段老师在 Materials 期刊发表论文

段老师参与完成的题目为“Improved Grain Boundary Reconstruction Method Based on Channel Attention Mechanism”的论文被期刊Materials接收。段老师为论文第一通讯作者。

本文介绍了一种基于改进通道注意力机制的晶界重构与晶粒度测量方法，旨在解决金属材料原始金相图像中常见的噪声严重、晶界缺失、对比度低以及边缘模糊等问题。这些问题限制了晶界的准确提取，从而影响晶粒度测量的精度和材料性能预测。研究以生成对抗网络（GAN）为核心，嵌入自定义设计的通道注意力模块，并结合全局上下文注意力机制，增强了网络对图像中晶界缺失区域的语义理解与重构能力。在图像重构过程中，该方法有效利用了图像中的长距离特征关联，显著提升了网络性能。此外，为解决实验中的模式崩塌问题，优化了损失函数，引入Focal Loss以平衡正负样本比例，提高了网络的鲁棒性。实验表明，改进的通道注意力模块在生成网络性能方面表现优异，其在MIoU（86.25%）、准确率（95.06%）和精度（86.54%）等指标上均优于其他模块。该方法不仅有效提升了晶界重构的准确性，还显著增强了网络的泛化能力，为金属材料微观结构表征与性能预测提供了可靠的技术支持。

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该文章基本信息如下：

  • 标题：Improved Grain Boundary Reconstruction Method Based on Channel Attention Mechanism
  • 作者：Xianyin Duan, Yang Chen, Xianbao Duan*, Zhijun Rong*, Wunan Nie and Jinwei Gao

  • 摘要：The grain size of metal materials has a significant impact on their macroscopic properties. However, original metallographic images often suffer from issues such as substantial noise, missing grain boundaries, low contrast, and blurred edges. These challenges hinder the accurate extraction of complete grain boundaries, limiting the precision of grain size measurement and material performance prediction. Therefore, effectively reconstructing incomplete grain boundaries is particularly crucial. This paper proposes a grain boundary reconstruction and grain size measurement method based on an improved channel attention mechanism. A generative adversarial network (GAN) serves as the backbone, with a custom-designed channel attention module embedded in the generator. Combined with a global context attention mechanism, the method captures the global contextual information of the image, enhancing the network’s semantic understanding and reconstruction accuracy for regions with missing grain boundaries. During the image reconstruction process, the method effectively leverages long-range feature correlations within the image, significantly improving network performance. To address the Mode Collapse observed during experiments, the loss function is optimized using Focal Loss, balancing the ratio of positive and negative samples and improving network robustness. Compared with other attention modules, the improved channel attention module significantly enhances the performance of the generative network. Experimental results demonstrate that the generative network based on this module outperforms comparable modules in terms of MIoU (86.25%), Accuracy (95.06%), and Precision (86.54%). The grain boundary reconstruction method based on the improved channel attention mechanism not only effectively improves the accuracy of grain boundary reconstruction but also significantly enhances the generalization ability of the network. This provides reliable technical support for the characterization of the microstructure and the performance prediction of metal materials.

  • 期刊：Materials

  • 链接：https://www.mdpi.com/1996-1944/18/2/253

Materials是中科院材料大类3区期刊，2024年影响因子为3.1。