Condition monitoring of delamination during drilling of GFRP composites using the acoustic emission technique - a neural network model

摘要

Acoustic emission technology (AET) is an unconventional non-destructive testing method for the detection, location and characterisation of various kinds of defect. In the drilling of materials, AET can be applied to understand the different events that occur from the time the drill comes in contact to the time when the process is complete. This paper focuses on monitoring the delamination damage with the help of the acoustic emission technique. The parameters will form a relationship with the level of delamination. These findings are to be used to build and develop an artificial neural network (ANN) model. The ANN model can be used to predict the delamination online. Hence, this study will help us to categorise and segregate the product in order to accept or reject, or rectify and accept based on the application of drilling of GFRP composites.