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Journal Articles International Journal of Material Forming Year : 2023

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

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Abstract

Elium® 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®/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.
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Dates and versions

hal-03905944 , version 1 (19-12-2022)

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Cite

Chady Ghnatios, Pierre Gérard, Anais Barasinski. An advanced resin reaction modeling using data-driven and digital twin techniques. International Journal of Material Forming, 2023, 16 (1), pp.5. ⟨10.1007/s12289-022-01725-0⟩. ⟨hal-03905944⟩
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