ChemNet: A Deep Neural Network for Advanced Composites Manufacturing.

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Abstract

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 time and energy. However, the cure kinetics of the matrix phase, usually a thermosetting polymer, brings difculty to the design and control of the process. Here, we develop a deep learning model, ChemNet, to solve an inverse problem in 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 square error (MSE) and by the maximum absolute error metrics. The MSE of ChemNet, on the train set and test set attain the values of 1E-4 and 2E-4, respectively.