Fusing machine algorithm with welder intelligence for adaptive welding robots
Fusing machine algorithm with welder intelligence for adaptive welding robots
复制标题
DOI：
10.1016/j.jmapro.2017.03.015
复制
发表时间：
2017-06
影响因子：
6.2
通讯作者：
Yukang Liu;Yuming Zhang
中科院分区：
文献类型：
--
作者：
Yukang Liu;Yuming Zhang
当前的工业焊接机器人大多是具有预先编程的运动集合的铰接臂。这些机器人缺乏熟练的人类焊工所拥有的适应性和智能。随着GTAW熔池表面三维测量技术的发展，焊接机器人也可以获得焊接过程中的关键反馈信息。为了有效地利用这种关键反馈来装备焊接机器人，焊工对这种关键反馈的响应已经被建模。可以理解的是，人类可能对过程干扰具有更好的鲁棒性，但机器算法可能被设计成响应更快。适当切换可以提高绩效，但应建立在科学的标准之上。此外，将它们融合在一起甚至可以比简单地切换更好地执行。在本文中，建立了一个标准，自动率的性能，从三个算法被融合-一个机器算法和两个人的反应模型/算法。一个模糊系统已被提出并建立融合的决定，从这些算法根据其性能评级。仿真结果证实了该融合方法的优越性。通过焊接机器人的自动焊接实验，验证了基于融合的焊接速度控制器在不同焊接电流和输入干扰下的鲁棒性。为融合机器智能和人智能开发下一代智能焊接机器人奠定了基础。
Current industrial welding robots are mostly articulated arms with a pre-programmed set of movement. These robots lack the adaptation and intelligence skilled human welders possess. With the recent developments in measuring 3D weld pool surface in GTAW, the critical process feedback available to human welders also becomes available to welding robots. To effectively use this critical feedback to equip welding robots, welders’ responses to this critical feedback have been modeled. It is arguable that human may have better robustness against process certainties but machine algorithms may be designed to be quicker in response. Switching among them appropriately may improve the performance but should base on a scientific criterion. Further, fusing them together may even better perform than simply switching. In this paper, a criterion is established to automatically rate the performance from each of the three algorithms being fused – one machine algorithm and two human response models/algorithms. A fuzzy system has been proposed and established to fuse the decisions from these algorithms per their performance ratings. Simulation confirms the superiority of the fusion method to any of these three algorithms. Automated welding experiments conducted by welding robot verifies that the fusion based welding speed controller was robust under different welding currents and input disturbances. A foundation is thus established to developing next generation intelligent welding robots by fusing machine and human intelligence.