This paper addresses the development of a digital twin, based on an inversion procedure, integrating process monitoring with simulation of composites manufacturing to provide a real time probabilistic estimation of process outcomes. A computationally efficient surrogate model was developed based on Kriging. The surrogate model reduces the computational time allowing inversion in real time. The tool was implemented in the filling stage of an resin transfer molding processing of a carbon fiber reinforced part resulting in the probabilistic prediction of unknown parameters. Flow monitoring data were acquired using dielectric sensors. The inverse scheme based on Markov Chain Monte Carlo uses input parameters, such as permeability and viscosity, as unknown stochastic variables. The scheme enhances the model by reducing model parameter uncertainty yielding an accurate on line estimation of process outcomes and critical events such as racetracking. The inverse scheme provides a prediction of filling duration with an error of about 5% using information obtained within the first 30% of the process.

中文翻译：

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树脂传递模塑工艺填充阶段的实时不确定度估算

本文介绍了一种基于反演程序的数字孪生系统的开发，该过程将过程监控与复合材料制造的仿真集成在一起，以提供过程结果的实时概率估计。在克里格的基础上开发了计算效率高的代理模型。代理模型减​​少了计算时间，允许实时反演。该工具是在碳纤维增强零件的树脂传递模塑加工的填充阶段实施的，从而可以预测未知参数。使用介电传感器获取流量监控数据。基于马尔可夫链蒙特卡罗的逆方案将诸如渗透率和粘度之类的输入参数用作未知随机变量。该方案通过减少模型参数的不确定性来增强模型，从而对过程结果和关键事件（例如赛道）进行精确的在线估计。逆方案使用在过程的前30％内获得的信息来预测填充持续时间，误差约为5％。