Real-time Bayesian Inversion in Resin Transfer Moulding using Neural Surrogates

The aim of this study is to rapidly estimate the properties of fibrous reinforcements during the injection phase of Resin Transfer Moulding. There are five data sets associated with this study. The first data set was generated by simulating the resin injection process for 50,000 samples of reinforcement properties. This data was used to train a surrogate model to emulate the injection simulator. The remaining four data sets correspond to the lab experiments included within the paper. These show time series plots for resin pressure at each sensor within the tool, recorded by the data acquisition system.