厚板T形接头焊接位置自主决策贝叶斯网络的实现  

Autonomous decision making of welding positions based on Bayesian network in thick plate T-joint welding

作　　者：余卓骅 方瑶 蒋吴曦 曾洋 何银水[2] Yu Zhuohua;Fang Yao;Jiang Wuxi;Zeng Yang;He Yinshui(Nanchang Jiaotong Institute,Nanchang 330100,China;Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌交通学院,南昌330100 [2]南昌大学,南昌330031

出　　处：《焊接》2022年第11期14-19,共6页Welding & Joining

基　　金：江西省教育厅科技项目(GJJ218417);江西省自然科学基金资助项目(20224BAB202031);南昌交通学院“大学生创新创业训练计划”项目资助(202213431003)。

摘　　要：提出了一种基于贝叶斯网络模型的厚板T形接头GMAW焊接位置的实时自主决策方法。该方法首先根据视觉传感获得的坡口轮廓特征信息,将焊脚要求和当前填充状态转换为视觉描述特征,实现上述信息的实时判定,利用实时识别的焊缝轮廓特征点、判定的焊接状态建立贝叶斯网络模型,并依据焊接经验设计先验概率计算方法,利用自适应重要性抽样算法,基于最大后验概率准则实现焊接位置实时决策。结果表明,与层次分析法的对比,提出的该模型能满足不同厚度T形接头多道焊焊接位置实时自主决策要求,具有更高的决策稳定性,对伪特征点的抗干扰能力强,其决策结果与依据焊接经验实施的决策结果一致,正确率约为95%。该方法能提高焊接效率,同时可为提高厚板GMAW自动化焊接水平提供实现的途径。This paper presented a Bayesian network-based method to implement autonomous decision making of welding positions with T-joints in gas metal arc multipass welding. First, the vision sensing system was used to detect the feature points of the weld seam profile, and the current filling states and the requirements of the weld leg were respectively transformed into visual feature representations, with which the welding state was defined as backing, filling and cosmetic welding. Second, a Bayesian network was built using the identified feature points and the determined welding state, and prior probability used in this network was designed in a computational way based on welding experience. Finally, the decision-making result was implemented using the adaptive importance sampling algorithm based on the maximum posteriori probability criterion. Experimental results showed that the proposed Bayesian network here possessed better decision stability of the welding position during the multipass welding process with T-joints of different thicknesses compared with the analytic hierarchy process. The proposed Bayesian network method can overcome the effect of fake possible feature points on the decision-making result of welding positions. The decision-making results by the proposed Bayesian network are consistent with those by welding experience. The effectiveness of the decision-making results is up to about 95%. This method contributes to high welding efficiency and provides a reference for improving the automatic welding level in thick plate gas metal arc welding.

关 键 词：贝叶斯网络 焊接位置自主决策 T形接头 视觉传感 机器人焊接 

分 类 号：TG403[金属学及工艺—焊接]