Predicting Failure Strength of Randomly Oriented Short Glass Fiber-Epoxy Composite Specimen by Artificial Neural Network Using Acoustic Emission Parameters

摘要

Acoustic emission (AE) peak amplitude and cumulative energy emitted during 50% of failure of composite specimen was collected, analyzed, and utilized to predict the ultimate tensile strength (UTS) using artificial neural network (ANN) and the performance of various training algorithm on prediction was analyzed. AE data have been collected from finite numbers of randomly oriented short glass fiber-epoxy tensile specimens, while loading up to failure in a tensile testing machine. AE response from each of the specimen was classified and segregated by understanding the failure mechanism. A feed forward back-propagation type ANN was designed and the segregated data of amplitude hits and cumulative energy was processed using two separate networks to predict the UTS of corresponding specimens using it with appropriate parameters and the results were analyzed.