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.

中文翻译：

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基于混合无监督和监督机器学习的电纺纳米纤维新型自动分类系统

通过静电纺丝工艺制造纳米材料需要对静电纺丝纳米纤维的相关扫描电子显微镜（SEM）图像进行准确，细致的检查，以确保不产生结构缺陷。异常的存在阻止了电纺纳米纤维材料在纳米技术中的实际应用。因此，在工业4.0的背景下，纳米材料的自动监测和质量控制是一个相关的挑战。本文提出了一种新颖的自动分类系统，用于均质（无异常）和非均质（有缺陷）纳米纤维。检查程序旨在避免直接处理多余的完整SEM图像。特别，首先将要分析的图像划分为子图像（nanopatches），然后将其用作混合无监督和受监督机器学习系统的输入。第一步，对自动编码器（AE）进行无监督学习训练，以生成代表输入图像和相关特征向量的代码。接下来，经过监督学习训练的多层感知器（MLP）使用提取的功能对非均质纳米纤维（NH-NF）和均质纳米纤维（H-NF）贴片进行分类。结果表明，所得的新颖AE-MLP系统优于其他标准机器学习模型和其他最新技术，报告的准确率高达92.5％。此外，相对于其他深度学习策略（如卷积神经网络（CNN）），提出的方法可以降低模型的复杂度。在此基准研究中取得的令人鼓舞的性能可以刺激所提出的方案在其他具有挑战性的工业制造任务中的应用。