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基于位姿变化的钻铆机器人刚度优化

Drilling Riveting Robot Stiffness Optimization Based on Pose Change
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摘要 钻铆机器人广泛应用于航空薄壁件加工装配中,当末端配备重载执行器进行钻铆作业时,因受到自身重力和横向切削力的影响会导致末端变形,加工质量变差,工业机器人这种弱刚性限制了其在精密加工领域的发展。以工业机器人自动钻铆系统为研究对象,建立运动学模型;选择合适的试验进行关节刚度辨识;在刚度模型基础上定义关节刚度性能指标;并通过粒子群优化算法寻找工业机器人作业时刚度最大位姿,确定所处位姿的关节角参数。通过选择合适的加工位姿提升工业机器人的刚度性能,保证孔位加工质量,提高生产效率,也可避免危险事故的发生。通过以上研究,可以求得符合钻铆工况下刚度最优的机器人工作位姿,并在假设实验中,验证了该姿态优化方法的可行性和有效性。 Drilling and riveting robots are widely used in aviation thin-walled parts processing and assembly.When the end is equipped with a heavy-duty actuator for drilling and riveting operations,due to the influence of its own gravity and lateral cutting force,it will lead to end deformation and poor processing quality,and the weak rigidity of industrial robots limits its development in the field of precision machining.Taking the automatic drilling and riveting system of industrial robot as the research object,the kinematic model was established;the appropriate test was selected for joint stiffness identification;the joint stiffness performance index was defined on the basis of the stiffness model;the particle swarm optimization algorithm was used to find the maximum stiffness pose of the industrial robot during operation,and the joint angle parameters of the posture were determined.By selecting the appropriate processing position,the rigidity performance of the industrial robot is improved,the quality of hole processing is guaranteed,the production efficiency is improved,and dangerous accidents can also be avoided.Through the above research,the robot working posture with the optimal stiffness under drilling and riveting conditions can be obtained,and the feasibility and effectiveness of the attitude optimization method are verified in hypothetical experiments.
作者 王明海 张威 刘香辰 WANG Minghai;ZHANG Wei;LIU Xiangchen(College of Mechanical and Electrical Engineering,Shenyang Aerospace University,Shenyang Liaoning 110136,China;Key Laboratory of Fundamental Science for National Defense of Aeronautical Digital Manufacturing Process,Shenyang Aerospace University,Shenyang Liaoning 110136,China;College of Aerospace Engineering,Shenyang Aerospace University,Shenyang Liaoning 110136,China)
出处 《机床与液压》 北大核心 2024年第3期49-54,共6页 Machine Tool & Hydraulics
关键词 工业机器人 位姿变化 刚度辨识 粒子群算法 industrial robots pose change stiffness identification particle swarm optimization algorithm
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