Neural fuzzy analysis of delaminated composites from shearography imaging

The use of shearography for impact damage detection and characterization is often difficult to achieve in laminated composites due to lack of a formation of clear fringe patterns as in the case of delaminations. Furthermore, existing techniques for interpreting shearograms are often inadequate for impact damage assessment in composite materials. In this paper, a technique is reported that combines conventional image analysis with neural networks (NNs) classification and fuzzy logic inference to characterize shearograms. The objective is to train an automated system for recognition of pertinent characteristics and features from shearograms of composites damaged by impact. The results demonstrate the potential of the NN-based shearography technique in characterizing damage in laminated composites. Its applicability to structural health monitoring is also discussed.