中文摘要：

理论与试验研究用于焊缝位置识别的视觉模型,该模型主要由弹性梯度下降训练法BP神经网络组成。在焊接工艺条件下,使用视觉传感器获取焊接区熔池图像,并选取特定区域进行中值滤波与图像灰度变换处理以增强被测对象的特征。在此基础上,计算和处理熔池特性参量（熔池图像质心差值、质心位移、质心移动速度）以及相对应的焊缝与电弧之间的偏差值,将其输入所设计的神经网络进行网络权值参数训练和推理学习,从而建立基于BP神经网络、具有一定认知和环境适应能力的焊缝位置识别视觉模型。对该模型进行通用性检验,试验结果表明该模型通过熔池特性参量可以较精确地识别焊缝位置。

英文摘要：

A visual model for detecting the weld position is studied theoretically and experimentally. This model is developed by a BP neural network trained by the elastically gradient descending arithmetic. The weld pool images are caught by a vision sensor during the arc welding process. A location of the welding pool image is chosen as a special processed image whose characteristic corresponds with the welding pool image centroid. This special image is processed by the median filtering and the image gray transform so as to sharpen the weld character in the welding pool images. The difference, the displacement and the moving velocity of the image centroid are regarded as weld pool characteristic parameters. These parameters are applied as the input variables of the BP neural network. The offset between the weld position and the welding arc is used as the output variable of the BP neural network. This BP neural network model is trained by the elastically gradient descending arithmetic and the network weights are calculated. The established visual model based on the BP neural network has certain perceiving and adapting ability to the welding environment. The generality of the visual model is tested and the experimental results show that this visual model has the excellent generality and accuracy. The weld position can be detected accurately through the weld pool characteristic parameters.