激光焊匙孔特征的近红外与X射线传感分析

高向东; 李竹曼; 游德勇; 张南峰

doi:10.3788/OPE.20162410.2400

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激光焊匙孔特征的近红外与X射线传感分析

现代应用光学 | 更新时间：2020-07-07

- 激光焊匙孔特征的近红外与X射线传感分析
- Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing
- 光学精密工程 2016年24卷第10期页码：2400-2407
- 作者机构：
  
  广东工业大学机电工程学院, 广东广州 510006
- 作者简介：
  
  高向东(1963-),男,河南郑州人,教授,博士生导师,1998年于华南理工大学获得博士学位,研究方向为焊接自动控制。E-mail:gaoxd666@126.com
  [ "李竹曼(1991-),女,湖北荆州人,硕士研究生,主要从事激光焊接技术方面的研究。E-mail:lzm_0805@163.com" ]
- 基金信息：
  
  广东省重大科技项目专项资金资助项目(No.2014B090921008);广州市科学研究专项资金资助项目(No.201510010089);广东省协同创新与平台环境建设专项资金资助项目(No.2015B090901013);广东省科技发展专项资金资助项目(No.2016A010102015);佛山市科技创新专项资金资助项目(No.2014AG10015)
- DOI：10.3788/OPE.20162410.2400
  中图分类号： TG456.7
- 收稿日期：2016-06-22，
  
  录用日期：2016-8-12，
  
  纸质出版日期：2016-10
- 稿件说明：
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高向东, 李竹曼, 游德勇, 等. 激光焊匙孔特征的近红外与X射线传感分析[J]. 光学精密工程, 2016,24(10):2400-2407.

Xiang-dong GAO, Zhu-man LI, De-yong YOU, et al. Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing[J]. Optics and precision engineering, 2016, 24(10): 2400-2407.
高向东, 李竹曼, 游德勇, 等. 激光焊匙孔特征的近红外与X射线传感分析[J]. 光学精密工程, 2016,24(10):2400-2407. DOI： 10.3788/OPE.20162410.2400.

Xiang-dong GAO, Zhu-man LI, De-yong YOU, et al. Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing[J]. Optics and precision engineering, 2016, 24(10): 2400-2407. DOI： 10.3788/OPE.20162410.2400.

摘要

由于多传感匙孔特征参数可以有效地反映大功率激光焊接质量状态，本文研究了匙孔特征信息的提取方法并建立了焊缝成形预测模型。以大功率盘形激光焊接304不锈钢为试验对象，应用近红外高速摄像机和X射线视觉成像系统同时提取了焊接过程中的熔池动态图像，并分割出匙孔区域。针对近红外图像，应用矩方法导出匙孔的不变矩特征，同时定义并提取匙孔面积和最前端点纵坐标两个特征；针对X射线图像则提取匙孔深度和熵两个特征。在不同激光功率条件下得到匙孔特征并进行特征融合分析，然后建立了3个BP神经网络焊缝成形预测模型。探索了匙孔形态、焊接条件和焊接状态三者之间的联系，实现了对焊接过程的在线监测。试验结果表明，将两个传感器获取的匙孔特征信息融合并进行主成分分析变换后，熔宽和熔深的预测绝对误差平均值分别为0.18 mm和0.57 mm，比基于单个传感器获取匙孔特征建立的BP神经网络分别减小了0.03 mm和0.31 mm，显示提出的方法能够有效在线监测大功率盘形激光焊接状态。

Abstract

As the characteristic parameters of a multi-sensing keyhole reflect effectively the welding quality of high power lasers

this paper researches the extraction method of keyhole characteristic information and establishes a prediction model for welding formation. By taking a high power disk laser to weld 304 austenitic stainless steel plates for an example

a near-infrared high-speed camera and an X-ray vision imaging system were used to capture the molten images in welding processing and to obtain the keyhole region by image processing. The invariant moment characteristics were extracted from near-infrared visual images by the moment method

meanwhile the keyhole area and ordinate value of the keyhole forefront were calculated as the characteristic parameters. Depth and entropy of the keyhole were extracted from X-ray visual images. In different laser powers

the keyhole characteristics were obtained and three BP (Back Propagation) neural network models were set up through feature fusion of all the characteristic parameters. The relationship between the keyhole formation

welding condition and welding state was explored and the on-line monitoring for welding process was implemented. Experimental results show that the average absolute value of relative errors between predictive and measured values of weld width and penetration are 0.18 mm and 0.57 mm

respectively through fusion analysis and principal component analysis on characteristic parameters of two sensors

and they have been reduced by about 0.03 mm and 0.31 mm as compared with that of a single sensor. The proposed method can be applied to monitoring high-power disk laser welding quality in real time.

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references

崔丽,李晓延,贺定勇,等. 5A90铝锂合金激光焊焊缝微观组织特征[J]. 焊接学报,2010,31(9):77-80.

CUI L, LI X Y, HE D Y, et al.. Microstructure investigation of Nd:YAG laser welded 5A90 aluminum-lithium alloys[J]. Transactions of The China Welding Institution, 2010, 31(9):77-80. (in Chinese)

陈玉华,戈军委,刘奋成,等. TiNi形状记忆合金/钛合金异种材料激光焊[J]. 光学精密工程,2014,22(8):2075-2080.

CHEN Y H, GE J W, LIU F CH, et al.. Micro laser welding of dissimilar materials between TiNi shape memory alloy and titanium alloy[J]. Opt. Precision Eng., 2014, 22(8):2075-2080. (in Chinese)

GAO X D, SUN Y. Monitoring of high-power disk laser welding of type 304 austenitic stainless steel based on keyhole dynamic characteristics[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2014, 56(6):312-317.

汪任凭, 雷永平, 史耀武. 激光深熔焊中匙孔形成过程的动态模拟[J]. 焊接学报, 2010, 31(11):38-40.

WANG R P, LEI Y P, SHI Y W. Numerical simulation of keyhole formation process in laser deep penetration welding[J]. Transactions of The China Welding Institution, 2010, 31(11):38-40. (in Chinese)

ZHANG T, WU C S, FENG Y H. Numerical analysis of heat transfer and fluid flow in key hole plasma arc welding[J]. Numer. Heat Tr. A-Appl., 2011,60(8):685-698.

ZOU J L, WU S K, YANG W X, et al.. A novel method for observing the micro-morphology of keyhole wall during high-power fiber laser welding[J]. Mater. Design, 2015, 89:785-790.

KIM J, DENG C, OH S, et al.. Electro dynamic simulation of energy absorption in laser keyhole welding of zinc-coated and uncoated steel sheets[J]. J. Mater. Process. Tech., 2016, 231:412-421.

MENG C, LU F G, CUI H C, et al.. Research on formation and stability of keyhole in stationary laser welding on aluminum MMCs reinforced with particles[J]. Int. J. Adv. Manuf. Tech., 2013, 67(9):2917-2925.

YOU D Y, GAO X D, KATAYAMA S. Multiple-optics sensing of high-brightness disk laser welding process[J]. NDT & E International, 2013, 60(2):32-39.

杨舒, 王玉德. 基于Contourlet变换和Hu不变矩的图像检索算法[J]. 红外与激光工程, 2014, 43(1):306-310.

YANG SH, WANG Y D. Image retrieval algorithm based on Contourlet transform and Hu invariant moments[J]. Infrared Laser Eng., 2014, 43(1):306-310. (in Chinese)

WU G, WU CH F, HOU Q Y, et al.. Target location method based on invariable moment feature matching[J]. Opt. Precision Eng., 2009, 17(2):460-468.

曹建农. 图像分割的熵方法综述[J]. 模式识别与人工智能,2012,25(6):958-971.

CAO J N. Review on image segmentation based on entropy[J]. Patt. Recogn. Artif. Intell., 2012, 25(6):958-971. (in Chinese)

李海森,张艳宁,姚睿,等. 基于主成分分析的直线运动模糊参数估计[J]. 光学精密工程,2013,21(10):2656-2663.

LI H S, ZHANG Y N, YAO R, et al.. Parameter estimation of linear motion blur based on principal component analysis[J]. Opt. Precision Eng., 2013, 21(10):2656-2663. (in Chinese)

GAO X D, SUN Y, KATAYAMA S. Neural network of plume and spatter for monitoring high-power disk laser welding[J]. Int. J. Precis. Eng. Man., 2014, 1(4):293-298.

秦国华,谢文斌,王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程,2015,23(5):1314-1321.

QING G H, XIE W B, WANG H M. Detection and control for tool wear based on neural network and genetic algorithm[J]. Opt. Precision Eng., 2015, 23(5):1314-1321. (in Chinese)

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