基于改进ViT的熔池识别与焊接偏差在线检测方法

Improved ViT-based method for molten pool recognition and online detection of welding deviation

焊接偏差的精确检测是实现焊接机器人焊缝轨迹自动跟踪及智能化焊接的前提。提出了一种基于改进视觉转换器(Vision Transformer,ViT)的熔池识别与焊接偏差在线检测方法。首先,采用轻量级ViT模型Segformer作为基线模型,在其掩码分割前嵌入置换注意力(Shuffle Attention,SA)机制,以更好地捕获特征信息在空间和通道这2个维度中的依赖关系,从而提高模型的分割精度;其次,在多层感知机(Multilayer Perceptron,MLP)中加入上下文广播(Context Broadcasting,CB)模块,在保证模型低参数量的前提下提高泛化能力;最后,基于模型分割结果,提出一种焊接偏差计算方法来定量描述偏差检测精度。实验结果表明,相较于基线模型,所提出模型的平均交并比和平均像素准确率分别提高了2.67%和2.12%,且对于不同预设焊枪偏移情况均具有良好的泛化性,焊接偏差精度控制在±0.021 mm之内,为实现精密焊接焊缝跟踪提供基础。

Accurate detection of welding deviations is a prerequisite for automatic seam tracking and intelligent welding by welding robots.An improved ViT-based method for molten pool recognition and online detection of welding deviation was proposed.Firstly,the lightweight ViT model Segformer was used as the baseline model.The Shuffle Attention(SA)was embedded before mask segmentation to better capture the dependencies of feature information in both spatial and channel dimensions.Thus,the model's segmentation accuracy was enhanced.Secondly,a Context Broadcasting(CB)module was added to the Multilayer Perceptron(MLP)to improve the generalization capability while ensuring low parameters of model.Finally,based on the model segmentation results,a welding deviation calculation method was proposed to quantitatively describe the deviation detection accuracy.The experimental results show that,compared with the baseline model,the mean intersection over union and mean pixel accuracy of proposed model were increased by 2.67%and 2.12%,respectively,and it has good generalization for different preset torch offsets.The welding deviation accuracy was controlled between±0.021 mm,which provided a basis for seam tracking in precision welding.