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Two protocols for multistep grain segmentation and analysis workflow in optical microscopy images of cubic boron nitride materials were developed and compared. One is based on statistical region merging and second one on morphological segmentation of grains without high contrast borders. Judging from corresponding manual image segmentation by expert, the second method gave more accurate grain boundaries and better statistical correspondence. Then, using the morphological segmentation method and incorporating of parameter optimization into it, a grain analysis workflow was established. Deviations from the correct answer (expert segmentation) were quantified based on five geometric statistical indices, and these deviations were added together to define the overall error. Cross-validation confirmed that the morphological segmentation workflow reproduces the expert segmentation with smaller 9.4% margin of error compared to 23.9% with statistical region merging one. The automated grain segmentation of such challenging materials with high throughput image analysis is an important help for industrial development of new milling tools.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: metallurgy, image analysis, microstructures, optical microscopy, grain recognition, cBN sintered compacts

刊行年月日: 2024-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 4 issue. 1

研究助成金:

• 材料モデリング Construction of Materials Fundamentals for Data-driven Research AG3030
• 積層材料 Structural Materials for Extreme Environments toward a Carbon Neutral Society AD2070
• 三菱マテリアル／袖山慶太郎 GSG011

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2024.2423599

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更新時刻: 2024-12-17 16:30:54 +0900

MDRでの公開時刻: 2024-12-17 16:30:54 +0900