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Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor
Robotics and Computer-Integrated Manufacturing ( IF 9.1 ) Pub Date : 2023-01-28 , DOI: 10.1016/j.rcim.2023.102540
Jimin Ge , Zhaohui Deng , Zhongyang Li , Tao Liu , Rongjin Zhuo , Xi Chen

Automatic robot grinding technology has been widely applied in the modern manufacturing industry. A flexible abrasive belt wheel used to grind the weld can avoid burns on the base material and improve the processing efficiency. However, when the robot grinds a weld seam, the material removal depth does not coincide with the feed depth because of the soft contact and uneven weld height, affecting the weld seam surface uniformity. Given these problems, an adaptive parameter optimization approach for the robotic grinding of a weld seam was proposed based on a laser vision sensor and a material removal model. First, the depth of weld seam removal was obtained by a laser vision sensor based on triangulation in real-time. Then, a macroscopic material removal model considering flexible deformation was established to determine the relationship between the weld height and process parameters, and the model coefficient was experimentally fitted to ensure the accuracy and reliability of the model. In addition, the data of real-time interaction structure between the robot controller and grinding system were obtained and used to unsure that the rotational speed of the belt wheel increased in the convex part and decreased in the concave part, in order to obtain a uniform weld seam surface. Comparative experiments were performed to verify the effectiveness and superiority of the method, and experiments on the surface roughness and weld seam surface height difference were conducted to verify the universality of the method. Experimental results show that the residual height of the weld after grinding can be controlled within 0.2mm, and the maximum removal height difference can be controlled within 0.05mm. The surface roughness Ra of the weld after grinding could reach 0.408 µm.



中文翻译:

基于激光视觉传感器的焊缝机器人打磨参数自适应优化方法

自动化机器人打磨技术在现代制造业中得到广泛应用。用于打磨焊缝的柔性砂带轮可避免母材烧伤,提高加工效率。然而,机器人打磨焊缝时,由于接触较软,焊缝高度不均匀,材料去除深度与进给深度不重合,影响焊缝表面均匀性。针对这些问题,提出了一种基于激光视觉传感器和材料去除模型的焊缝机器人磨削自适应参数优化方法。首先,通过基于三角测量的激光视觉传感器实时获得焊缝去除深度。然后,建立考虑柔性变形的宏观材料去除模型,确定焊缝高度与工艺参数之间的关系,并通过实验拟合模型系数,确保模型的准确性和可靠性。此外,还获取了机器人控制器与打磨系统之间实时交互结构的数据,用于确定带轮的转速在凸部增大,在凹部减小,以获得均匀的焊缝表面。通过对比实验验证了该方法的有效性和优越性,并进行了表面粗糙度和焊缝表面高度差实验,验证了该方法的普适性。实验结果表明,磨削后的焊缝残余高度可控制在0.2mm以内,最大去除高度差可控制在0.05mm以内。表面粗糙度磨削后焊缝Ra可达0.408μm。

更新日期:2023-01-29
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