Weld penetration control in gas tungsten arc welding (GTAW) process

Liu, YuKang; Zhang, Yuming

doi:10.1109/iecon.2013.6699748

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…This system allows the automated predictive control of weld penetration. Novel methods for feature extraction from 3D vision systems are presented in [ 25 , 26 , 27 ]. Vision systems are often applied; there must be a good access to the welding pool and solidified seam area, to obtain images of proper quality.…”

Section: Introductionmentioning

confidence: 99%

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

2021

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Welding is an important process in terms of manufacturing components for various types of machines and structures. One of the vital and still unsolved issues is determining the quality and properties welded joint in an online manner. In this paper, a technique for prediction of joint hardness based on the sequence of thermogram acquired during welding process is proposed. First, the correspondence between temperature, welding linear energy and hardness was revealed and confirmed using correlation analysis. Using a linear regression model, relations between temperature and hardness were described. According to obtained results in the joint area, prediction error was as low as 1.25%, while for HAZ it exceeded 15%. Future work on optimizing model and input data for HAZ hardness prediction are planned.

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Section: Introductionmentioning

confidence: 99%

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

2021

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“…The weld quality control technology is far from the actual applications; however, the weld quality is also very important for it is the basis for a welding process. Till now, most references about the weld seam forming control are about TIG welding [3][4][5] , and CO 2 weld seam forming control is comparatively rare because it is difficult to obtain the referred information about the process because of the influence of the spatter. This paper used arc and visual sensors to obtain the electrical and visual information of CO 2 weld process, and the information was process to model the CO 2 weld process to predict the weld seam forming, experiment results showed that the method could effectively use the information and obtain qualified prediction results.…”

Section: Introductionmentioning

confidence: 99%

A Study on Modeling of CO<sub>2</sub> Weld Seam Forming Quality

Chen

Feng

2014

AMM

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CO2welding technology is widely used nowadays, because the work environment is very bad, weld automation technology is urgently needed. To control the weld quality automatically, weld sensors should be first used to obtain information that could reflect the weld quality. This paper used arc and visual sensors to obtain the electrical and weld pool image of CO2weld process, and signal processing method was used to obtain the signal features of the information. Then neural network method was used to model the process, experiment results showed that the method could effectively predict the weld seam forming.

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