In arc welding processes, real time control algorithms have to be developed in order to achieve desired weld quality. However, there could exist big uncertainties and noise in the process, which nullifies the conventional online control method. Besides, due to the modelling difficulty and low experimental efficiency, this task is usually performed offline. In this paper, a real time parameter optimization method is developed to find the optimal welding parameters to achieve desired characteristic performance. Gaussian Process Regression (GPR), a non-parametric modelling technique, is employed to model the relationship between input welding parameters and output characteristic performance. The GPR surrogated Bayesian Optimization Algorithm (GPRBOA) is proposed to optimize the welding parameters. Lower Confidence Bound (LCB) and Upper Confidence Bound (UCB) acquisition functions are utilized. Gas tungsten arc welding experiments were performed and the corresponding experimental data are collected and utilized to validate the proposed modelling method. The predicted characteristic performance is compared with the original data and it shows that the modelling method can accurately predict the weld bead geometry. The control algorithms were demonstrated and the results are presented. This paper opens a door for real time parameter tuning to achieve desired performance, hence the proposed method will innovate the arc welding process.
|Title of host publication||ICRA 2017 - IEEE International Conference on Robotics and Automation|
|Number of pages||6|
|State||Published - Jul 21 2017|
|Event||2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore|
Duration: May 29 2017 → Jun 3 2017
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2017 IEEE International Conference on Robotics and Automation, ICRA 2017|
|Period||5/29/17 → 6/3/17|
Bibliographical noteFunding Information:
H. Dong and M. Cong∗ (corresponding author) are with the Department of Mechanical Engineering, Dalian University of Technology, Email: email@example.com, Tel. (+86)13130491926 Y. Zhang and Y. Liu are with the Department of Electrical and Computer Engineering, University of Kentucky, Email: firstname.lastname@example.org H. Chen∗ (corresponding author) is with the Ingram School of Engineering, Texas State University, Email: email@example.com *This work is supported by Key Scientific and Technological Project of Liaoning Province (No. 2015080009-201) to M. Cong.
© 2017 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering