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 seg-mentation to better capture the dependencies of feature information in both spatial and channel dimensions.Thus,the model's seg-mentation 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 ex-perimental results show that,compared with the baseline model,the mean intersection over union and mean pixel accuracy of pro-posed 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.
关键词
焊接偏差/焊缝跟踪/熔池识别/视觉转换器/注意力机制
Key words
welding deviation/seam tracking/molten pool recognition/Vision Transformer(ViT)/attention mechanism
维普
万方数据