A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope SEM images of the electrospun nanofiber, to ensure that no structural defects are produced. The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology. Hence, the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0. In this paper, a novel automatic classification system for homogenous anomaly-free and non-homogenous with defects nanofibers is proposed. The inspection procedure aims at avoiding direct processing of the redundant full SEM image. Specifically, the image to be analyzed is first partitioned into sub-images nanopatches that are then used as input to a hybrid unsupervised and supervised machine learning system. In the first step, an autoencoder AE is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features. Next, a multilayer perceptron MLP , trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber NH-NF and homogenous nanofiber H-NF patches. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques, reporting accuracy rate up to 92.5. In addition, the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks CNN . The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.