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
ISSN2329-9266
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
DOI10.1109/JAS.2020.1003387
引用统计
被引频次:53[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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
JAS-2020-0679.pdf(5488KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Cosimo Ieracitano]的文章
[Annunziata Paviglianiti]的文章
[Maurizio Campolo]的文章
百度学术
百度学术中相似的文章
[Cosimo Ieracitano]的文章
[Annunziata Paviglianiti]的文章
[Maurizio Campolo]的文章
必应学术
必应学术中相似的文章
[Cosimo Ieracitano]的文章
[Annunziata Paviglianiti]的文章
[Maurizio Campolo]的文章
相关权益政策
暂无数据
收藏/分享
文件名: JAS-2020-0679.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。