Open AccessAll-position welding control system based on machine vision and nonlinear regression
Author(s) -
Yu Tang,
Zhongren Wang,
Liwen Jin,
KE Xi-lin,
Haisheng Liu
Publication year - 2021
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/16878140211052740
Subject(s) - welding , nonlinear system , process (computing) , position (finance) , robot welding , nonlinear regression , machine vision , computer science , mechanical engineering , engineering , artificial intelligence , regression analysis , machine learning , physics , finance , quantum mechanics , economics , operating system
Aiming at the problems of poor welding quality and low degree of automatic welding on the engineering site, a welding process parameter control method based on machine vision and nonlinear regression technology is proposed. Firstly, a vision unit and a peripheral sensor unit are designed to obtain the information of each influencing factor of the welding process parameters. Secondly, a clustering algorithm is used to improve the extraction accuracy of feature point coordinates of weld images. Thirdly, a nonlinear regression fitting method is proposed to determine the mathematical relationship between welding quality at different welding positions and corresponding process parameters. Experimental results show that the control system is easy to operate, and the flexible control of welding process parameters in the whole process is realized. The weld cumulative height and width deviations are less than 0.5 and 0.3 mm, respectively. The welding surface is stable and meets welding requirements. Therefore, this method is of great practical significance in engineering field welding.