An advanced resin reaction modeling using data-driven and digital twin techniques

Elium (R) resin is nowadays actively investigated to leverage its recycling ability. Thus, multiple polymerization modeling are developed and used. In this work, we investigate the polymerization of Elium (R)/Carbon fiber composite in a cylindrical deposition, followed by an in-oven heating. The model parameters are optimized using an active-set algorithm to match the experimental heating profiles. Moreover, the simulation efforts are coupled to an artificial intelligence modeling of the discrepancies. For instance, a surrogate model using convolution recurrent neural network is trained to reproduce the error of the simulation. Later, a digital twin of the process is built by coupling the simulation and the machine learning algorithm. The obtained results show a good match of the experimental results even on the testing sets, never used during the training of the surrogate model. Finally, the digital twin results are post-processes to investigate the resin polymerization through the thickness of the part.