Skip to main content
Log in

A study of multisensor information fusion in welding process by using fuzzy integral method

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Multisensor information fusion technology in welding process was studied, and Choquet fuzzy integral method was used in this paper to fuse the information obtained by arc, sound, and visual sensors in pulsed gas tungsten arc welding (GTAW) process. A novel method to obtain the fuzzy set function values in fuzzy integral was proposed to obtain the prediction results of single sensor, and a method by using supporting degrees of different sensors was proposed to obtain the fuzzy measure function values in fuzzy integral. Choquet fuzzy integral was used to fuse the information after the fuzzy set function and fuzzy measure values were obtained, and the multisensor information fusion model based on the method was proposed. Experiment was done to test the effectiveness of the method in the end.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Cheng L (2008) Latest progress of research on fault diagnosis based on information fusion. Inf Technol J 7(5):825–829

    Article  Google Scholar 

  2. Cai G, Du D, Tian Y, Hou R, Gao Z (2007) Defect detection of X-ray images of weld using optimized heuristic search based on image information fusion. Trans China Weld Inst 28(2):29–32+37

    Google Scholar 

  3. Carvalho EAN, Luciano BA, Freire RCS, Molina L, Freire EO. Fault-tolerant weld line detection for automatic inspection of storage tanks based on distance and visual information fusion. in 2009 I.E. Instrumentation and Measurement Technology Conference, I2MTC 2009, May 5, 2009 - May 7, 2009. 2009. Singapore, Singapore: IEEE Computer Society

  4. Chang Y, Su H, Lin B, Yang X (2007) Spot welding quality fuzzy control system based on multisensor information fusion. Chin J Mech Eng EN 20(4):36–39

    Article  Google Scholar 

  5. Chen B, Chen S (2010) Multi-sensor information fusion in pulsed GTAW based on fuzzy measure and fuzzy integral. Assem Autom 30(3):276–285

    Article  Google Scholar 

  6. Chen B, Wang J, Chen S (2010) A study on application of multi-sensor information fusion in pulsed GTAW. Ind Robot 37(2):168–176

    Article  Google Scholar 

  7. Chen B, Wang J, Chen S (2010) Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. Int J Adv Manuf Technol 48(1–4):83–94

    Article  Google Scholar 

  8. Pan C, Du S, Song Y, Li H (2007) Multiple information fusion and quality classification of aluminum alloy resistance spot welding. Chin J Mech Eng 43(8):181–185

    Article  Google Scholar 

  9. Fan CJ, Lv FL, Chen SB (2009) Visual sensing and penetration control in aluminum alloy pulsed GTA welding. Int J Adv Manuf Technol 42(1–2):126–137

    Article  Google Scholar 

  10. Grabisch M (1996) The representation of importance and interaction of features by fuzzy measures. Pattern Recogn Lett 17(6):567–575

    Article  Google Scholar 

  11. Grabisch M (1996) The application of fuzzy integrals in multicriteria decision making. Eur J Oper Res 89(3):445–456

    Article  MATH  Google Scholar 

  12. Sugeno M (1974) Theory of fuzzy integrals and its applications,[Doctoral Thesis], Tokyo,Tokyo Institute of Technology

  13. Sugeno M (1977) Fuzzy measures and fuzzy integrals: a survey. Fuzzy automata and decision processes:89–102

  14. Banon G (1981) Distinction between several subsets of fuzzy measures. Fuzzy Set Syst 5(3):291–305

    Article  MATH  MathSciNet  Google Scholar 

  15. Murofushi T, Sugeno M (1991) A theory of fuzzy measures: representations, the Choquet integral, and null sets. J Math Anal Appl 159(2):532–549

    Article  MATH  MathSciNet  Google Scholar 

  16. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  17. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69

    Article  MATH  MathSciNet  Google Scholar 

  18. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

  19. Yang H, Chan L, King I (2002) Support vector machine regression for volatile stock market prediction. Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning:391–396

  20. Ho C, Lee DT (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp 5(4):276–281

    Article  Google Scholar 

  21. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V Support vector regression machines. Advances in Neural Information Processing Systems-9:155–161

  22. Cho SB, Kim JH (1995) Multiple network fusion using fuzzy logic. IEEE Trans Neural Netw 6(2):497–501

    Article  Google Scholar 

  23. Jousselme AL, Grenier D, Bosse E (2001) A new distance between two bodies of evidence. Inf Fusion 2(2):91–101

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bo Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, B., Chen, S. & Feng, J. A study of multisensor information fusion in welding process by using fuzzy integral method. Int J Adv Manuf Technol 74, 413–422 (2014). https://doi.org/10.1007/s00170-014-6001-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6001-6

Keywords

Navigation