Nannapaneni Saideep, Mahadevan Sankaran, Dubey Abhishek, Lee Yung-Tsun Tina
Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS 67260, USA.
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235, USA.
J Intell Manuf. 2020;195. doi: 10.1007/s10845-020-01609-7.
Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate real-time analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.
计算、传感和通信领域最近的技术进步推动了信息物理制造过程的发展，在这种过程中，一个计算子系统通过分析传感器数据实时监测制造过程性能，并实施必要的控制以提高产品质量。本文开发了一种预测控制框架，其中控制动作是在使用过程模型预测未来某个时刻的制造过程状态或产品质量之后实施的。在信息物理制造过程中，产品质量预测可能会受到来自计算子系统（资源和通信不确定性）、制造过程（输入不确定性、过程变异性和建模误差）以及传感器（测量不确定性）的不确定性源的影响。此外，由于计算子系统与制造过程之间的持续交互，这些不确定性源可能会随着时间的推移而聚集和复合。在某些情况下，模型预测所需的一些过程参数可能无法精确得知，可能需要从实时传感器数据中推导出来。本文开发了一种动态贝叶斯网络方法，该方法能够对多种不确定性进行聚集、参数估计以及进行用于在线控制的稳健预测。随着过程参数数量的增加，使用传感器数据实时估计这些参数的计算成本可能会很高。为便于进行实时分析，基于方差的全局敏感性分析用于降维。针对信息物理车削过程，阐述了所提出的不确定性下在线监测与控制以及降维方法。
