Online monitoring and control of a cyber-physical manufacturing process under uncertainty

Abstract

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 realtime 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.

Preliminary results for a data-driven uncertainty quantification framework in wire + arc additive manufacturing using bead-on-plate studies

“…They emphasized that the DTs would extend the application of metal AM to have more repeatability in the process and reproducibility in the parts. Nannapaneni et al introduced a CPS consisting of the manufacturing process, sensors, computing, and actuation subsystems, applying the dynamic Bayesian network to the systems [37]. The authors performed the sensitivity analysis to reduce the dimension and the number of parameters.…”

“…In the data-driven UQ of metal AM, and can be the mean and standard deviation of the QoI-related output parameters from bead geometry, melt pool dimensions, surface roughness, or mechanical properties. where and are the expected value and the variance, respectively, is the -th variable, is a vector, array, or matrix of variables except , and is the expected value of over with xed [32,37,68]. Even though the rst-order Sobol index of is small, it does not mean that the variable has a small contribution to [64].…”

Big data and machine learning: A roadmap towards smart plants

“…In a CPS, the model predictions may be affected by uncertainty sources from (Nannapaneni et al, 2020): The computing (cyber) subsystem (resource and communication uncertainty), manufacturing (physical) subsystem (input uncertainty, process variability and modelling errors), and sensors (measurement uncertainty). The interaction between the physical and cyber components further increases the complexity by aggregating and compounding these uncertainty sources over time.…”

Adaptive resilient control for cyber-physical systems against unknown injection attacks in sensor networks

“…As a tight integrated system, cyber-physical systems are composed of physical elements (including controllers, sensors, actuators, plants) and cyber networks for accurate sensing, ubiquitous computation, efficient communication, effective control [1][2][3] . The real application communities of CPSs cover transportation [4,5] , health monitoring [6,7] , manufacture [8,9] , smart gird [10] , aerospace system [11] , and so on. The connection to the internet in CPSs renders it is more vulnerable to malicious instructions and attacks.…”