Model Predictive Control of GTAW Weld Pool Penetration

Chen, Jinsong; Chen, Jian; Feng, Zhili; Zhang, Yuming

doi:10.1109/LRA.2019.2918681

Title: Model Predictive Control of GTAW Weld Pool Penetration

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Abstract

Weld pool penetration is a major implication of weld integrity but it is not directly measurable. The key to solving this issue is to find or create observable phenomena that can be used to derive the penetration. An innovative method has recently been proposed/developed to derive the penetration from the reflection of projected laser from an oscillating weld pool surface. Yet, in addition to the penetration, the oscillation, thus the reflection determined by it, also depends on the pulse amplitude of the current, which needs to be adjusted as the control variable to feedback control the penetration as the output. This dependence imposes a constraint on the control algorithm such that the adjustment of the control variable must be relatively slow to minimize additional sources that affect the oscillation/sensor. This restriction on the control variable adjustment also brings challenges to the identification of the dynamic model of the weld process to be controlled, which has to apply a varying control signal to stimulate the dynamics. To solve these issues, this letter proposes using a low order model to catch the dominant dynamics such that a high speed variation in the control signal can be avoided when choosing the model predictivemore »« less

Authors:

Chen, Jinsong ^[1];

^[2];

^[1]

Univ. of Kentucky, Lexington, KY (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Thu May 23 00:00:00 EDT 2019

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1524852

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Robotics and Automation Letters

Additional Journal Information:: Journal Volume: 4; Journal Issue: 3; Journal ID: ISSN 2377-3774

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; Welding; Process control; Oscillators; Reflection; Control systems; Lasers; Aerodynamics; GTAW; weld pool; image sensor; MPC

Citation Formats


                    Chen, Jinsong, Chen, Jian, Feng, Zhili, and Zhang, Yuming. Model Predictive Control of GTAW Weld Pool Penetration.  United States: N. p., 2019. 
Web.  doi:10.1109/LRA.2019.2918681.

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                    Chen, Jinsong, Chen, Jian, Feng, Zhili, & Zhang, Yuming. Model Predictive Control of GTAW Weld Pool Penetration.  United States.  https://doi.org/10.1109/LRA.2019.2918681

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                    Chen, Jinsong, Chen, Jian, Feng, Zhili, and Zhang, Yuming. Thu .  
"Model Predictive Control of GTAW Weld Pool Penetration".  United States.  https://doi.org/10.1109/LRA.2019.2918681.  https://www.osti.gov/servlets/purl/1524852.

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@article{osti_1524852,

  title        = {Model Predictive Control of GTAW Weld Pool Penetration},

  author       = {Chen, Jinsong and Chen, Jian and Feng, Zhili and Zhang, Yuming},

  abstractNote = {Weld pool penetration is a major implication of weld integrity but it is not directly measurable. The key to solving this issue is to find or create observable phenomena that can be used to derive the penetration. An innovative method has recently been proposed/developed to derive the penetration from the reflection of projected laser from an oscillating weld pool surface. Yet, in addition to the penetration, the oscillation, thus the reflection determined by it, also depends on the pulse amplitude of the current, which needs to be adjusted as the control variable to feedback control the penetration as the output. This dependence imposes a constraint on the control algorithm such that the adjustment of the control variable must be relatively slow to minimize additional sources that affect the oscillation/sensor. This restriction on the control variable adjustment also brings challenges to the identification of the dynamic model of the weld process to be controlled, which has to apply a varying control signal to stimulate the dynamics. To solve these issues, this letter proposes using a low order model to catch the dominant dynamics such that a high speed variation in the control signal can be avoided when choosing the model predictive control algorithm. This allows use of relatively poor models to determine the adjustment on the pulsing amplitude, and still achieves relatively accurate control. Experimental results verified the effectiveness of the resultant penetration control system despite the restrictions imposed by the penetration sensor, which affect the identification of high order dynamics and free adjustments of control variables.},

  doi          = {10.1109/LRA.2019.2918681},

  journal      = {IEEE Robotics and Automation Letters},

  number       = 3,

  volume       = 4,

  place        = {United States},

  year         = {Thu May 23 00:00:00 EDT 2019},

  month        = {Thu May 23 00:00:00 EDT 2019}

}

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