ULTIMATE STRENGTH PREDICTION IN FIBERGLASS/EPOXY BEAMS SUBJECTED TO THREE-POINT BENDING USING ACOUSTIC EMISSION AND NEURAL NETWORKS

摘要

1. The Kohonen self organizing map appeared to successfully classify the AE data into 4 failure mechanisms. Duration, energy and amplitude data were the only AE parameters used for classification. 2. The backpropagation neural network successfully predicted the ultimate loads in unidirectional fiberglass/epoxy beams subjected to 3-point bending from the acoustic emission amplitude data taken up to 80% of the average ultimate load within the desired ± 5 percent goal. 3. Multivariate statistical analysis using the number of hits associated with each failure mechanism predicted ultimate failure loads, but not within the desired goal of ± 5 percent. 4. The backpropagation neural network probably provided better prediction results than the multivariate statistical analysis because multivariate statistical analyses are inherently sensitive to noisy (multiple hit) or sparse data, whereas backpropagation neural networks are not.