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Robust image-based cross-sectional grain boundary detection and characterization using machine learning

Author

Listed:
  • Nicholas Satterlee

    (San Diego State University)

  • Runjian Jiang

    (San Diego State University)

  • Eugene Olevsky

    (San Diego State University)

  • Elisa Torresani

    (San Diego State University)

  • Xiaowei Zuo

    (San Diego State University)

  • John S. Kang

    (San Diego State University)

Abstract

Understanding the anisotropic sintering behavior of 3D-printed materials requires massive analytic studies on their grain boundary (GB) structures. Accurate characterization of the GBs is critical to study the metallurgical process. However, it is challenging and time-consuming for sintered 3D-printed materials due to immature etching and residual pores. In this study, we developed a machine learning-based method of characterizing GBs of sintered 3D-printed materials. The developed method is also generalizable and robust enough to characterize GBs from other non-3D-printed materials. This method can be applied to a small dataset because it includes a diffusion network that generate augmented images for training. The study compared various machine learning methods commonly used for segmentation, which include UNet, ResNeXt, and Ensemble of UNets. The comparison results showed that the Ensemble of UNets outperformed the other methods for the GB detection and characterization. The model is tested on unclear GBs from sintered 3D-printed samples processed with non-optimized etching and classifies the GBs with around 90% accuracy. The model is also tested on images with clear GBs from literature and classifies GBs with 92% accuracy.

Suggested Citation

  • Nicholas Satterlee & Runjian Jiang & Eugene Olevsky & Elisa Torresani & Xiaowei Zuo & John S. Kang, 2025. "Robust image-based cross-sectional grain boundary detection and characterization using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3067-3095, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02383-6
    DOI: 10.1007/s10845-024-02383-6
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