Automatic void content assessment of composite laminates using a machine-learning approach

Abstract

Voids have a substantial impact on the mechanical properties of composite laminates and can lead to premature failure of composite parts. Optical microscopy is a commonly employed imaging technique to assess the void content of composite parts, as it is reliable and less expensive than alternative options. Usually, image thresholding techniques are used to parse the void content of the acquired microscopy images automatically; however, these techniques are very sensitive to the imaging acquisition conditions and type of composite material used. Additionally, these algorithms have to be calibrated before each analysis, in order to provide accurate results.This work proposes a machine-learning approach, based on a convolutional neural network architecture, with the objective of providing a robust tool capable of automatically parsing the void content of optical microscopy images, without the need of parameter tuning.Results from training and testing datasets composed of microscopy images extracted from three distinct types of laminates confirm that the proposed approach parses void content from microscopy images more accurately than a traditional thresholding algorithm, without the need of a previous calibration step. This work shows that the proposed approach is promising, despite sometimes lower than expected precision in individual void statistics.