All-position welding control system based on machine vision and nonlinear regression

Yu Tang,Xilin Ke,Zhongren Wang,Haisheng Liu,Liying Jin

doi:10.1177/16878140211052740

Abstract

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.

Highlights

Vision technology has developed into a mature stage, enabling more and more industrial robots to be used in enterprise production.[1,2] Welding system with multiple sensing technologies has a good application prospect in the welding field.[3,4,5] Welding technology and system play an important role in modern industrial application,[6] with all-position welding of pipelines always being a research hotspot, which has a broad application prospect to realize all-position low-cost and efficient welding while ensuring welding quality.[7]
This paper was proposed to design an allposition welding control system based on machine vision and nonlinear regression for all-position welding control of welding robots
In order to verify the accuracy and stability of the welding control of the allposition welding control system, the welding seams to be welded were machined by lathe, with high straightness and consistent seam width values, which could eliminate the interference caused by the irregularity of initial welding seams and help to improve the accuracy of data analysis

Summary

Introduction

Vision technology has developed into a mature stage, enabling more and more industrial robots to be used in enterprise production.[1,2] Welding system with multiple sensing technologies has a good application prospect in the welding field.[3,4,5] Welding technology and system play an important role in modern industrial application,[6] with all-position welding of pipelines always being a research hotspot, which has a broad application prospect to realize all-position low-cost and efficient welding while ensuring welding quality.[7]. By designing an appropriate excitation structure, Yue et al.[7] used an exterior high-frequency alternating field to generate eddy current in the metal molten pool, triggering electromagnetic force opposite to gravity This scheme is of positive significance for restraining metal flowing and raising efficiency in all-position welding, but it is inapplicable to outdoor pipeline welding with complex working environment. This paper was proposed to design an allposition welding control system based on machine vision and nonlinear regression for all-position welding control of welding robots It provided a nonlinear regression-based optimization method for all-position welding parameters, which is effective in automatic control of welding parameters by analyzing welding parameters and weld preparation information at different positions of the pipeline and fitting a function.

Related works

Experimental results and analysis

Conclusions