微间隙焊缝磁光检测神经网络修正

Neural network compensation for micro-gap weld detection by magneto-optical imaging

针对激光焊接微间隙焊缝(间隙小于0.1 mm),研究提高磁光传感检测焊缝精度的BP神经网络修正方法.以碳钢平板对接激光焊为试验对象,利用磁光传感器检测焊缝区域磁场分布并成像.通过分析焊缝处磁场成像并应用BP神经网络修正磁光传感器得到焊缝中心数据,有效避免焊缝磁光图像低对比度和强噪声干扰问题.经过在不同焊接速度试验下的测试,四组神经网络试验的焊缝位置误差的绝对平均值都在0.015 mm左右,BP神经网络测量误差比磁光成像直接测量平均减少约28%.BP神经网络修正磁光成像测量技术可有效识别微间隙焊缝,为解决激光焊接微间隙焊缝过程自动识别和跟踪焊缝的难题提供了一种新方法.

A BP neural network was proposed to compensate detection accuracy of micro-gap weld seam( seam width less than 0. 1 mm). The butt welding of low carbon steel was carried out with laser welding. The magnetized weldments were detected by using a magneto-optical sensor and the magneto-optical images of weld seam were captured. By using BP neural network and processing weld seam magneto-optical images with low contrast and strong magnetic field noises,the weld seam center position could be extracted accurately. Experimental results at different welding speeds indicated that the absolute mean error of weld seam is about 0. 015 mm,and the error measured by BP neural network decrease about 28% than that detected directly by magneto-optimal imaging. The compensation technique for magnetooptical imaging by BP neural network can be applied to detect the micro-gap weld seam accurately. It provides a novel approach for automatic identification and tracking of the micro-gap weld during the laser welding.