Indian Journal of Science and Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


Currently, the volume fraction of a glass fibre/matrix based composite material is being assessed only by destructive techniques. Instead of changing or destroying the structure, a new non-destructive approach based on vibration technique is proposed in this research. Further, the main objective of this paper is on the determination of fibre/matrix volume fractions using vibration analysis. A complete experimental protocol has been developed to record the vibration signals produced from experimental plates with different volume fractions and thicknesses. The recorded vibration signals were analyzed both in time and frequency domains. Subsequently, statistical parameter features from each thickness was extracted and associated to the volume fraction levels. Artificial Neural Network (ANN) models were then developed to classify the level of volume fraction. The classification performance of the developed network models were in the range of 80-98 percent. From the results, it has been observed that the network model with frequency band based features has yielded a better classification performance. This proves that the method implemented can be used as the alternatives to the ASTM D2584−11 for determination of volume fraction of a glass fibre/matrix composite plate using vibration analysis.

Keywords

Composite, Feed-Forward Neural Network, Non-Destructive Testing, Statistical Features, Vibration Signal, Volume Fraction.
User