液体火箭发动机推力室熔焊缝数字化胶片图像缺陷识别方法研究

任文坚; 王永红; 李春凯; 石玗; 孙忠诚; 秦建刚

doi:10.7512/j.issn.1001-2303.2023.01.02

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液体火箭发动机推力室熔焊缝数字化胶片图像缺陷识别方法研究
Research on Automatic Detection Method of DR Digital imaging for Brazing Seam Defects of Thrust Chamber Body
2023年53卷第1期页码：9-14
DOI： 10.7512/j.issn.1001-2303.2023.01.02

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任文坚，王永红，李春凯，等.液体火箭发动机推力室熔焊缝数字化胶片图像缺陷识别方法研究［J］.电焊机，2023，53（1）：9-14.

REN Wenjian, WANG Yonghong, LI Chunkai, et al.Research on Automatic Detection Method of DR Digital imaging for Brazing Seam Defects of Thrust Chamber Body[J].Electric Welding Machine, 2023, 53(1): 9-14.
任文坚，王永红，李春凯，等.液体火箭发动机推力室熔焊缝数字化胶片图像缺陷识别方法研究［J］.电焊机，2023，53（1）：9-14. DOI： 10.7512/j.issn.1001-2303.2023.01.02.

REN Wenjian, WANG Yonghong, LI Chunkai, et al.Research on Automatic Detection Method of DR Digital imaging for Brazing Seam Defects of Thrust Chamber Body[J].Electric Welding Machine, 2023, 53(1): 9-14. DOI： 10.7512/j.issn.1001-2303.2023.01.02.

摘要

推力室的焊接质量可靠性与稳定性对于发动机服役性能以及火箭安全运行具有非常重要的意义，主要通过人工评定胶片图像来识别其焊缝缺陷，检测效率低、经验依赖性强。针对液体火箭发动机推力室胶片数字化图像的特点，通过对原始图像进行尺寸归一化、图像增强等图像预处理构建图像样本集，并采用YOLO V3算法建立了基于深度学习理论的熔焊缝缺陷自识别模型。结果表明，训练的YOLO神经网络模型能够准确识别胶片数字化图像中气孔、裂纹、未熔合、未焊透等4种典型熔焊缝缺陷，识别准确率达90%以上，有良好的工业应用前景。

Abstract

As one of the most important components of a liquid rocket engine, the reliability and stability of welding quality are very important for the service performance of the engine and the reliable and safe operation of the rocket.In actual production, the weld defects are mainly identified by manual evaluation of film images, which generally have shortcomings such as low detection efficiency and strong dependence on manual experience. According to the characteristics of the digitized image of the liquid rocket engine thrust chamber film, the image sample set was constructed by performing image preprocessing such as size normalization and image enhancement on the original image.The YOLO algorithm was used to establish a self-identification model of fusion weld defects based on deep learning theory.The research results show that the trained YOLO neural network model can accurately identify four typical fusion weld defects such as pores, cracks, lack of fusion, and lack of penetration in digital film images, and the recognition accuracy can reach more than 90%. which has good industrial application prospects.

关键词

无损检测钎焊缺陷X射线数字成像深度学习

Keywords

non-destructive testingbrazing defectsX-Ray digital radiographydeep learning

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