Defect Characterisation of GFRP Cross Ply Laminates using Artificial Neural Networks

Abstract

An experimental work has been carried out to characterise the defects of post impacted Glass/Epoxy composite laminates using online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from ten-layered glass fibre (200 MIL cloth) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression moulding machine for 24 hours. 25 test specimens (ASTM D3039 standard) were prepared from the cross-ply laminates using water jet cutting machine. 21 specimens were subjected to impact load from three different heights using CEAST Fractovis Drop Impact machine. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using 30 kN INSTRON 3367 universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. These AE parameters are then used to characterise the defects in composite materials using Fuzzy C-means clustering algorithm associated with Principal Component Analysis. Artificial Neural Network technique is used in the process of getting the results. The acquired results can be used for online health monitoring through which failure of composite components can be identified at the initial stages.

Modeling and influence of thrust force torque and delamination during drilling of natural fiber composite

“…Other defects such as cracks, brittle fractures, cavities were not found within the drilled surface. [27][28][29][30] 4 | CONCLUSIONS Experimental work to analyze the behavior of drilling the fiber reinforced composites under varying cutting conditions has been undertaken. The major parameters that have been investigated are the diameter of the drill, cutting speed, feed, and quality of the hole.…”