摘要
为避免转向架的焊接过程出现烧穿、未熔合、卡丝、夹渣等缺陷,文中构建了转向架机器人MAG焊接过程信息采集系统,能够实时采集熔池图像、电弧声音及电压信息。分别提取了与质量密切相关的关键特征,并基于XGBoost模型开展了知识建模与融合分析。最终实现了类似于焊接技能大师的信息感知与质量分析能力,综合精度可达90%以上,从而提升了转向架焊接作业的质量可靠性与智能制造水平。
In order to avoid defects such as burn-through,incomplete fusion,wire blocking and slag inclusion appearing in welding process of bogie,the information acquisition system of bogie robot MAG welding process was constructed,which could collect real-time weld pool image,arc sound and voltage information.Key features closely related to quality were extracted and knowledge modeling and fusion analysis were carried out based on XGBoost model.Finally,the ability of information perception and quality analysis similar to welding masters were realized,and the comprehensive accuracy could reach more than 90%,thus improving the quality reliability and intelligent manufacturing level of bogie welding operation.
作者
邓钢
马志
陈伟光
刘昱
陈华斌
Deng Gang;Ma Zhi;Chen Weiguang;Liu Yu;Chen Huabin(CRRC Changchun Railway Vehicles Co.,Ltd.,Changchun 130062,China;CRRC Industrial Academy Co.,Ltd.,Beijing 100160,China;Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《焊接》
北大核心
2021年第12期53-59,I0010,共8页
Welding & Joining
关键词
智能焊接
缺陷识别
信息融合
转向架焊接
intelligent welding
defect recognition
information fusion
bogie welding
