Simulation and controlling for weld shape process in P-GMAW based on fuzzy logic

Yan, Zhihong; Zhang, Guangjun; Wun, Lin

doi:10.1109/icma.2011.5986301

Cited by 21 publications

(8 citation statements)

References 7 publications

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“…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%

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: 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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“…Specifically for the 3D weld pool measurement, the most popular techniques currently being studied can be divided into four categories:  Model-based reconstruction The 3D weld pool surface was partially reconstructed based on a simple model proposed in [40]. The reconstruction algorithm was further applied in [41] for the control of weld pool shape. A fuzzy logic controller was constructed to control weld penetration.…”

Section: D Weld Pool Sensingmentioning

confidence: 99%

A tutorial on learning human welder's behavior: Sensing, modeling, and control

Liu

Zhang

2014

Journal of Manufacturing Processes

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Human welder's experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder's behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder's intelligence are surveyed, including sensing of the weld pool, modeling of the welder's adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding system. Finally future research directions in this field are presented.

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“…The proposed model then used the capturing angle of the camera and the 2D weld pool profile to calculate the weld pool width, the length of the pool tail, the height of the rear of the pool, etc. The reconstruction algorithm was further applied in [38] for the control of weld pool shape. A fuzzy logic controller was constructed to control weld penetration.…”

Section: Model-based Reconstructionmentioning

confidence: 99%