4.6 Article

Robotic welding for filling shape-varying geometry using weld profile control with data-driven fast input allocation

期刊

MECHATRONICS
卷 79, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechatronics.2021.102657

关键词

Control algorithm; Optimization; Polyhedral machine learning; Robotic welding; Weaving

资金

  1. National Research Foundation, Singapore
  2. National University of Singapore
  3. Keppel Corporation

向作者/读者索取更多资源

This paper proposes a welding profile control algorithm for robotic multi-pass welding, which achieves good weld quality by changing welding inputs in real-time. Experimental results show a significant decrease in error and an improvement in welding quality while meeting industry standards.
Robotic multi-pass welding for thick and shape-varying weld geometry is a challenging problem. To achieve good weld quality, desired welding profile for the whole welding bevel is necessary, which requires the welding inputs to be changed appropriately in real-time. In this paper, a welding profile control with data-driven fast input allocation (PC-FIA) algorithm is proposed for robotic multi-pass welding on a shape-varying weld geometry, namely, TYK pipe-to-pipe joint. Firstly, the H-infinity control algorithm is used for the welding profile control in order to suppress the error propagation during the multi-pass welding. Secondly, the welding input parameters including torch traveling speed and weaving parameters are allocated using a max- min optimization based on the identified weld input constraint from a data-driven approach. Experimental results show that the weld profile using the proposed method achieves 60% decrease of root-mean-square error with respect to the planned reference, as compared to the case of without using the proposed PC-FIA method. In addition, the allocated weaving parameters ensure that the welding inputs are always maintained within the polyhedral constraint and the whole welding quality is acceptable by industry standard.

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

4.6
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