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<p>Grain size distribution is a critical factor in determining materials’ physical and mechanical properties, including thermal conductivity, hardness, and creep behavior. Understanding the distribution of grain sizes is essential for advancing the comprehension of material properties and improving materials development and design. Traditional methods for determining grain size, such as electron backscatter diffraction (EBSD), are resource-intensive, underscoring the need for more efficient approaches to grain segmentation in standard micrographs, such as those obtained via SEM and optical imaging. This paper presents a streamlined, unsupervised computer vision pipeline that employs superpixel segmentation and region adjacency merging techniques to segment and measure grain geometry from micrographs efficiently. The pipeline is validated using two methods: hand-labeled SEM images of laser powder bed fusion (LPBF) fabricated Haynes 282 Ni-alloy and open-source EBSD data of IN100 from Dream3D. Both validation approaches achieved IoU and Dice scores greater than 0.9, while processing an image with a resolution of 1000 × 1000 pixels in under 40&#xa0;s, demonstrating a fast and sufficiently accurate pipeline.</p>

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Rapid Grain Segmentation of Heat-treated and Annealed LPBF Haynes 282 Using an Unsupervised Learning-Based Computer Vision Approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月20日

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  • Yu-Tsen Yi,
  • Junwon Seo,
  • Kevin Murphy,
  • Anthony Rollett

摘要

Grain size distribution is a critical factor in determining materials’ physical and mechanical properties, including thermal conductivity, hardness, and creep behavior. Understanding the distribution of grain sizes is essential for advancing the comprehension of material properties and improving materials development and design. Traditional methods for determining grain size, such as electron backscatter diffraction (EBSD), are resource-intensive, underscoring the need for more efficient approaches to grain segmentation in standard micrographs, such as those obtained via SEM and optical imaging. This paper presents a streamlined, unsupervised computer vision pipeline that employs superpixel segmentation and region adjacency merging techniques to segment and measure grain geometry from micrographs efficiently. The pipeline is validated using two methods: hand-labeled SEM images of laser powder bed fusion (LPBF) fabricated Haynes 282 Ni-alloy and open-source EBSD data of IN100 from Dream3D. Both validation approaches achieved IoU and Dice scores greater than 0.9, while processing an image with a resolution of 1000 × 1000 pixels in under 40 s, demonstrating a fast and sufficiently accurate pipeline.