Deep-Learning-Based Microfluidic Droplet Classification for Multijet Monitoring
Deep-Learning-Based Microfluidic Droplet Classification for Multijet Monitoring
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DOI：
10.1021/acsami.1c22048
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发表时间：
2022-04-06
影响因子：
9.5
通讯作者：
Kang, Kyung-Tae
中科院分区：
文献类型：
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作者：
Choi, Eunsik;An, Kunsik;Kang, Kyung-Tae
喷墨打印是一种将微流控液滴沉积在特定区域上的打印技术，因其通用性和大规模生产的可扩展性而日益受到学术界和工业界的关注。喷墨打印效率取决于多喷嘴过程中使用的喷嘴数量。然而，由于多种因素，每个喷嘴的液滴喷射条件可能会有所不同，例如喷嘴的表面润湿条件、油墨的性质以及喷头制造中的差异。出于这些原因，必须由熟练的工程师持续监测和评估液滴喷射条件。本研究提出了一种基于深度学习的方法来识别单喷墨打印过程中的液滴喷射状态。将基于MobileNetV2模型的卷积神经网络(CNN)与优化的超参数相结合，对包含由CCD摄像机拍摄的图像的喷墨帧进行分类。通过按帧时间顺序累积分类数据，可以高精度地评估喷射条件。该方法还成功地用多喷嘴过程进行了演示，每幅图像的测试时间不到一秒。
Inkjet printing, the deposition of microfluidic droplets on a specified area, has gained increasing attention from both academia and industry for its versatility and scalability for mass production. Inkjet printing productivity depends on the number of nozzles used in a multijet process. However, droplet jetting conditions can vary for each nozzle due to multiple factors, such as the surface wetting condition of the nozzle, properties of the ink, and variances in the manufacturing of the nozzle head. For these reasons, droplet jetting conditions must be continuously monitored and evaluated by skillful engineers. The present study presents a deep-learning-based method to identify the droplet jetting status of a single-jet printing process. A convolutional neural network (CNN)-based on the MobileNetV2 model was employed with optimized hyperparameters to classify the inkjet frames containing images captured with a CCD camera. By accumulating the classified class data in order by frame time, the jetting conditions could be evaluated with high accuracy. The method was also successfully demonstrated with a multijet process, with a test time of less than a second per image.