چكيده به لاتين
The development of structural health monitoring systems is one of the necessary solutions to evaluate the performance of superstructures such as aircraft, ships, structures such as bridges, pipelines, etc., which ultimately leads to increased reliability in the performance of structures and changes in the maintenance process. Sheet waves have attracted the attention of many researchers in recent years due to their advantages in the non-destructive evaluation of structures and are in the research stages in order to develop the monitoring of health structures. Due to the constant presence of connections in the mentioned structures and also the importance of sheet waves in the field of troubleshooting, in the present dissertation, the health of joints using sheet waves has been investigated. In this study, shell-reinforcing adhesive joints as a functional joint in sensitive industries such as aerospace have been investigated. In this dissertation, using lamb waves, suitable features have been obtained to identify the size and position of defects. First, after examining the theories of diffusion of lamb waves on isotropic sheets, the parameters required for the appropriate test were determined. Using finite element simulations, the effect of the defect on the propagation of the lamb waves has been investigated. Simulations for three different adhesive thicknesses, three different sizes of circular defects have been performed in 9 different positions and the effect of each of them on the wave passing through the joint has been investigated. The signals obtained from the faulty connections were compared with the signal obtained from the healthy connection and the desired area was isolated from the total received signal for further analysis. This reduced the computational volume and increased its accuracy in future processing. Proper and correct separation of defects requires finding suitable characteristics for it. Therefore, 32 features were examined to differentiate and differentiate defects. Then, the neural network was used to provide the basis for creating appropriate patterns for the separation of defects. The obtained features were used as neural network inputs. By changing the educational functions, number of middle layers and neurons of the middle layers of the neural network, patterns with the highest percentage of correct diagnosis were extracted. The percentage of correct detection of neural network for adhesive thickness separation was 93.8%, for defect area separation in terms of size 100% and for defect position separation in X and Y positions were 96.1% and 95.1%, respectively. The obtained results show the efficiency of the IDE method and the features selected to distinguish the defects of such connections
