Real time uncertainty estimation in filling stage of resin transfer molding process

Abstract

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.

PermeabilityNets: Comparing Neural Network Architectures on a Sequence-to-Instance Task in CFRP Manufacturing

“…Local changes of preform permeability can occur at curved sections of the component or at locations of wall thickness changes. In addition, they may be caused by imperfect material properties (e.g., missing or misaligned fiber bundles) or by manual handling of the fibrous structure The variation of permeability can reach up to 20% [2]. Local variations in permeability affect the temporal progress of the flow front and can thus cause suboptimal impregnation quality or even dry spots, deteriorating the mechanical performance.…”

An Overview of the Measurement of Permeability of Composite Reinforcements

“…The resin flow front position x f during the infusion process of a preform consisting of nine layers of a 5H satin weave carbon fabric was determined from the admittance Y values measured by the sensor: where Y w and Y d (S) are the admittance of the fully wetted and dry sensor, respectively, and L (m) is the sensor length, and results were compared with visual monitoring. This sensor was applied to acquire the flow monitoring data required for a real-time probabilistic estimation of process outcomes of the RTM fabrication process of a carbon fiber-reinforced composite flat part with a recessed edge [ 90 ].…”

Graphene nanoparticles as data generating digital materials in industry 4.0

“…Finally, the machine/deep learning-based surrogate/predictive models can be used for process simulations [155][156][157] as well as for failure predictions in diagnostic and prognostic maintenance [158][159][160] . Using the data provided by a set of pressure sensors, Zhu et al 161 implemented a neural network model for the prediction of flow-front patterns at any impregnation time.…”