基于数字图像技术的高频电阻焊状态监测

State Monitoring of High-Frequency Electric Resistance Welding Based on Digital Image Technology

为实现对高频电阻焊焊接过程的在线实时监测,通过对焊接现象、焊接缺陷及其成因的分析,提出了一种基于高速CCD成像和数字图像处理的焊接过程自动化监测方法.该方法利用架设在焊接区域上部的高速CCD相机获取焊接区的数字图像,通过对焊接区图像的特征加热面积、V型开口角大小、焊接熔合点位置、加热区对称度等多个参数的提取和测量,获取焊接区的多特征参数,然后将这些参数进行数据融合,得到当前的焊接状态.在图像处理中应用了基于非平衡R-G-B填充的抗干扰分割算法、基于直线拟合的V型角开口角大小及角分线检测算法,设计了面积对称度评价函数及基于BP神经网络的多特征融合模型.通过现场实验发现:文中方法能够有效监测焊接区的状态,有利于焊接质量控制.

In order to realize the real-time and on-line state monitoring of the high-frequency electric resistance welding（HF-ERW）,according to the analytical results of various welding phenomena as well as the welding defects and their causes,an automatic monitoring method based on the high-speed CCD imaging and the digital image processing is proposed.In this method,digital images of the welding region are obtained by using a CCD camera set on a fixed location above the welding region,and the parameters,such as the characteristic heating area,the value of V-type angle,the location of welding bond point and the symmetry degree of the heating region,are mea-sured and processed through a multi-characteristic fusion,thus revealing the current welding state.Moreover,during the image processing,a new segmentation algorithm based on R-G-B non-equilibrium anti-interference technology is employed,the V-type angle detection and the corresponding bisection algorithm based on line fitting are adopted,a function to evaluate the symmetry degree of the heating region is designed,and a multi-characteristic fusion model based on BP neural network is established.Field test results show that the proposed method helps to effectively monitor the state of HF-ERW and control the welding quality.