Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing

Xiang-dong GAO; Zhu-man LI; De-yong YOU; Nan-feng ZHANG

doi:10.3788/OPE.20162410.2400

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Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing

Modern Applied Optic | 更新时间：2020-07-07

- Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing
- Optics and Precision Engineering Vol. 24, Issue 10, Pages: 2400-2407(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162410.2400
  CLC： TG456.7
- Received：22 June 2016，
  
  Accepted：12 August 2016，
  
  Published：2016-10
- 稿件说明：
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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：

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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