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Accelerating Microstructure Recognition of Nickel-Based Superalloy Data by UNet++ 

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Advances in Intelligent Automation and Soft Computing (IASC 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 80))

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Abstract

In recent years, the field of alloys has been developed rapidly under the high-throughput experiments. At the same time, the research and application of superalloy microstructure have become a very important part in the field of alloys. However, it is difficult to deal with the massive images with inconsistent brightness and contrast by conventional methods, which limit the development of superalloy microstructure research. In this paper, combining the traditional threshold segmentation, we propose a microstructure segmentation method based on UNet++, which circumvents the large amount of labeled training data that would need intensive labor. This integrated approach improves efficiency and accuracy compared with traditional methods, and can be applied to many other fields and data.

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Acknowledgment

This work is funded in part by the Fundamental Research Funds for the Central Universities of China under Grant 2662020LXQD002 and Grant 2662019FW003, in part by Students Research Fund under Grant 2021356, in part by the Hubei Key Laboratory of Applied Mathematics under Grant HBAM 202004 and in part by the Natural Science Foundation of China under Grant 11771130.

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Authors and Affiliations

  1. College of Science, Huazhong Agricultural University, 430070, Wuhan, China

    Kang Jia, Weifu Li & Zhelun Wang

  2. Hubei Key Laboratory of Applied Mathematics (Hubei University), Wuhan, 430062, China

    Weifu Li

  3. The State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China

    Zijun Qin

Authors
  1. Kang Jia
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  2. Weifu Li
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  3. Zhelun Wang
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  4. Zijun Qin
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Corresponding author

Correspondence to Weifu Li .

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Editors and Affiliations

  1. Electronics and Computer Engineering Technology, Indiana State University, Terre Haute, IN, USA

    Xiaolong Li

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Jia, K., Li, W., Wang, Z., Qin, Z. (2022). Accelerating Microstructure Recognition of Nickel-Based Superalloy Data by UNet++ . In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_99

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