The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

• Published in: Composites. Part A, Applied science and manufacturing Applied science and manufacturing, 2024-12, Vol.187 (108491), p.108491, Article 108491
• Main Authors: Larson, Richard A., Nazmus Saquib, Mohammad, Li, Jiang, Favaloro, Anthony J., Sommer, Drew E., Denos, Benjamin R., Byron Pipes, R., Kravchenko, Sergii G., Kravchenko, Oleksandr G.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Fiber Orientation; Non-destructive Inspection; Prepreg Platelets
• ISSN: 1359-835X
• DOI: 10.1016/j.compositesa.2024.108491

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. The MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10 %. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11 %. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.