Model-Based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding

Liu, Yu Kang; Zhang, Yu Ming

doi:10.1109/tcst.2013.2266662

Cited by 119 publications

(32 citation statements)

References 23 publications

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“…Another way to obtain 3D profiles is using the structured light pattern as a laser dot matrix or laser line grill [1,[16][17][18][19][20][21][22]. With this two-dimensional information and using the same triangulation techniques, it is possible to obtain the third dimension of the profile.…”

Section: Direct Measurement Of the Width And Reinforcement Of The Welmentioning

confidence: 99%

“…Dynamic behaviour is essential to estimate the current or future state form to the past state. Despite this, the minority reports dynamic models [17,34,39,40,[53][54][55][56][57][58][59][60][61][62][63][64][65] as shown in the summary of An example of the use of dynamic models is shown in [54,56], which employs neural networks that aggregate previous values of the variables of interest and also in [17,59] that develop an adaptive neuro-fuzzy inference system (ANFIS)-based human intelligence. They are both used for total weld bead depth control in the GTAW process.…”

Section: Estimation Of the Weld Bead Depthmentioning

confidence: 99%

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Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Bestard

Alfaro

2018

J Braz. Soc. Mech. Sci. Eng.

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The arc welding process is widely used in industry, but the automatic control is limited by the difficulty in the process for measuring the principal magnitudes and to close the control loop. Adverse environmental conditions make use of conventional measurement systems difficult for obtaining information of the weld bead geometry. Under these conditions, indirect sensing techniques are a good option. Different sensing and estimation techniques are used, but few researchers are focusing on the flat welding position. The theory and practice prove that the dynamic models are the best representation to control the welding process, but most studies are performed with static models. This work is a review of the algorithms and sensing techniques used for collecting values of the arc welding process that allow the measurement or estimation of the weld bead geometry. Special attention is given to sensor fusion techniques due to its promising future in the welding process. Discussed in this text are the papers, patents, thesis and other documents found on the theme. It shows a summary of their evolution over the last 50 years.

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Section: Direct Measurement Of the Width And Reinforcement Of The Welmentioning

confidence: 99%

Section: Estimation Of the Weld Bead Depthmentioning

confidence: 99%

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Bestard

Alfaro

2018

J Braz. Soc. Mech. Sci. Eng.

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“…By changing the length of welding arc to control the heat that transferred to the welding workpiece, the control requirements were well met. In order to imitate the estimation and decision-making process of skilled welders, an adaptive neurofuzzy inference system (ANFIS) was used to establish the model that related the back weld width to the welding current and speed, and a model predictive control was used to follow a trajectory to reach the desired back weld width [11]. Although some achievements have been made in the above research, for welding process, which has multivariable, nonlinear, time-varying parameters and contains many uncertain factors and constraints, it is almost impossible to obtain an accurate mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Welding Process Tracking Control Based on Multiple Model Iterative Learning Control

Liu

Wang

et al. 2019

Mathematical Problems in Engineering

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The welding of the same parts has same welding trajectory, so welding process has strong repeatability. In this paper, aiming at the repeatability of welding process, an iterative learning controller is designed to achieve the control of weld quality. Due to the extremely variable welding environment and the presence of noise interferences and load disturbances, it is easy to cause the jumping change in parameters and even the structure of the welding system. Therefore, the idea of multiple model adaptive control (MMAC) is introduced into iterative learning control (ILC), and a multiple model iterative learning control (MMILC) algorithm is designed according to model of weld pool dynamic process in gas tungsten arc welding (GTAW). Besides, the convergence of the algorithm is analyzed for two cases: fixed parameters and jumping parameters. It turns out that the MMILC can not only utilize the repetitive information effectively in the welding process to achieve high precision tracking control of weld seam in limited time interval, but also realize the multiple model switching according to different working conditions to improve the welding quality.

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“…However, to estimate the penetration state from any top-side measurements, including the weld pool surface, models are needed. Empirical modelling has been studied to certain extent [20,21]. Unfortunately, for the most promising top-side measurement (3D weld pool surface), success in empirical modelling has been limited.…”

Section: Introductionmentioning

confidence: 99%

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

Huang

Xue

Huang

et al. 2019

Chin. J. Mech. Eng.

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Penetration estimation is a prerequisite of the automation of backing welding based on vision sensing technology. However, the arc interference in welding process leads to the difficulties of extracting the weld pool characteristic information, which brings great challenges to the penetration estimation. At present, most researches focus on the extraction of weld pool geometry parameters, and the visual sensing systems are complex in structure and complicated in the image processing algorithms. The research of penetration estimation based on weld pool geometry parameters is still in the exploratory stage. The purpose of this paper is to research the relationship between the weld pool geometry parameters and the penetration during backing welding and to estimate penetration using the weld pool geometry parameters. A passive vision sensing test system for gas metal arc (GMA) backing welding was established. An image processing algorithm was developed to extract the weld pool geometry parameters, namely, the area, maximum width and length, half-length, length-width ratio and advancing contact angle (simplified as AWP, MWWP, MLWP, HLWP, LWR and ACA, respectively). The corresponding relationships between the weld pool geometry parameters and the penetration state were explored by analysing their changes with the welding current and speed. The distribution of the weld pool geometry parameters corresponding to penetration was determined. When the AWP of the weld pool is within a certain range and the values of LWR and ACA are close to their maximum and minimum respectively, the penetration is in good condition. A mathematical model with the weld pool geometry parameters as independent variables and the back-bead width (the indicator of the penetration state) as a dependent variable was established based on multivariable linear regression analysis, and relevant statistical tests were carried out. Multivariable linear regression equations for the weld pool geometry parameters and the back-bead width were deduced according to the variations in the current and speed, and the equations can be used to estimate the penetration of backing welding. The study provides a solution to penetration estimation of GMA backing welding based on automatic vision sensing.

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Model-Based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding

Cited by 119 publications

References 23 publications

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Welding Process Tracking Control Based on Multiple Model Iterative Learning Control

Penetration Estimation of GMA Backing Welding Based on Weld Pool Geometry Parameters

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