基于图像分析和可信度数据的薄膜硝酸盐传感器性能预测,以实现认证框架

Xihui Wang, Ajanta Saha, Ye Mi, A. Shakouri, Muhammad Ashraful Alam, G. Chiu, J. Allebach
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引用次数: 0

摘要

在现代工业环境中,对各种类型的传感器的需求不断增加。对传感器数量和质量的需求每年都在增加。我们的研究重点是薄膜硝酸盐传感器,并寻求提供一种可靠的方法来监测传感器的质量,同时降低生产成本。我们正在研究一种基于图像的机器学习方法,以便对制造管道中的每个传感器进行实时质量评估。它开辟了实时生产参数调整的可能性,以提高传感器的性能。该技术有可能显著降低质量控制成本,同时提高传感器质量。以往的研究已经证明,传感器局部层(离子选择膜(ISM)层)的质地直接关系到传感器的性能。我们的方法试图使用这样建立的相关性来训练一个基于学习的系统,以从传感器有源区域(即ISM)的静态照片中预测任何给定传感器的性能。这将允许实时评估每个传感器,而不是样品测试。随机抽样测试在时间和人力上都很昂贵,因此,它不能解释所有的单个传感器。传感器测量是数据采集过程中至关重要的一部分。为了测量传感器的性能,传感器被带到专门的实验室进行性能测量。在测量过程中,噪声和误差是不可避免的;因此,我们根据性能数据生成可信度数据,以显示每个传感器在每个采样时间的性能信号的可靠性。在本文中,我们提出了一种基于机器学习的方法,利用从非接触式传感器图像中提取的图像特征,在可信度数据的指导下预测传感器的性能。这将消除在制造过程中对每个传感器进行测试的需要,这在高速滚对滚设置中是不切实际的,从而真正实现了构建框架的认证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thin-Film Nitrate Sensor Performance Prediction Based on Image Analysis and Credibility Data to Enable a Certify As Built Framework
In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production. We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors. Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time. In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.
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