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Feature Extraction from Arc Signal for Height Tracking System of P-MAG Welding

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Robotic Welding, Intelligence and Automation (RWIA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 363))

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Abstract

An experimental system is established to acquire and process the arc signal. Possible Features and a signal process method are proposed by analyzing the characteristics of the pulse power supply. The best feature is selected by applying the feature selection method and the relational model between the feature and the torch height is established. Experiments are conducted to confirm the accuracy of the model and its application in height tracking system.

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References

  1. Bao Y, Zhu Z, Wu W (2001) Through-the-wire sensor of short-circuit CO2 welding. Trans China Weld Inst 22(3):55–58

    Google Scholar 

  2. Chen SB, Qiu T (2004) Intelligent technologies for robotic welding. Lect Notes Control Inf Sci 299:123–143

    Article  Google Scholar 

  3. Cook GE (1983) Robotic arc welding: research in sensory feedback control. IEEE Trans Ind Electron IE 30(3):252–268

    Google Scholar 

  4. Gan Y, Dai X (2009) Deviation extraction for through-arc sensor with sinusoidal weave motion. Trans China Weld Inst 30(9):89–92

    Google Scholar 

  5. Kim JW, Na SJ (1991) A study on prediction of welding current in gas metal arc welding part 1. Proc Inst Mech Eng B 205:59–63

    Article  Google Scholar 

  6. Kim JW, Na SJ (1991) A study on prediction of welding current in gas metal arc welding part 2. Proc Inst Mech Eng B 205:64–69

    Article  MATH  Google Scholar 

  7. Kim JW, Na SJ (1991) A study on arc sensor algorithm for weld seam tracking in gas metal arc welding of butt joints. Proc Inst Mech Eng Part B J Eng Manuf 205(B4):247–255

    Article  Google Scholar 

  8. Li LP, Lin T, Chen SB (2005) Light intensity analysis of a passive visual sensing system in GTAW. Int J Adv Manuf Technol 27(1–2):106–111

    Google Scholar 

  9. Ma HB, Wei SC, Sheng ZX (2010) Robot weld seam tracking method based on passive vision for thin plate closed-gap butt welding. Int J Adv Manuf Technol 48(9–12):945–953

    Article  MATH  Google Scholar 

  10. Shen HY, Li LP, Lin T, Chen SB (2010) Real-time seam tracking technology of welding robot with visual sensing. J Intell Rob Syst 59(3–4):283–298

    Article  Google Scholar 

  11. Shi YH, Yoo WS, Na SJ (2006) Mathematical modelling of rotational arc sensor in GMAW and its applications to seam tracking and endpoint detection. Sci Technol Weld Joining 11(6):723–730

    Article  Google Scholar 

  12. Xu PQ, Xu GX, Tang XH, Yao S (2008) A visual seam tracking system for robotic arc welding. Int J Adv Manuf Technol 37(1–2):70–75

    Article  Google Scholar 

  13. Zhu ZM (2008) Real-time detection and control of torch-height in waveform controlled short-circuit CO2 arc welding. Mater Sci Forum 580–582:427–430

    Article  Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under the Grant No. 51075268 and Shanghai Sciences & Technology Committee under Grant No. 11111100302, P.R. China.

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Authors and Affiliations

  1. Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, People’s Republic of China

    Zhen Ye

  2. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Hua-Bin Chen & Shan-Ben Chen

  3. School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, NSW, 2751, Australia

    Fang Gu

Authors
  1. Zhen Ye
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  2. Hua-Bin Chen
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  3. Fang Gu
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  4. Shan-Ben Chen
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Corresponding author

Correspondence to Zhen Ye .

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Editors and Affiliations

  1. Washington University, St. Louis, Missouri, USA

    Tzyh-Jong Tarn

  2. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shan-Ben Chen

  3. Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand

    Xiao-Qi Chen

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Ye, Z., Chen, HB., Gu, F., Chen, SB. (2015). Feature Extraction from Arc Signal for Height Tracking System of P-MAG Welding. In: Tarn, TJ., Chen, SB., Chen, XQ. (eds) Robotic Welding, Intelligence and Automation. RWIA 2014. Advances in Intelligent Systems and Computing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-319-18997-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-18997-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18996-3

  • Online ISBN: 978-3-319-18997-0

  • eBook Packages: EngineeringEngineering (R0)

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