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

Journal Article · 23 September 2024 · Composites. Part A, Applied Science and Manufacturing

DOI: https://doi.org/10.1016/j.compositesa.2024.108491 · OSTI ID:2472748

Larson, Richard A. ^[1]; Nazmus Saquib, Mohammad ^[1]; Li, Jiang ^[1]; Favaloro, Anthony J. ^[2]; Sommer, Drew E. ^[3]; Denos, Benjamin R. ^[4]; Byron Pipes, R. ^[4]; Kravchenko, Sergii G. ^[5]; Kravchenko, Oleksandr G. ^[1]

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1. Old Dominion Univ., Norfolk, VA (United States)
2. Hexagon Manufacturing Intelligence, Inc., Huntsville, AL (United States)
3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
4. Purdue Univ., West Lafayette, IN (United States)
5. Univ. of British Columbia, Vancouver, BC (Canada)

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. Further, 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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 2472748
• Report Number(s): SAND--2024-14055J
• Journal Information: Composites. Part A, Applied Science and Manufacturing, Journal Name: Composites. Part A, Applied Science and Manufacturing Journal Issue: 108491 Vol. 187; ISSN 1359-835X
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English