Multiclass Reinforced Active Learning for Droplet Pinch-Off Behaviors Identification in Inkjet Printing
Multiclass Reinforced Active Learning for Droplet Pinch-Off Behaviors Identification in Inkjet Printing
复制标题
喷墨打印中液滴夹断行为识别的多类强化主动学习
DOI：
10.1115/1.4057002
复制
发表时间：
2023
期刊：
影响因子：
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通讯作者：
Sun, Hongyue
中科院分区：
文献类型：
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作者：
Li, Zebin;Segura, Luis Javier;Li, Yifu;Zhou, Chi;Sun, Hongyue
喷墨打印(IJP)是一种很有前途的添加剂制造技术，在电子和生物医学产品中产生了许多创新。在IJP中，产品是通过在衬底上沉积液滴来制备的，液滴的夹断行为对产品质量有很大影响。因此，识别液滴的夹断行为是至关重要的。然而，由于可以收集大量的夹取行为的图像，因此对夹取行为进行注释是繁琐的。主动学习(AL)是一种机器学习技术，它通过迭代获取人类标注并更新分类模型来提取人类知识，以识别夹击行为。因此，可以在有限的标签下获得良好的分类性能。然而，在查询过程中，信息量最大的实例(即图像)是不同的，并且AL中的大多数查询策略无法处理这些动态，因为它们是手工制作的。为此，本文提出了一种多类强化主动学习(MCRAL)框架，通过强化学习(RL)来训练查询策略。我们设计了一种独特的内在奖励信号来提高分类模型的性能。此外，如何从图像中提取特征用于夹带行为识别并不是一件容易的事情。因此，我们使用图卷积网络来提取液滴图像的特征。结果表明，MCRAL优于AL，可以减少人对夹击行为识别的工作量。我们进一步证明，通过将工艺参数与预测的液滴夹断行为联系起来，可以基于MCRAL调整液滴夹断行为。
Inkjet printing (IJP) is one of the promising additive manufacturing techniques that yield many innovations in electronic and biomedical products. In IJP, the products are fabricated by depositing droplets on substrates, and the quality of the products is highly affected by the droplet pinch-off behaviors. Therefore, identifying pinch-off behaviors of droplets is critical. However, annotating the pinch-off behaviors is burdensome since a large amount of images of pinch-off behaviors can be collected. Active learning (AL) is a machine learning technique which extracts human knowledge by iteratively acquiring human annotation and updating the classification model for the pinch-off behaviors identification. Consequently, a good classification performance can be achieved with limited labels. However, during the query process, the most informative instances (i.e., images) are varying and most query strategies in AL cannot handle these dynamics since they are handcrafted. Thus, this paper proposes a multiclass reinforced active learning (MCRAL) framework in which a query strategy is trained by reinforcement learning (RL). We designed a unique intrinsic reward signal to improve the classification model performance. Moreover, how to extract the features from images for pinch-off behavior identification is not trivial. Thus, we used a graph convolutional network for droplet image feature extraction. The results show that MCRAL excels AL and can reduce human efforts in pinch-off behavior identification. We further demonstrated that, by linking the process parameters to the predicted droplet pinch-off behaviors, the droplet pinch-off behavior can be adjusted based on MCRAL.