Title: Reconstruction of Internal Morphology in Molded Platelet Composite Using Residual Stresses and Deep Convolutional Neural Network
Authors: Mohammad N. Saquib, Richard Larson, Jiang Li, Sergii G. Kravchenko, Oleksandr G. Kravchenko
DOI: 10.33599/nasampe/c.24.0282
Abstract: Compression molding of discontinuous long fiber composite materials is crucial for meeting industry demands for lightweight, durable products manufactured at a high rate. However, the variability in orientation state of prepreg platelet molded composites, resulting from stochastic molding conditions, poses challenges to reliability. This study introduces a novel computational tool aimed at rapid non-destructive reconstruction of fiber orientation distribution (FOD) in each layer in prepreg platelet molded composite (PPMC) plates. While traditional micro CT would require significant time to generate layer based FOD, a deep convolutional neural network (DCNN) can achieve an order of magnitude increase in part inspection speed. The DCNN utilizes thermal residual strain components on the surface as input to predict FOD throughout the volume. The neural network was trained using thousands of synthetic PPMC morphologies from finite element simulation data. The trained DCNN was used to reconstruct the microstructure of the entire PPMC synthetic coupon. Progressive failure analysis (PFA) was performed on the reconstructed tensile coupons and compared to the PFA simulation of the initial synthetic morphology, which was considered as a ground truth. The failure analysis of the reconstructed PPMC coupon showed 14.53% error in predicting a tensile strength. Furthermore, the observed damage modes and failure locations closely matched between the true and reconstructed morphology, demonstrating that the proposed DCNN model is capable of full morphology reconstruction in PPMC materials.
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