标题： 凹印印刷中流体动力学图案形成的降阶建模与分类 显示英文标题

标题： Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing

摘要： 在印刷和涂布过程中，流体力学图案形成现象仍然没有被完全理解。然而，基本的理解对于实现高质量的印刷产品以及根据特定应用（如印刷电子、图形印刷或生物医学印刷）的需求调整印刷图案至关重要。本文的目的是开发一种基于监督机器学习和降阶建模方法的自动化图案分类算法。我们使用了HYPA-p数据集，这是一个大型的凹版印刷图像图像数据集，展示了各种类型的流体力学图案形成现象。它首次实现了印刷工艺参数与最终印刷图案之间的相关性。HYPA-p数据集中的26880张图像由人工观察者标记为点图案、混合图案或手指状图案；864000张图像（97%）未标记。使用奇异值分解（SVD）来找到标记图像的模式，并通过截断和投影来降低整个数据集的维度。选择的机器学习分类技术在降阶数据上进行训练。我们研究了多个因素的影响，包括分类器的选择、是否使用快速傅里叶变换（FFT）对标记图像进行预处理、数据平衡和数据归一化。表现最好的模型是一个在不平衡、FFT变换数据上训练的k最近邻（kNN）分类器，测试误差为3%，比人工观察者高出7%。数据平衡略微将kNN模型的测试误差增加到5%，但也将混合类的召回率从90%提高到94%。最后，我们演示了如何使用训练好的模型预测未标记图像的图案类别，以及如何将预测结果以区域图的形式与印刷工艺参数相关联。 显示英文摘要

摘要： Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood. However, fundamental understanding is essential to achieve high-quality printed products and to tune printed patterns according to the needs of a specific application like printed electronics, graphical printing, or biomedical printing. The aim of the paper is to develop an automated pattern classification algorithm based on methods from supervised machine learning and reduced-order modeling. We use the HYPA-p dataset, a large image dataset of gravure-printed images, which shows various types of hydrodynamic pattern formation phenomena. It enables the correlation of printing process parameters and resulting printed patterns for the first time. 26880 images of the HYPA-p dataset have been labeled by a human observer as dot patterns, mixed patterns, or finger patterns; 864000 images (97%) are unlabeled. A singular value decomposition (SVD) is used to find the modes of the labeled images and to reduce the dimensionality of the full dataset by truncation and projection. Selected machine learning classification techniques are trained on the reduced-order data. We investigate the effect of several factors, including classifier choice, whether or not fast Fourier transform (FFT) is used to preprocess the labeled images, data balancing, and data normalization. The best performing model is a k-nearest neighbor (kNN) classifier trained on unbalanced, FFT-transformed data with a test error of 3%, which outperforms a human observer by 7%. Data balancing slightly increases the test error of the kNN-model to 5%, but also increases the recall of the mixed class from 90% to 94%. Finally, we demonstrate how the trained models can be used to predict the pattern class of unlabeled images and how the predictions can be correlated to the printing process parameters, in the form of regime maps.