大功率盘形激光焊焊缝背面宽度预测

陈子琴; 高向东; 王琳

doi:10.3788/OPE.20172509.2524

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大功率盘形激光焊焊缝背面宽度预测

信息科学 | 更新时间：2020-08-13

- 大功率盘形激光焊焊缝背面宽度预测
- Weld width prediction of weldment bottom surface in high-power disk laser welding
- 光学精密工程 2017年25卷第9期页码：2524-2531
- 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：
  
  [ "陈子琴(1989-), 女, 甘肃兰州人, 博士研究生, 2007年于兰州理工大学获得学士学位, 主要从事焊接自动控制和图像处理技术等方面的研究.E-mail:chenzq_gdut@163.com" ]
  [ "高向东(1963-), 男, 河南郑州人, 教授, 博士研究生导师, 1985年于郑州大学获得学士学位, 1988年于中南大学获得硕士学位, 1998年于华南理工大学获得博士学位, 主要从事焊接自动控制的研究.E-mail:gaoxd666@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(51675104);广东省科技计划资助项目(2016A010102015);广州市科技计划资助项目(201510010089)
- DOI：10.3788/OPE.20172509.2524
  中图分类号： TG441.3
- 收稿日期：2017-03-06，
  
  录用日期：2017-5-17，
  
  纸质出版日期：2017-09-25
- 稿件说明：
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陈子琴, 高向东, 王琳. 大功率盘形激光焊焊缝背面宽度预测[J]. 光学精密工程, 2017,25(9):2524-2531.

Zi-qin CHEN, Xiang-dong GAO, Lin WANG. Weld width prediction of weldment bottom surface in high-power disk laser welding[J]. Optics and precision engineering, 2017, 25(9): 2524-2531.
陈子琴, 高向东, 王琳. 大功率盘形激光焊焊缝背面宽度预测[J]. 光学精密工程, 2017,25(9):2524-2531. DOI： 10.3788/OPE.20172509.2524.

Zi-qin CHEN, Xiang-dong GAO, Lin WANG. Weld width prediction of weldment bottom surface in high-power disk laser welding[J]. Optics and precision engineering, 2017, 25(9): 2524-2531. DOI： 10.3788/OPE.20172509.2524.

摘要

提出了通过视觉传感获取焊接过程中的焊接特征信息并利用神经网络模型预测焊缝背面宽度的方法。利用大功率盘形激光器焊接了低碳钢SS400焊件，在焊接过程中改变焊接功率、焊接速度和焊接路径，并利用两台高速摄像机同步获取焊件正面和侧面出现的焊接特征信息。对获取的图像进行色彩空间转换、分层、滤波去噪和空域图像处理，提取飞溅、熔池和金属蒸气等焊接特征信息，观察焊接路径对各个特征的影响。最后，建立了一个三层的LMBP（Levenberg-Marquardt Back Propagation）神经网络模型，将提取的特征信息作为输入量，预测焊缝的背面宽度。结果显示：当熔透不稳定或出现未熔透状态时，LMBP神经网络拟合度大于0.83，最大训练误差均值为0.0028 mm，最大实际误差均值为0.2256 mm。试验结果表明所建立的预测模型具有良好的准确性和稳定性。

Abstract

A method was proposed to obtain characteristic information in a welding process by visual sensing and to predict the weld width of weldment bottom surface by using a neural network model. A workpiece made from mild steel SS400 was welded by a high power disk laser. In welding processing

the weld conditions were changed

including laser welding power

welding speed and welding route and two high speed cameras were used to capture images containing characteristic information on both top surface and side surface of weldment simultaneously. In order to get a better characteristics extraction

the colour space of a RGB image was changed into NTSC (National Television Standards Committee) colour space

then both RGB image and YIQ image were separated into their colour components

filtered to denoising and processed in space domain. The weld characteristic information was extracted

including spatter

weld pool and metal vapour and the effect of weld route on characteristic information was researched. Finally

a LMBP (Levenberg-Marquardt Back Propagation) neural network model including three layers and one hidden layer was established. The obtained characteristic information was taken as input

and the weld width of weldment bottom surface was predicted. The results show that when the welding penetration is unstable or lack of penetration

the fitting degree of LMBP neural network is greater than 0.83

the maximum training error mean is 0.002 8 mm

and maximum actual error mean is 0.225 6 mm. It concludes that the prediction model has good accuracy and stability.

关键词

Keywords

references

KATAYAMA S, KAWAHITO Y, MIZUTANI M. Elucidation of laser welding phenomena and factors affecting weld penetration and welding defects [J]. Physics Procedia, 2010, 5:9-17.

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

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.

GAO X D, LI ZH M, YOU D Y, et al.. Analysis of laser welding keyhole characteristics based on near-infrared high speed camera and X-ray sensing [J]. Opt. Precision Eng., 2016, 24(10):2400-2407. (in Chinese)

KIM J, KI H. Scaling law for penetration depth in laser welding [J]. Journal of Materials Processing Technology, 2014, 214(12):2908-2914.

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]. Materials & Design, 2016, 89:785-790.

LI S C, CHEN G Y, ZHOU C. Effects of welding parameters on weld geometry during high-power laser welding of thick plate [J]. The International Journal of Advanced Manufacturing Technology, 2015, 79(1-4):177-182.

LI S C, CHEN G Y, KATAYAMA S, et al.. Relationship between spatter formation and dynamic molten pool during high-power deep-penetration laser welding [J]. Applied Surface Science, 2014, 303:481-488.

ZHANG Y X, GAO X D. Analysis of characteristics of molten pool using cast shadow during high-power disk laser welding [J]. The International Journal of Advanced Manufacturing Technology, 2014, 70(9-12):1979-1988.

MIRAPEIX J, COBO A, CONDE O M, et al.. Real-time arc welding defect detection technique by means of plasma spectrum optical analysis [J]. NDT & E International, 2006, 39(5):356-360.

HAO K D, LI G, GAO M, et al.. Weld formation mechanism of fiber laser oscillating welding of austenitic stainless steel [J]. Journal of Materials Processing Technology, 2015, 225:77-83.

MUNGLE T, TEWARY S, ARUN I, et al.. Automated characterization and counting of Ki-67 protein for breast cancer prognosis:A quantitative immunohistochemistry approach [J]. Computer Methods and Programs in Biomedicine, 2017, 139:149-161.

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