Robotic GMAW online learning: issues and experiments

  • Reyes Rios-Cabrera
  • America B. Morales-Diaz
  • Jaime F. Aviles-Viñas
  • Ismael Lopez-Juarez
ORIGINAL ARTICLE

Abstract

This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process.

Keywords

ANN Computer vision Bead geometry Bead penetration Industrial robots Robotics on-line learning Robotics welding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    BBC NEWS, Facebook to open AI lab in Paris (2015) Technology section, link: http://www.bbc.com/news/technology-32977242, Consulted on June 2, 2015
  2. 2.
    Gorle P, Clive A (2013) Positive impact of industrial robots on employment. International Federation of Robotics. METRA MARTECH LimitedGoogle Scholar
  3. 3.
    World Robotics 2013 (2013) Industrial robots. International Federation of RoboticsGoogle Scholar
  4. 4.
    Gorle P, Clive A (2013) Positive impact of industrial robots on employment. International Federation of Robotics. METRA MARTECH LimitedGoogle Scholar
  5. 5.
    Aviles-Vias JF, Lopez-Juarez I, Rios-Cabrera R (2015) Acquisition of welding skills in industrial robots. Indus Robot: Int J 42(2):156–166CrossRefGoogle Scholar
  6. 6.
    Aviles-Vias JF, Rios-Cabrera R, Lopez-Juarez I (2015) On-line learning of welding bead geometry in industrial robots. In: The international journal of advanced manufacturing technology, pp 1-15. ISSN 0268-3768, doi:10.1007/s00170-015-7422-6
  7. 7.
    Grossberg S, Markuzon N, Reynolds JH, Carpenter GA, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5)Google Scholar
  8. 8.
    Kah P, Shrestha M, Hiltunen E, Martikainen J (2015) Robotic arc welding sensors and programming in industrial applications. Int J Mech Mater Eng 10. doi:10.1186/s40712-015-0042-y. issn: 1823-0334, Nr 1
  9. 9.
    Li S, Chen G, Zhou C (2015) Effects of welding parameters on weld geometry during high-power laser welding of thick plate. Int J Adv Manuf Technol 79:177–182. doi:10.1007/s00170-015-6813-z. issn: 0268–3768, Nr. 1–4CrossRefGoogle Scholar
  10. 10.
    Ganjigatti JP, Pratihar DK, RoyChoudhury A (2008) Modeling of the MIG welding process using statistical approaches. Int J Adv Manuf Technol 35:1166–1190CrossRefGoogle Scholar
  11. 11.
    Luo H, Chen X (2005) Laser visual sensing for seam tracking in robotic arc welding of titanium alloys. Int J Adv Manuf Technol 26:1012–1017CrossRefGoogle Scholar
  12. 12.
    Horvat J, Prezelj J, Polajnar I, Cudina M (2011) Monitoring gas metal arc welding process by using audible sound signal. Strojniski vestnik-J Mech Eng 57(3):267–278CrossRefGoogle Scholar
  13. 13.
    Cudina M, Prezelj J, Polajnar I (2008) Use of audible sound for on-line monitoring of gas metal arc welding process. Metalurgia 47(2):81–85Google Scholar
  14. 14.
    Na Lv, Yanling X, Zhong J, Chen H (2013) Research on detection of welding penetration state during robotic GTAW process based on audible arc sound. Indus Robot: Int J 40(/5):474–493Google Scholar
  15. 15.
    Kolahan F, Heidari M (2010) A new approach for predicting and optimizing weld bead geometry in GMAW. Int J Mech Syst Sci Eng 2:2:138–142Google Scholar
  16. 16.
    Sreeraj P, Kannan T, Maji S (2013) Prediction and control of weld bead geometry in gas metal arc welding process using simulated annealing algorithm. Int J Comput Eng Res 3(1):213– 222Google Scholar
  17. 17.
    Ma H, Wei S, Lin T, Chen S, Li L (2010) Binocular vision system for both weld pool and root gap in robot welding process. Sensor Rev 30(2):116–123CrossRefGoogle Scholar
  18. 18.
    Kim I-S, Son J-S, Lee S-H, Yarlagadda PKDV (2004) Optimal design of neural networks for control in robotic arc welding. Robot Comput-Int Manuf 20(1):57–63CrossRefGoogle Scholar
  19. 19.
    Ismail MIS, Okamoto Y, Okada A (2013) Neural network modeling for prediction of weld bead geometry in laser microwelding. Adv Opt Technol 2013(Article ID 415837):7Google Scholar
  20. 20.
    Iqbal A, Khan SM, Mukhtar, Sahir H (2011) ANN assisted prediction of weld bead geometry in gas tungsten arc welding of HSLA steels. In: Proceedings of the World congress on engineering, vol I, WCE 2011. LondonGoogle Scholar
  21. 21.
    Huang W, Kovacevic R (2011) A laser-based vision system for weld quality inspection. J Sensors 11:506–521CrossRefGoogle Scholar
  22. 22.
    Chan B, Pacey J, Bibby M (1999) Modelling gas metal arc weld geometry using artificial neural network technology. Can Metall Q 38(1):43–51Google Scholar
  23. 23.
    Kim IS, Kwon WH, Siores E (1996) An investigation of a mathematical model for predicting weld bead geometry. Can Metall Q 35(4):385–392CrossRefGoogle Scholar
  24. 24.
    Sreeraj P, Kannan T (2012) Modelling and prediction of stainless steel clad bead geometry deposited by GMAW using regression and artificial neural network models. Adv Mech Eng 2012(Article ID 237379):1–12Google Scholar
  25. 25.
    Akkas N, Karayel D, Ozkan SS, Ogur A, Topal B (2013) Modeling and analysis of the weld bead geometry in submerged arc welding by using adaptive neurofuzzy inference system mathematical problems in engineering 2013(Article ID 473495):10Google Scholar
  26. 26.
    Yanling X, Huanwei Y, Zhong J, Lin T, Chen S (2012) Real-time image capturing and processing of seam and pool during robotic welding process. Ind Robot Int J 39(5):513–523CrossRefGoogle Scholar
  27. 27.
    Ye Z, Fang G, Chen S, Dinham M (2013) A robust algorithm for weld seam extraction based on prior knowledge of weld seam. Sensor Rev 33(2):125–133CrossRefGoogle Scholar
  28. 28.
    Fang Z, De X, Ta M (2013) Vision-based initial weld point positioning using the geometric relationship between two seams. Int J Adv Manuf Technol 66:1535–1543CrossRefGoogle Scholar
  29. 29.
    Yanling X, Fang G, Chen S, Zou JJ, Ye Z (2014) Real-time image processing for vision-based weld seam tracking in robotic GMAW. Int J Adv Manuf Technol 73:1413–425CrossRefGoogle Scholar
  30. 30.
    Hardle W, Steiger W (1994) Optimal median smoothingGoogle Scholar
  31. 31.
    In: Small BB, Western electric company. Statistical quality control handbook. AT&;T. Mack Printing Company: Easton, pp 161–183 (1958)Google Scholar
  32. 32.
    Zhang M, Cheng W (2015) Recognition of mixture control chart pattern using multiclass support vector machine and genetic algorithm based on statistical and shape features. Math Problems Eng 2015(Article ID 382395):10Google Scholar
  33. 33.
    Wang J, Kochhar AK, Hannam RG (1998) Pattern recognition for statistical process control charts. Int J Adv Manuf Technol 14:99–109CrossRefGoogle Scholar
  34. 34.
    Swift JA, Mize JH (1995) Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems. Comput Indus Eng 28(1):81–91CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Reyes Rios-Cabrera
    • 1
  • America B. Morales-Diaz
    • 1
  • Jaime F. Aviles-Viñas
    • 1
  • Ismael Lopez-Juarez
    • 1
  1. 1.Robotics and Advanced ManufacturingCINVESTAV-IPNRamos ArizpeMexico

Personalised recommendations