Automated generation of labeled synthetic training data for machine learning based segmentation of 3D-woven composites

Written by: Johan Friemann, Lars P. Mikkelsen, Carolyn Oddy and Martin Fagerström

Published on: July, 2024

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Summary

A novel pipeline for the generation of synthetic tomograms of woven composite materials, to be used for training of machine learning based segmentation algorithms is presented. The pipeline is completely based on open source software and heavily utilizes the graphical processing unit for fast data generation. The proposed method generates a surface mesh of the woven geometry, scans it, reconstructs the scan, and generates a voxel labeling of the generated tomogram. It is demonstrated that the method can generate images that show good agreement with experimentally produced x-ray computed tomography images of a 3D-woven carbon fiber reinforced polymer composite.

Publication

Friemann, J., Mikkelsen, L. P., Oddy, C., & Fagerström, M. (2024). Automated generation of labeled synthetic training data for machine learning based segmentation of 3D-woven composites. Proceedings of the 21st European Conference on Composite Materials: Special sessions (pp. 333–338). doi:10.60691/yj56-np80

Citation

@inproceedings{Joahn2024ECCM,
    author = {Friemann, Johan and Mikkelsen, Lars P. and Oddy, Carolyn and Fagerstr\"{o}m, Martin},
    title = "Automated generation of labeled synthetic training data for machine learning based segmentation of {3D}-woven composites",
    booktitle = "Proceedings of the 21st European Conference on Composite Materials: Special sessions",
    year = 2024,
    volume = 8,
    pages = {333-338},
    doi = {10.60691/yj56-np80}
}