Artificial neural network-based approach for detection and classification of defects in polymeric composites using machine vision in SEM study

Miscibility and adhesion of polymeric composites are adversely affected by some microstructural defects commonly found in polymeric composites. Conventionally, the above-mentioned defects are detected and classified through manual visual analysis of the scanning electron micrographs obtained via scanning electron microscopy (SEM). However, in manual visual inspection of the micrographs, inspection personnel have their own standard of detecting and classifying defects. So, it is quite possible that two different inspection personnel may classify the same defect into two different predefined classes of defects. In order to overcome this problem of misclassification, a machine vision-based approach, which utilises an artificial neural network (ANN) model, is proposed in this study for automated detection and classification of the defects commonly found in polymeric composites. Results obtained reveal that quite a reasonable level of accuracy in detection and classification of microstructural defects of polymeric composites is achieved with the proposed approach.