Author(s)

R. Teti & G. Caprino

Abstract

This paper deals with the evaluation of residual tensile strength of composite laminates containing impact damage generated with different impact energies. Sensor fusion of acoustic emission and load data is carried out through neural networks to obtain a prediction of residual tensile strength as early as possible in the loading history of impacted composite laminates. The results show that neural network processing provides an effective monitoring of laminate fracture behavior based on acoustic emission analysis. Introduction One of the main disadvantages of composite materials in comparison with metals is their liability to be damaged by low velocity impact. Accordingly, composite laminates can undergo severe strength reduction because of im

Keywords