密封焊缝激光堆焊修复金属材料表面缺陷的检测

IF 0.6 Q4 ENGINEERING, MECHANICAL
Weiyong Wang
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引用次数: 0

摘要

密封焊缝激光堆焊修复技术在金属修复中得到了广泛的应用。由于焊接技术和使用场景的原因,金属表面的工艺缺陷是不可避免的。因此,超声波表面波技术被用于分析金属材料的表面缺陷。主成分分析(PCA)用于提取金属表面的主要缺陷信号,合成孔径聚焦技术用于降低成像误差。考虑到PCA在缺陷成像中的不足,结合小波域隐马尔可夫模型(WHMM)对信号进行优化,从而提高了金属缺陷的检测效果。在316 L钢传播距离与缺陷回波信号关系的测试结果中,回波信号随着传播距离的增加而逐渐拟合。当传播距离大于10mm时,图像采集缺陷信号具有显著的噪声点。使用各种技术来处理金属表面缺陷的原始回波信号。改进后的PCA-WHMM算法具有显著的优势,缺陷图像的信噪比比比PCA-WHHM提高了13.65%。同时,比较了优化前后激光堆焊316L金属的表面修复效果。优化后的金属的硬度、韧性和耐腐蚀性显著提高。所提出的技术创新将传统激光堆焊修复与深度学习故障诊断相结合,不仅大大提高了故障诊断的效率,而且证明了本研究可以有效避免激光堆焊修复技术的共同焦点问题,为超声波技术在金属缺陷检测中的应用提供了重要的技术参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surface defects detection in metal materials repaired by laser surfacing of seal welds
Laser surfacing repair technology for sealing welds is widely used in metal repair. Due to welding technology and usage scenarios, process defects on the metal surface are inevitable. Therefore, ultrasonic surface wave technology is used to analyze the surface defects of metal materials. Principal Component Analysis (PCA) is used to extract the main defect signals on the metal surface, and synthetic aperture focusing technology is used to reduce imaging errors. Considering the lack of PCA in imaging defects, wavelet domain hidden Markov models (WHMM) are combined to optimize the signal, thereby improving the inspection effect of metal defects. In the test results of the relationship between the propagation distance of 316 L steel and the defect echo signal, the echo signal gradually fitted as the propagation distance increased. When the propagation distance was greater than 10 mm, the image acquisition defect signal had significant noise points. Various techniques were used to process the original echo signals of metal surface defects. The improved PCA-WHMM algorithm had significant advantages with the SNR value of the defect image increased by 13.65 % compared to PCA-WHMM. At the same time, the surface repair effects of laser surfacing 316 L metal before and after optimization were compared. The hardness, toughness, and corrosion resistance of the optimized metal were significantly improved. The proposed technological innovation combines traditional laser surfacing repair with deep learning fault diagnosis, which not only greatly improves the efficiency of fault diagnosis, but also proves that this research can effectively avoid common focus issues of laser surfacing repair technology, providing important technical reference for the application of ultrasonic technology in metal defect detection.
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来源期刊
Journal of Measurements in Engineering
Journal of Measurements in Engineering ENGINEERING, MECHANICAL-
CiteScore
2.00
自引率
6.20%
发文量
16
审稿时长
16 weeks
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