Victor Viny Saajan, Schmeiser Andre, Leitte Heike, Gramsch Simone
IEEE Comput Graph Appl. 2022 Mar-Apr;42(2):56-67. doi: 10.1109/MCG.2022.3155867. Epub 2022 Apr 13.
Technical textiles, in particular, nonwovens used, for example, in medical masks, have become increasingly important in our daily lives. The quality of these textiles depends on the manufacturing process parameters that cannot be easily optimized in live settings. In this article, we present a visual analytics framework that enables interactive parameter space exploration and parameter optimization in industrial production processes of nonwovens. Therefore, we survey analysis strategies used in optimizing industrial production processes of nonwovens and support them in our tool. To enable real-time interaction, we augment the digital twin with a machine learning surrogate model for rapid quality computations. In addition, we integrate mechanisms for sensitivity analysis that ensure consistent product quality under mild parameter changes. In our case study, we explore the finding of optimal parameter sets, investigate the input-output relationship between parameters, and conduct a sensitivity analysis to find settings that result in robust quality.
特别是技术纺织品，例如用于医用口罩的无纺布，在我们的日常生活中变得越来越重要。这些纺织品的质量取决于制造工艺参数，而这些参数在实际生产环境中难以轻松优化。在本文中，我们提出了一个可视化分析框架，该框架能够在无纺布的工业生产过程中进行交互式参数空间探索和参数优化。因此，我们调研了用于优化无纺布工业生产过程的分析策略，并在我们的工具中予以支持。为了实现实时交互，我们用一个机器学习替代模型增强数字孪生，以进行快速质量计算。此外，我们集成了灵敏度分析机制，以确保在参数轻微变化的情况下产品质量的一致性。在我们的案例研究中，我们探索了最优参数集的发现，研究了参数之间的输入-输出关系，并进行了灵敏度分析以找到能产生稳健质量的设置。
