TY - GEN
T1 - Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm
AU - Sterling, Dillon
AU - Sterling, Tyler
AU - Zhang, Yuming
AU - Chen, Heping
PY - 2015/10/7
Y1 - 2015/10/7
N2 - In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian Optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process; moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.
AB - In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian Optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process; moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.
UR - http://www.scopus.com/inward/record.url?scp=84952784991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84952784991&partnerID=8YFLogxK
U2 - 10.1109/CoASE.2015.7294310
DO - 10.1109/CoASE.2015.7294310
M3 - Conference contribution
AN - SCOPUS:84952784991
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1490
EP - 1496
BT - 2015 IEEE Conference on Automation Science and Engineering
Y2 - 24 August 2015 through 28 August 2015
ER -