RESUMO
Among advanced manufacturing techniques for fiber-reinforced polymer-matrix composites (FRPCs) which are critical for aerospace, marine, automotive, and energy industries, frontal polymerization (FP) has been recently proposed to save orders of magnitude of time and energy. However, the cure kinetics of the matrix phase, usually a thermosetting polymer, brings difficulty to the design and control of the process. Here, we develop a deep learning model, ChemNet, to solve an inverse problem for predicting and optimizing the cure kinetics parameters of the thermosetting FRPCs for a desired fabrication strategy. ChemNet consists of a fully connected FeedForward 9 layer deep neural network trained on one million examples, and predicts activation energy and reaction enthalpy given the front characteristics such as speed and maximum temperature. ChemNet provides highly accurate predictions measured by the mean squared error (MSE) and by the maximum absolute error (MAE) metrics. ChemNet's performance on the "hidden" test data set had an MSE of 5.58 × 10-6 and a MAE of 1 × 10-3.
RESUMO
Thermoset polymers and composite materials are integral to today's aerospace, automotive, marine and energy industries and will be vital to the next generation of lightweight, energy-efficient structures in these enterprises, owing to their excellent specific stiffness and strength, thermal stability and chemical resistance1-5. The manufacture of high-performance thermoset components requires the monomer to be cured at high temperatures (around 180 °C) for several hours, under a combined external pressure and internal vacuum 6 . Curing is generally accomplished using large autoclaves or ovens that scale in size with the component. Hence this traditional curing approach is slow, requires a large amount of energy and involves substantial capital investment6,7. Frontal polymerization is a promising alternative curing strategy, in which a self-propagating exothermic reaction wave transforms liquid monomers to fully cured polymers. We report here the frontal polymerization of a high-performance thermoset polymer that allows the rapid fabrication of parts with microscale features, three-dimensional printed structures and carbon-fibre-reinforced polymer composites. Precise control of the polymerization kinetics at both ambient and elevated temperatures allows stable monomer solutions to transform into fully cured polymers within seconds, reducing energy requirements and cure times by several orders of magnitude compared with conventional oven or autoclave curing approaches. The resulting polymer and composite parts possess similar mechanical properties to those cured conventionally. This curing strategy greatly improves the efficiency of manufacturing of high-performance polymers and composites, and is widely applicable to many industries.
RESUMO
As frontal polymerization is being considered as a faster and more energy efficient manufacturing technique for polymer-matrix fiber-reinforced composites, we perform a finite-element-based numerical study of the initiation and propagation of a polymerization front in dicyclopentadiene (DCPD). The transient thermochemical simulations are complemented by an analytical study of the steady-state propagation of the polymerization front, allowing to draw a direct link between the cure kinetics model and the key characteristics of the front, i.e., front velocity and characteristic length scales. The second part of this study focuses on the prediction of the temperature spike associated with the merger of two polymerization fronts. The thermal peak, which might be detrimental to the properties of the polymerized material, is due to the inability of the heat associated with the highly exothermic reaction to be dissipated when the two fronts merge. The analysis investigates how the amplitude of the thermal spike is affected by the degree of cure at the time of the front merger.
