To meet the demands for more adaptable and expedient approaches to augment
both research and manufacturing, we report an autonomous system using real-time
in-situ characterization and an autonomous, decision-making processer based on
an active learning algorithm. This system was applied to a plastic film forming
system to highlight its efficiency and accuracy in determining the process
conditions for specified target film dimensions, importantly, without any human
intervention. Application of this system towards nine distinct film dimensions
demonstrated the system ability to quickly determine the appropriate and stable
process conditions (average 11 characterization-adjustment iterations, 19
minutes) and the ability to avoid traps, such as repetitive over-correction.
Furthermore, comparison of the achieved film dimensions to the target values
showed a high accuracy (R2 = 0.87, 0.90) for film width and thickness,
respectively. In addition, the use of an active learning algorithm afforded our
system to proceed optimization with zero initial training data, which was
unavailable due to the complex relationships between the control factors
(material supply rate, applied force, material viscosity) within the plastic
forming process. As our system is intrinsically general and can be applied to
any most material processes, these results have significant implications in
accelerating both research and industrial processes.

应用实时原位表征和基于主动学习算法的自主系统，在无人干预的情况下，确定特定目标薄膜尺寸的工艺条件，具有高效准确的特点，为加快研究和工业过程提供了重要的启示。