Neural Network/Acoustic Emission Burst Pressure Prediction for Impact Damaged Composite Pressure Vessels

摘要

Acoustic emission signal analysis has been used to measure the effect impact damage has on the burst pressure of 146 mm (5.75 in.) diameter graphite/epoxy and the organic polymer, Kevlar/epoxy filament wound pressure vessels. Burst pressure prediction models were developed by correlating the differential acoustic emission amplitude distribution collected during low level hydroproof tests to known burst pressures using backpropagation artificial neural networks. Impact damage conditions ranging from barely visible to obvious fiber breakage, matrix cracking, and delamination were included in this work. A simulated (inert) pro-pellant was also cast into a series of the vessels from each material class, before impact loading, to provide boundary conditions during impact that would simulate those found on solid rocket motors. The results of this research effort demonstrate that a quantitative assessment of the effects that impact damage has on burst pressure can be made for both organic polymer/epoxy and graphite/epoxy pressure vessels. Here, an artificial neural network analysis of the acoustic emission parametric data recorded during low pressure hydroproof testing is used to relate burst pressure to the vessel's acoustic signature. Burst pressure predictions within 6.0 percent of the actual failure pressure are demonstrated for a series of vessels.