Toward an Augmented and Explainable Machine Learning Approach for Classification of Defective Nanomaterial Patches

Electrospinning is a manufacturing technique used to produce nanofibers for engineering applications. This process depends on several control parameters (such as solution concentration, applied voltage, flow rate, tip-to-collector distance) whose variations during the experiments may lead to the formation of defective nanofibers (D-NF) along with non-defective nanofibers (ND-NF). D-NF present either with impurities or morphological defects that prevent their practical use in nanotechnology. In this context, here, a data augmentation based machine learning approach is proposed to classify Scanning Electron Microscope (SEM) images in two classes (i.e., D-NF vs. ND-NF). To this end, a custom Convolutional Neural Network (CNN) is developed to perform the binary classification task, achieving an accuracy rate up to 93.85%. Moreover, the explainability of the proposed CNN is also explored by means of an occlusion sensitivity analysis in order to investigate which area of the SEM image mostly contributes to the classification task. The achieved encouraging findings stimulate the use of the proposed framework in industrial applications.