Three-dimensional quantitative characterization of defects in inconel 625 superalloy based on deep learning image identification

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外文摘要：Three-dimensional(3D)quantitative characterization of defects in superalloys is an important way to promote the ability of material design and service life prediction.In this work,3D spatial distribution of defects for Inconel 625 superalloy manufactured by laser additive manufacturing(LAM)is carried out deep learning(DL)image iden-tification technology and 3D image reconstruction.Firstly,computer tomography(CT)technology was used to obtain continuous slice images of sample.The U-net DL algorithm was applied to intelligently identify material defects in the continuous slices.On this basis,quantitative identification and analysis of spatial defect positions and typical sizes is achieved by using 3D reconstruction software.Compared with traditional threshold seg-mentation(TS)techniques,the defect recognition rate has significantly improved from 61.90％to 95.00％.This work provides a promising characterization method for efficient characterizing alloy defects and damage espe-cially during material performance evaluation.

外文关键词：

Inconel 625 superalloyDefects and damageThree-dimensional characterizationDeep learningImage identification

作者：

Wenyu Zhang、Qun Ren、Weihao Wan、Mengru Shi、Xiaofen Zhang、Lei Zhao、Lixia Yang、Cheng Zhong、Suran Liu、Song Chai、Yaxin Ma、Haizhou Wang

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作者单位：

National Center for Materials Service Safety,University of Science and Technology Beijing,Beijing,100083,China

Beijing Key Laboratory of Metal Materials Characterization,The NCS Testing Technology Co.,Ltd,Beijing,100081,China

Central Iron and Steel Research Institute Co.,Ltd.,Beijing,100081,China

School of Metallurgical Engineering,Xi'an University of Architecture and Technology,Xi'an,710055,China

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出版年：

2024

DOI：

10.1016/j.pnsc.2024.07.015

自然科学进展·国际材料(英文)

国家自然科学基金委员会

自然科学进展·国际材料(英文)

影响因子：0.25

ISSN：1002-0071

年,卷(期)：2024.34(5)