4.7 Article

Robot-process precision modelling for the improvement of productivity in flexible manufacturing cells

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2020.101966

关键词

Position tolerance; Multibody system with mixed coordinates; Drilling robot; Quaternions; Uncertainty analysis

资金

  1. Spanish Ministry of Economy and Competitiveness [DPI2016-79960-C3-1-P, SIDIX92513X2017]
  2. CONICYT Becas Chile [72170282]

向作者/读者索取更多资源

Industrial robots are traditionally used at machining cells for machine feeding and workpiece handling. A reassignment of tasks to improve the productivity requires a modelling of the robot behaviour from the point of view of its position precision. This paper characterizes and predicts the precision achievable when drilling with an industrial robot in order to use it in machining operations. Robot behaviour and drilling phenomena are analysed to determine working accuracy and their contribution in position deviation and uncertainty. An efficient model for drilling is developed, applying quaternions and considering the influence of all cutting tool angles, providing a very precise estimation of drilling torques and forces. An innovative model for the robot is developed based on multibody systems, using mixed natural coordinates that enhance the computing and deliver outputs with direct interpretation. Besides, the effect of stiffness is added in joints as additional element. The complete robot-process model shows the significative process influence in working precision against robot influence. This influence is responsible of up to 40% of the total uncertainty. The model and the tests performed show that the deviations and their uncertainties depend strongly on drilling forces and the robot configuration. In the other hand, the model allows to correct the systematic behaviour in robot deviations and improve with that the position tolerance of the holes to be drilled.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据