Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite

Abstract A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to non-destructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of PPMC plate. Optical microscopy was then used to measure FOD in the cross-section of PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, non-destructive inspection of manufacturing induced FOD in molded composites.