Stochastic Modelling of the Flow-Front Evolution in a Vacuum Assisted Resin Transfer Moulding Process with Missing Data
For manufacturers using VARTM, this provides a practical flow-front tracking solution that remains accurate even when sensors fail, improving process monitoring and control.
The paper proposes a stochastic differential equation-based grey-box model for tracking the epoxy flow-front in VARTM processes, handling missing sensor data via a modified extended Kalman filter. The method shows robust performance across various sensor fault scenarios, noise levels, and sampling rates.
The real-time fault monitoring and control of the Vacuum Assisted Resin Transfer Moulding (VARTM) production process requires a knowledge of the position of the epoxy flow-front inside the mould. Therefore, a fast and accurate flow-front tracking system capable of combining the underlying physics of the flow-front dynamics with the measured data is highly prized. Stochastic differential equations (SDEs) based grey-box models deliver a good trade-off between high fidelity models and data-driven black-box models for designing such a flow-front position tracking system. In this paper, we propose a simple yet novel coupled SDE based spatiotemporal grey-box model of the flow-front dynamics in case of missing sensor information. The proposed method uses the finite difference approximation of the spatial domain of the flow-front for estimating spatial flow pattern of the epoxy. Furthermore, to accommodate for the missing sensor data, we utilise a modified version of the continuous-discrete extended Kalman filter (CD-EKF) based estimation framework for SDEs that takes into consideration the effective dimension of the measurement space during the identification process. The performance of the method is evaluated for various common sensor faults scenarios at different levels of measurement noise and sampling rates.