Data mining in resistance spot welding

A non-destructive method to predict the welding spot diameter by monitoring process parameters
  • Ingo Boersch
  • Uwe Füssel
  • Christoph Gresch
  • Christoph Großmann
  • Benjamin Hoffmann
ORIGINAL ARTICLE
  • 108 Downloads

Abstract

Resistance spot welding is the dominant process in the present mass production of steel constructions without sealing requirements with single sheet thicknesses up to 3 mm. Two of the main applications of resistance spot welding are the automobile and the railway vehicle manufacturing industry. The majority of these connections has safety-related character and therefore they must not fall below a certain weld diameter. Since resistance spot welding has been established, this weld diameter has been usually used as the gold standard. Despite intensive efforts, there has not been found yet a reliable method to detect this connection quality non-destructively. Considerable amounts of money and steel sheets are wasted on making sure that the process does not result in faulty joints. The indication of the weld diameter by in-process monitoring in a reliable way would allow the quality documentation of joints during the welding process and additionally lead through demand-actuated milling cycles to a substantial decrease of electrode consumption. An annual, estimated reduction in the seven- to nine-figure range could be achieved. It has an important impact, because the economics of the process is essentially characterized by the electrode caps (Klages 24). We propose a simple and straightforward approach using data mining techniques to accurately predict the weld diameter from recorded data during the welding process. In this paper, we describe the methods used during data preprocessing and segmentation, feature extraction and selection, and model creation and validation. We achieve promising results during an analysis of more than 3000 classified welds using a model tree as a predictor with a success rate of 93 %. In the future, we hope to validate our model with unseen welding data and implement it in a real world application.

Keywords

Resistance spot welding Electrode life prognosis Data mining Feature extraction Model selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Afshari D, Sedighi M, Karimi MR, Barsoum Z (2014) Prediction of the nugget size in resistance spot welding with a combination of a finite-element analysis and an artificial neural network. Mater Technol 48(1):33–38. ISSN 1580–2949Google Scholar
  2. 2.
    Arndt B, Hoffmann B (2013) Segmentierung und Merkmalsdefinition mehrkanaliger Messdaten zur Prognose bei einem punktförmigen Fügeverfahren. In: Fischer A, Oesterreich M, Scheidat T (eds) 14. Nachwuchswissenschaftlerkonferenz ost- und mitteldeutscher Fachhochschulen NWK 14, Verlag Werner HülsbuschGoogle Scholar
  3. 3.
    Boersch I, Heinsohn J, Socher R (2007) Wissensverarbeitung - Eine Einführung in die Künstliche Intelligenz für Informatiker und Ingenieure, 2nd edn. Spektrum Akademischer VerlagGoogle Scholar
  4. 4.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    D8.1M:2007 (2007) Specification for automotive weld quality resistance spot welding of steel. ISBN 978-0-87171-065-9Google Scholar
  6. 6.
    Das M, Strausbaugh J, Fernandez V, Grzadzinski G (2007) Method for estimating nugget diameter and weld parameters http://www.google.de/patents/US7244905, US Patent 7,244,905
  7. 7.
    DVS 2902-3 (2015) Widerstandspunktschweißen von Stählen bis 3 mm Einzeldicke - Konstruktion und BerechnungGoogle Scholar
  8. 8.
    EN 10346 (2009) Continuously hot-dip coated steel flat products - technical delivery conditionsGoogle Scholar
  9. 9.
    Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37– 54Google Scholar
  10. 10.
    Garza F, Das M (2001) On real time monitoring and control of resistance spot welds using dynamic resistance signatures. In: Proceedings of the 44th IEEE 2001 midwest symposium on circuits and systems. MWSCAS 2001, vol 1. IEEE, pp 41–44Google Scholar
  11. 11.
    Großmann C, Füssel U, Mathiszik C, Zschetzsche J (2014) Resistance spot welding – quality assurance and new testing methods. In: Tailored joining 2014 - proceedings of the international symposium tailored joining, Fraunhofer IWS Dresden & Technische Universität Dresden, vol 2, pp J11, 1–14Google Scholar
  12. 12.
    Haapalainen E, Laurinen P, Junno H, Tuovinen L, Röning J (2005) Methods for classifying spot welding processes: A comparative study of performance. In: The 18th international conference on industrial & engineering applications of artificial intelligence & expert systemsGoogle Scholar
  13. 13.
    Haapalainen E, Koskimäki H, Laurinen P, Röning J, Tuovinen L (2007) Building a database to support intelligent computational quality assurance of resistance spot welding joints. Tech. rep., University of Oulu, Department of Computer Science and Engineering, Intelligent Systems GroupGoogle Scholar
  14. 14.
    Haapalainen E, Laurinen P, Junno H, Tuovinen L, Röning J (2008) Feature selection for identification of spot welding processes. In: Cetto J, Ferrier JL, Costa dias Pereira JM, Filipe J (eds) Informatics in control automation and robotics, lecture notes electrical engineering, vol 15. Springer Berlin Heidelberg, pp 69– 79Google Scholar
  15. 15.
    Hoffmann B, Mögelin J, Arndt B, Mosters C (2014) Data Mining beim Widerstandspunktschweißen: Vorgehensweise und erste Ergebnisse der Prognose von Punktdurchmessern. In: Gesellschaft für Informatik (ed) Lecture Notes in Informatics (LNI) Seminars 13 / Informatiktage 2014, pp 105–108Google Scholar
  16. 16.
    Holmes G, Hall M, Frank E (1999) Generating rule sets from model trees. In: Proceedings of the 12th Australian joint conference on artificial intelligence. Springer-Verlag, pp 1–12Google Scholar
  17. 17.
    ISO 14327 (2004) Resistance welding - procedures for determining the weldability lobe for resistance spot, projection and seam weldingGoogle Scholar
  18. 18.
    ISO 14373 (2015) Resistance welding - procedure for spot welding of uncoated and coated low carbon steelsGoogle Scholar
  19. 19.
    ISO 17677-1 (2009) Resistance welding - vocabulary - part 1: Spot, projection and seam weldingGoogle Scholar
  20. 20.
    ISO 18278-1 (2015) Resistance welding - weldability - part 1: General requirements for the evaluation of weldability for resistance spot, seam and projection welding of metallic materialsGoogle Scholar
  21. 21.
    ISO 18278-2 (2014) Resistance welding - weldability - part 2: Evaluation procedures for weldability in spot weldingGoogle Scholar
  22. 22.
    ISO 8166 (2003) Resistance welding - procedure for the evaluation of the life of spot welding electrodes using constant machine settingsGoogle Scholar
  23. 23.
    Jonata M, Neumann H (2008) Share of spot welding and other joining methods in automotive production. Weld World 52(3–4):12–16CrossRefGoogle Scholar
  24. 24.
    Klages EC (2014) Beurteilung der Beanspruchung von Elektrodenkappen beim Widerstandspunktschweißen von höher- und höchstfestem Stahl. Dissertation, Technische Universität Clausthal, ISBN-13: 978-3-8325-3868-2Google Scholar
  25. 25.
    Laurinen P, Junno H, Tuovinen L, Röning J (2004a) Studying the quality of resistance spot welding joints using self-organising maps. In: 4th international ICSC symposium on engineering of intelligent systems (EIS), pp 705–711Google Scholar
  26. 26.
    Laurinen P, Junno H, Tuovinen L, Röning J (2004b) Studying the quality of resistance spot welding joints using bayesian networks. In: Proceedings of artificial intelligence and applications, pp 705–711Google Scholar
  27. 27.
    Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81(1):667–684CrossRefGoogle Scholar
  28. 28.
    Mathiszik C, Großmann C, Zschetzsche J, Füssel U (2016) Zerstörungsfreie Bewertung des Linsendurchmessers beim Widerstandspunktschweißen mit magnetischen Prüfverfahren. Schweissen und Schneiden 68(1/2)Google Scholar
  29. 29.
    Mögelin J, Mosters C (2013) Merkmalsselektion und transparente Modellierung zur Prognose einer Zielgröße bei einem punktförmigen Fügeverfahren. In: Fischer A, Oesterreich M, Scheidat T (eds) 14. Nachwuchswissenschaftlerkonferenz ost- und mitteldeutscher Fachhochschulen NWK 14 (18.04.2013), Verlag Werner HülsbuschGoogle Scholar
  30. 30.
    Muhammad N, Manurung YH (2012) Design parameters selection and optimization of weld zone development in resistance spot welding. World Acad Sci Eng Technol 6(11):1184–1189. ISSN 1307–6892Google Scholar
  31. 31.
    National Instruments (2015) TDMS File Format Internal Structure., http://www.ni.com/white-paper/5696/en/, accessed: 2015-10-01
  32. 32.
    OICA (2015) 2014 Production Statistics., http://www.oica.net/category/production-statistics/, accessed: 2015-02-01
  33. 33.
    Park Y, Cho H (2004) Quality evaluation by classification of electrode force patterns in the resistance spot welding process using neural networks. Proc Inst Mech Eng B J Eng Manuf 218(11):1513–1524CrossRefGoogle Scholar
  34. 34.
    Quinlan JR (1992) Learning with continuous classes. In: Proceedings of the Australian joint conference on artificial intelligence. World Scientific, Singapore, pp 343–348Google Scholar
  35. 35.
    Rivas S, Servent R, Belda J (2004) Automated spot welding in the automotive industry. In: 16th World Conference on NDT, NDT.net. Bad Breisig, GermanyGoogle Scholar
  36. 36.
    Ruisz J, Biber J, Loipetsberger M (2007) Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision. Int J Adv Manuf Technol 33(9–10):952–960CrossRefGoogle Scholar
  37. 37.
    Schlichting J (2012) Integrale Verfahren der aktiven Infrarotthermografie. Dissertation, Technische Universität BerlinGoogle Scholar
  38. 38.
    Tao F, Zhang L, Venkatesh VC, Luo Y, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model. Proc Inst Mech Eng B J Eng Manuf 225(10):1969–1976. http://pib.sagepub.com/content/225/10/1969.full.pdf+html CrossRefGoogle Scholar
  39. 39.
    Tao F, Cheng Y, Zhang L, Nee AYC (2015) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 1–16Google Scholar
  40. 40.
    Voigt A (2015) Entwicklung eines stoffschlüssigen Fügeverfahrens zum Fügen eines Stahl-Kunststoff-Verbundbleches mit höchstfesten Stahl. Dissertation, Technische Universität Dresden, Fakultät Maschinenwesen. ISBN-13: 978-3959080071Google Scholar
  41. 41.
    Wan X, Wang Y, Zhao D (2016) Quality monitoring based on dynamic resistance and principal component analysis in small scale resistance spot welding process. Int J Adv Manuf Technol 86(9):3443–3451CrossRefGoogle Scholar
  42. 42.
    Wang Y, Witten IH (1997) Inducing model trees for continuous classes. In: Proceedings of the 9th European conference on machine learning poster papers, pp 128–137Google Scholar
  43. 43.
    Witkin AP (1983) Scale-space filtering. In: Proceedings of the 8th international joint conference on artificial intelligence, IJCAI’83, vol 2. Morgan Kaufmann Publishers Inc., San Francisco, pp 1019–1022Google Scholar
  44. 44.
    Yongyan L, Weimin Z, Haitao X, Jian D (2012) Defect recognition of resistance spot welding based on artificial neural network. In: Wu Y (ed) Software engineering and knowledge engineering: theory and practice, vol 2. Springer Berlin Heidelberg, pp 423– 430Google Scholar
  45. 45.
    Yu J (2015) Quality estimation of resistance spot weld based on logistic regression analysis of welding power signal. Int J Precis Eng Manuf 16(13):2655–2663CrossRefGoogle Scholar
  46. 46.
    Zhang H, Hou Y, Zhang J, Qi X, Wang F (2015) A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier. Int J Adv Manuf Technol 78(5):841–851CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Ingo Boersch
    • 1
  • Uwe Füssel
    • 2
  • Christoph Gresch
    • 1
  • Christoph Großmann
    • 2
  • Benjamin Hoffmann
    • 1
  1. 1.Department of Informatics and MediaUniversity of Applied Sciences BrandenburgBrandenburg an der HavelGermany
  2. 2.Institute of Manufacturing Science and EngineeringTechnische Universität DresdenDresdenGermany

Personalised recommendations