Artificial neural network based delamination prediction in composite plates using vibration signals

Abstract

Dynamic loading on composite components may induce damages such as cracks, delaminations, etc. and development of an early damage detection technique for delamination prediction is one of the most important aspects in ensuring the integrity and safety of such components. The presence of damages such as delaminations on the composites reduces its stiffness and changes the dynamic behaviour of the structures. As the loss in stiffness leads to changes in the natural frequencies, mode shapes, and other aspects of the structure, vibration analysis may be the ideal technique for delamination prediction. In this research work, the supervised feed-forward multilayer back-propagation Artificial Neural Network is used to determine the position and area of delaminations in glass fiber-reinforced polymer (GFRP) plates using changes in natural frequencies as inputs. The natural frequencies were obtained by finite element analysis and results are validated experimentally. The findings show that the suggested technique can satisfactorily estimate the location and extent of delaminations in composite plates.

Keywords

• health monitoring
• composite
• gfrp
• delamination
• vibration
• natural frequency
• artificial neural network