摘要
以提升双金属锯条激光焊接机焊缝轨迹跟踪精度为目的,构建双金属锯条激光焊接机焊缝轨迹跟踪控制模型。采用基于自适应感兴趣区域的方法预处理焊缝轨迹图像,利用水平投影法获取焊缝轨迹中心线,根据焊缝位置处激光条纹有间断的特点,经由扫描激光条纹中心线可以获取焊缝的特征信息,由此确定焊缝轨迹。考虑非完整约束性,利用Lagrange动力学方法描述双金属锯条激光焊接机的动力学模型,基于动力学模型,根据跟踪过程中焊枪点与焊缝轨迹中心线的距离和横向滑块的极限长度间的相关性,制定不同的焊缝跟踪控制策略,考虑双金属锯条激光焊接机的惯性、工件表面不平度等因素,设计基于移动焊接机器人动力学和十字滑块协调控制的滑模变结构控制模型。应用测试结果显示该模型控制下,研究对象焊缝轨迹跟踪结果与实际轨迹基本重合。
In order to improve the tracking accuracy of the welding seam of the double metal saw blade laser welding machine, the welding seam tracking control model for the double metal saw blade laser welding machine was established. The method based on adaptive region of interest(ROI) is used to preprocess the weld track image, and the center line of weld track is obtained by using the horizontal projection method. According to the characteristics of laser stripe discontinuity at the weld position, the characteristic information of weld can be obtained by using the scanning the center line of laser stripe, thus the weld track can be determined.Considering the nonholonomic constraints, the dynamic model for the laser welding machine of bimetal saw blade is described by using Lagrange dynamics method. Based on the dynamic model, different seam tracking control strategies are formulated according to the correlation between the distance between the welding gun and the center line of the weld track and the limit length of the transverse slider in the tracking process. The inertia and workpiece table of the laser welding machine for bimetal saw blade are considered. The sliding mode variable structure control model is designed based on the dynamics of mobile welding robot and the coordinated control of cross slider. The test results show that under the control of the model, the tracking results of the welding seam track of the research object basically coincide with the actual track.
作者
关景新
朱韶平
GUAN Jingxin;ZHU Shaoping(Zhuhai City Polytechnic,Zhuhai 519090,Guangdong,China)
出处
《机械科学与技术》
CSCD
北大核心
2022年第9期1381-1386,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
广东省教育厅青年创新人才类项目(2017GKQNCX074)
广东省普通高校重点项目(2019GKPTZD082)。
