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Defect Detection in SEM Images of Nanofibrous Materials

机译：纳米纤维材料的SEM图像中的缺陷检测

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摘要

Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial production.
机译：纳米产品代表了一个潜在的增长领域，在工业，医疗和环境应用中广泛要求使用纳米纤维材料。不幸的是，纳米级的生产过程难以控制，并且纳米产品经常表现出损害其功能特性的局部缺陷。因此，缺陷检测在智能制造系统中特别重要，可以在缺陷超过给定的公差水平时立即发出警报，并设计能够优化物理性能并控制所生产材料的缺陷的生产过程。在这里，我们提出了一种通过分析扫描电子显微镜图像来检测纳米纤维材料中缺陷的新颖解决方案。我们采用了一种算法，该算法在训练阶段可以学习一个模型，该模型可产生表征正确生产的纳米纤维材料的结构的稀疏表示。然后，通过分析输入图像的每个补丁并提取定量评估补丁是否符合学习模型的特征，来检测缺陷。所提出的解决方案已经成功验证了从原型静电纺丝机生产的样品中获得的45张图像。计算时间短表明所提出的解决方案可以有效地用于工业生产的监视系统中。

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