Keyhole tungsten inert gas(K-TIG)welding is capable of realizing single-sided welding and double-sided forming and has been widely used in medium and thick plate welding.In order to improve the accuracy of automatic w...Keyhole tungsten inert gas(K-TIG)welding is capable of realizing single-sided welding and double-sided forming and has been widely used in medium and thick plate welding.In order to improve the accuracy of automatic weld identification and weld penetration prediction of robot in the process of large workpiece welding,a two-stage model is proposed in this paper,which can monitor the K-TIG welding penetration state in real time on the embedded system,called segmentation-LSTM model.The proposed system extracts 9 weld pool geometric features with segmentation network,and then extracts the weld gap using a traditional algorithm.Then these 10-dimensional features are input into the LSTM model to predict the penetration state,including under penetration,partial penetration,good penetration and over penetration.The recognition accuracy of the proposed system can reach 95.2%.In this system,to solve the difficulty of labeling data and lack of segmentation accuracy,an improved LabelMe capable of live-wire annotation tool and a novel loss function were proposed,respectively.The latter was also called focal dice loss,which enabled the network to achieve a performance of 0.933 mloU on the testing set.Finally,an improved slimming strategy compresses the network,making the segmentation network achieve real-time on the embedded system(RK3399pro).展开更多
基金the Key Research and Development Program of Guangdong Province(Grant No.2020B090928003)the National Natural Science Foundation of Guangdong Province(Grant No.2020A1515011050).
文摘Keyhole tungsten inert gas(K-TIG)welding is capable of realizing single-sided welding and double-sided forming and has been widely used in medium and thick plate welding.In order to improve the accuracy of automatic weld identification and weld penetration prediction of robot in the process of large workpiece welding,a two-stage model is proposed in this paper,which can monitor the K-TIG welding penetration state in real time on the embedded system,called segmentation-LSTM model.The proposed system extracts 9 weld pool geometric features with segmentation network,and then extracts the weld gap using a traditional algorithm.Then these 10-dimensional features are input into the LSTM model to predict the penetration state,including under penetration,partial penetration,good penetration and over penetration.The recognition accuracy of the proposed system can reach 95.2%.In this system,to solve the difficulty of labeling data and lack of segmentation accuracy,an improved LabelMe capable of live-wire annotation tool and a novel loss function were proposed,respectively.The latter was also called focal dice loss,which enabled the network to achieve a performance of 0.933 mloU on the testing set.Finally,an improved slimming strategy compresses the network,making the segmentation network achieve real-time on the embedded system(RK3399pro).
文摘提出了一种适用于高功率CO2激光焊缝熔透状态的在线监测方法,设计了不同熔透状态的高功率CO2激光堆焊焊接试验,采用高速摄影技术获取焊接过程中连续变化的光致等离子体图像,编程实现基于最大类间方差的图像分割,计算了光致等离子的高度、底部宽度和面积,得到了特征参数的概率密度分布图和变异系数,并分析了光致等离子体面积的频谱特征.结果表明:在焊缝未熔透时,光致等离子体的高度和面积均显著大于熔透状态时;焊缝未熔透状态的光致等离子体的面积波动频率约为400 Hz.
