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Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

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Cosimo Ieracitano, Annunziata Paviglianiti, Maurizio Campolo, Amir Hussain, Eros Pasero and Francesco Carlo Morabito, "A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 64-76, Jan. 2021. doi: 10.1109/JAS.2020.1003387
Citation: Cosimo Ieracitano, Annunziata Paviglianiti, Maurizio Campolo, Amir Hussain, Eros Pasero and Francesco Carlo Morabito, "A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 64-76, Jan. 2021. doi: 10.1109/JAS.2020.1003387

A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

doi: 10.1109/JAS.2020.1003387
Funds:  This work was supported by the European Commission, the European Social Fund and the Calabria Region (C39B18000080002). The authors are the only responsible for this publication and the European Commission and the Region of Calabria decline any responsibility for the use that may be made of the information in it held. This work was also supported by the UK Engineering and Physical Sciences Research Council (EPSRC) (EP/M026981/1, EP/T021063/1, EP/T024917/1)
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  • 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.


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    • Anomaly detection system for electrospun nanofibers
    • Decomposition of original SEM images in sub-patches
    • Hybrid unsupervised and supervised machine learning approach
    • Combination of AE and MLP for features extraction and classification


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