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.
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This work is supported by the National Science Foundation (Grant No. 2119832).
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Satterlee, N., Jiang, R., Olevsky, E. et al. Robust image-based cross-sectional grain boundary detection and characterization using machine learning. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02383-6
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DOI: https://doi.org/10.1007/s10845-024-02383-6