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.

翻译：为满足制造业与科研领域对更灵活高效方法的需求，本文报道了一种基于主动学习算法的自主系统，该系统集成实时原位表征与自主决策处理器。将该系统应用于塑料薄膜成型工艺，验证其在无人工干预条件下高效精准确定目标膜厚工艺参数的能力。针对九种不同膜厚规格的系统测试表明：系统能快速锁定稳定工艺参数（平均11次表征-调整循环，耗时19分钟），并具备规避重复过调等陷阱的能力。进一步将成型膜厚与目标值对比发现，膜宽与膜厚的决定系数分别达到0.87与0.90，展现出高精度特性。值得注意的是，由于塑料成型过程中控制因素（供料速率、施加载荷、材料粘度）存在复杂关联导致初始训练数据缺失，主动学习算法赋予系统从零数据启动物料优化进程的能力。本系统具有本质通用性，可适用于绝大多数材料加工过程，上述成果对加速科研探索与工业化进程具有重要指导意义。