Tanaka Marino, Sasaki Kai, Punyafu Jesada, Muramatsu Mayu, Murayama Mitsuhiro
Department of Science for Open and Environmental Systems, Graduate School of Keio University, 3-14-1, Yokohama, Kanagawa, 233-8522, Japan.
Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan.
Sci Rep. 2025 Apr 25;15(1):14435. doi: 10.1038/s41598-025-99319-8.
Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. Dynamic Transmission Electron Microscopy (TEM) captures crystal defects in materials undergoing plastic deformation, generating vast datasets with high temporal and spatial resolution. However, manual analysis of these images is labor-intensive, and automated, unbiased analysis remains a challenge. In this study, we developed a U-net-based machine learning approach to analyze TEM videos of crystal defect evolution in Twinning-Induced Plasticity (TWIP) steels with different grain sizes. The method overcame challenges like field-of-view translation and nonuniform defect motion. This approach quantitatively measured defect evolution as a function of time and strain with the same temporal resolution as the original videos, detecting even minor changes with high accuracy. We use this technique to quantitatively reveal the switch of the dominant plastic deformation mechanism with grain size and the relaxation of elastic strain due to the rapid increase in stacking faults. Our results validate the use of U-net models for efficient semantic segmentation of TEM videos, enabling accurate quantitative analysis. This work advances TEM video analysis and provides new insights into the deformation mechanisms of materials.
建立有效的方法来获取晶体缺陷演变的定量数据对于理解材料性能至关重要。动态透射电子显微镜（TEM）捕捉正在经历塑性变形的材料中的晶体缺陷，生成具有高时间和空间分辨率的大量数据集。然而，对这些图像进行人工分析劳动强度大，而自动化、无偏差的分析仍然是一个挑战。在本研究中，我们开发了一种基于U-net的机器学习方法，用于分析不同晶粒尺寸的孪生诱导塑性（TWIP）钢中晶体缺陷演变的TEM视频。该方法克服了诸如视野平移和缺陷运动不均匀等挑战。这种方法以与原始视频相同的时间分辨率定量测量缺陷演变作为时间和应变的函数，能够高精度地检测到即使是微小的变化。我们使用这种技术定量揭示了主导塑性变形机制随晶粒尺寸的转变以及由于堆垛层错的快速增加导致的弹性应变弛豫。我们的结果验证了U-net模型用于TEM视频有效语义分割的有效性，从而实现准确的定量分析。这项工作推动了TEM视频分析的发展，并为材料的变形机制提供了新的见解。
