Neural network approach for damaged area location prediction of a composite plate using electromechanical impedance technique

Nowadays, breakthrough composite technologies are intensifying the complexity of structural components every day and assuring the structural integrity is becoming more essential, thus creating challenges for developing a cost effective and reliable non-destructive evaluation (NDE) technique. As conventional NDE techniques usually require expensive equipments, trained experts and out-of-service period, such techniques may be inadequate for autonomous online health monitoring of structures. In this study, a relatively new technique known as electromechanical impedance (EMI) technique is combined with a neural network technique to predict the damaged areas on a composite plate. Regardless of the advantages such as low cost, robustness, simplicity and online possibilities, this technique still has various problems to be solved. For one, locating a damaged area can be extremely difficult as this non-model based technique heavily relies on the variations in the impedance signatures. The results show that the non-homogenous property is an advantage for the study, successfully identifying the damage location for the prepared test specimen with an acceptable performance.