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A Neuro-fuzzy Approach to Part Fitup Fault Control During Resistance Spot Welding Using Servo Gun

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Resistance spot welding (RSW) is widely utilized as a joining technique for automobile industry. However, good weld quality control method has not yet been developed in plant environment when part fitup fault exists. This paper proposed a neuro-fuzzy algorithm to control weld quality by adjusting welding current. An experimental system was developed to measure electrode displacement curve. Accordingly based on electrode displacement curve optimal current for every cycle will be achieved under poor fitup fault condition. Results showed that proposed neuro-fuzzy system is suitable as a weld quality monitoring for resistance spot welding.

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References

  1. Nagel, G.L., Lee, A.: A new approach to spot welding feedback control, SAE Technical Paper, No. 880371 (1988)

    Google Scholar 

  2. Karagoulis, M.: Nuts-and-Bolts approach to the control of resistance spot welding. Welding Journal 73(7), 27–31 (1995)

    Google Scholar 

  3. Li, W., Cheng, S., Hu, S.J.: Statistical Investigation on Resistance Spot Welding Quality Using a Two-State, Sliding-Level Experiment. Journal of Manufacturing Science and Engineering-Transactions of The ASME 123(8), 513–520 (2001)

    Article  Google Scholar 

  4. Cho, Y., Hu, S.J., Li, W.: Resistance spot welding of aluminum and steel: a comparative experimental study. Proceedings of the institution of mechanical engineers part B-Journal of Engineering Manufacture 217(7), 1355–1363 (2003)

    Article  Google Scholar 

  5. Tsai, C.L., Dai, W.L., Dickinson, D.W., Papritan, J.C.: Analysis and development of a real-time control methodology in resistance spot welding. Welding Research Supplement 12, 339–351 (1991)

    Google Scholar 

  6. Cho, H.S., Chun, D.W.: A microprocessor-based electrode movement controller for spot weld quality assurance. IEEE Transactions on Industrial Electronics 32(3), 234–238 (1985)

    Article  Google Scholar 

  7. Chang, H.S., Cho, Y.J., Choi, S.G., Cho, H.S.: A proportional-integral controller for resistance spot welding using nugget expansion. ASME Journal of Dynamic Systems, Measurement, and Control 111, 332–336 (1989)

    Article  Google Scholar 

  8. Messler Jr., R.W., Jou, M.: An intelligent control system for resistance spot welding using a neural network and fuzzy logic. In: Conference Record-IAS Annual Meeting, pp. 1757–1763 (1995)

    Google Scholar 

  9. Jou, M.: Experimental investigation of resistance spot welding for sheet metals used in automotive industry. JSME International Journal Series C-Mechanical Systems Machine Elements and Manufacturing 44(2), 544–552 (2001)

    Google Scholar 

  10. Khoo, L.P., Young, H.Y.: A prototype fuzzy resistance spot welding system. International Journal of Production Research 33(7), 2023–2036 (1995)

    Article  MATH  Google Scholar 

  11. Dilthey, U., Dickersbach, J.: Application of neural networks for quality evaluation for resistance spot welds. ISIJ International 39(10), 1061–1066 (1999)

    Article  Google Scholar 

  12. Lee, S.R., Choo, Y.J.: A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm. Journal of manufacturing systems, 320–328 (2001)

    Google Scholar 

  13. Jang, S.R.: ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man & Cybernetics 23(3), 665–685 (1993)

    Article  MathSciNet  Google Scholar 

  14. Kiguchi, K., Tanaka, T., Fukuda, T.: Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Trans. Fuzzy Systems 12, 481–490 (2004)

    Article  Google Scholar 

  15. Wang, L.P., Frayman, Y.: A Dynamically-generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles. Engineering Applications of Artificial Intelligence 15, 541–550 (2003)

    Article  Google Scholar 

  16. Lin, C.-M., Hsu, C.-F.: Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings. IEEE Trans. Fuzzy Systems 12, 733–742 (2004)

    Article  Google Scholar 

  17. Frayman, Y., Wang, L.P.: A Dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes. Soft Computing 6, 244–253 (2002)

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China

    Y. S. Zhang & G. L Chen

Authors
  1. Y. S. Zhang
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  2. G. L Chen
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Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, Y.S., Chen, G.L. (2005). A Neuro-fuzzy Approach to Part Fitup Fault Control During Resistance Spot Welding Using Servo Gun. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_135

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  • DOI: https://doi.org/10.1007/11539902_135

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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