Extracting ductile cast iron microstructure parameters from fracture surfaces: A deep learning based instance segmentation approach

IF 4.7 2区 工程技术 Q1 MECHANICS
Johannes Rosenberger , Johannes Tlatlik , Carla Beckmann , Benedikt Rohrmüller , Sebastian Münstermann
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引用次数: 0

Abstract

This study investigates the deep-learning based microstructural analysis from SEM images of ductile cast iron fracture surfaces. A Mask R-CNN model was trained, achieving 70% precision and 75% recall in graphite particle detection. Combined with a fracture surface reconstruction using the.
4-quadrant backscattered electron signal, key parameters, including the particle size, shape and distance were extracted accurately. Compared to micrograph analysis, following probabilistic simulations showed the impact of the higher microstructural variance for the fracture surfaces on crack initiation, leading to higher scatter and elevated crack resistance curves. This highlights the potential of deep-learning based analysis for comprehensive microstructural characterization.
从断裂面提取球墨铸铁微观结构参数:基于深度学习的实例分割方法
本研究探讨了基于深度学习的球墨铸铁断口表面扫描电镜图像微观结构分析。研究人员训练了一个 Mask R-CNN 模型,该模型在石墨颗粒检测方面达到了 70% 的精确度和 75% 的召回率。结合使用 4象限反向散射电子信号进行的断裂表面重构,可以精确提取包括颗粒大小、形状和距离在内的关键参数。与显微照片分析相比,后续的概率模拟显示了断裂表面较高的微观结构差异对裂纹起始的影响,从而导致较高的散度和较高的裂纹阻力曲线。这凸显了基于深度学习的分析在综合微结构表征方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.70
自引率
13.00%
发文量
606
审稿时长
74 days
期刊介绍: EFM covers a broad range of topics in fracture mechanics to be of interest and use to both researchers and practitioners. Contributions are welcome which address the fracture behavior of conventional engineering material systems as well as newly emerging material systems. Contributions on developments in the areas of mechanics and materials science strongly related to fracture mechanics are also welcome. Papers on fatigue are welcome if they treat the fatigue process using the methods of fracture mechanics.
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