Real-time inverse solution of the composites' cure heat transfer problem under uncertainty

This paper addresses the development of an inversion scheme based on Markov Chain Monte Carlo integrating process modelling with monitoring data for the real-time probabilistic estimation of unknown stochastic input parameters such as heat transfer coefficient and resin thermal conductivity and process outcomes during the manufacture of fibrous composites materials. Kriging was utilized to build an efficient surrogate model of the composite curing process based on finite element modelling. The utilization of an inverse scheme with real-time temperature monitoring driving the estimation of process parameters during manufacture results in real-time probabilistic prediction of process outcomes.