Premium
Real time uncertainty estimation in filling stage of resin transfer molding process
Author(s) -
Tifkitsis K. I.,
Skordos A. A.
Publication year - 2020
Publication title -
polymer composites
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 82
eISSN - 1548-0569
pISSN - 0272-8397
DOI - 10.1002/pc.25803
Subject(s) - surrogate model , transfer molding , markov chain monte carlo , probabilistic logic , kriging , algorithm , computer science , inverse , monte carlo method , materials science , mathematical optimization , bayesian probability , mathematics , machine learning , composite material , artificial intelligence , statistics , mold , geometry
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.