitude distribution histogram, the technique for predicting burst pressures in impact-damaged filament-wound Kevlar/epoxy pressure vessels was made significantly more accurate in spite of a scarcity of AE data.