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

doi:10.1109/JAS.2020.1003387

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers
	Cosimo Ieracitano; Annunziata Paviglianiti; Maurizio Campolo; Amir Hussain; Eros Pasero; Francesco Carlo Morabito
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:1 页码:64-76
摘要	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.
关键词	Anomaly detection autoencoder (AE) electrospinning machine learning material informatics nanomaterials
DOI	10.1109/JAS.2020.1003387
引用统计	被引频次：54[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43896
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Cosimo Ieracitano,Annunziata Paviglianiti,Maurizio Campolo,et al. A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(1):64-76.
APA	Cosimo Ieracitano,Annunziata Paviglianiti,Maurizio Campolo,Amir Hussain,Eros Pasero,&Francesco Carlo Morabito.(2021).A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers.IEEE/CAA Journal of Automatica Sinica,8(1),64-76.
MLA	Cosimo Ieracitano,et al."A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers".IEEE/CAA Journal of Automatica Sinica 8.1(2021):64-76.

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