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Qualitative and quantitative analysis of gmaw welding fault based on mahalanobis distance

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Abstract

The concept of calculating Mahalanobis distance (MD) was introduced in order to describe welding faults. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. The values of Mahalanobis distance obtained from the reference weldments were taken as a reference or a standard. Then, additional weldments were fashioned while artificial changing the flow rate of shielding gas and types of contact tips and simultaneously obtaining values for arc voltage and current at a rate of more than 8000 samples per second. Last, Mahalanobis distances for voltage and current values were calculated and used for qualitative and quantitative analysis with comparison to values obtained from the reference welds. The results described in this article confirm that Mahalanobis distances based on the short-term changes of welding parameters can be feasibly and effectively used for quality rating of welding faults in gas metal arc welding.

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References

  1. Cook, G. E., Maxwell, J. E., Barnett, R. J. and Strauss, A. M., “Statistical process control application to weld process,” IEEE Transactions on Industry Applications, Vol. 33, No. 2, pp. 454–463, 1997.

    Article  Google Scholar 

  2. Mao, W. and Ushio, M., “Measurement and theoretical investigation of arc sensor sensitivity in dynamic state during gas metal arc welding,” Science and Technology of Welding and Joining, Vol. 2, No. 5, pp. 191–198, 1997.

    Google Scholar 

  3. Adolfsson, S., Bahrami, A., Bolmsjo, G. and Claesson, I., “Online quality monitoring in short-circuit gas metal arc welding,” Welding Journal, Vol. 78, No. 2, pp. 59s–73s, 1999.

    Google Scholar 

  4. Luksa, K., “Influence of weld imperfection on short circuit GMA welding arc stability,” Journal of Materials Processing Technology, Vol. 175, No. 1–3, pp. 285–290, 2006.

    Article  Google Scholar 

  5. Luksa, K. and Rymarski, Z., “Collection of arc welding process data,” Journal of Achievement in Materials and Manufacturing Engineering, Vol. 17, No. 1–2, pp. 377–380, 2006.

    Google Scholar 

  6. Wu, C. S., Gao, J. Q. and Hu, J. K., “Real-time sensing and monitoring in robotic gas metal arc welding,” Measurement Science & technology, Vol. 18, No. 1, pp. 303–310, 2007.

    Article  Google Scholar 

  7. Simpson, S. W., “Statistics of signature images for arc welding fault detection,” Science and Technology of Welding and Joining, Vol. 12, No. 6, pp. 556–563, 2007.

    Article  Google Scholar 

  8. Simpson, S. W., “Signature images for arc welding fault detection,” Science and Technology of Welding and Joining, Vol. 12, No. 6, pp. 481–486, 2007.

    Article  Google Scholar 

  9. Simpson, S. W., “Through arc sensing in gas metal arc welding with signature images,” Science and Technology of Welding and Joining, Vol. 13, No. 1, pp. 80–86, 2008.

    Article  Google Scholar 

  10. Li, X. and Simpson, S. W., “Parametric approach to positional fault detection in short arc welding,” Science and Technology of Welding and Joining, Vol. 14, No. 2, pp. 146–151, 2009.

    Article  Google Scholar 

  11. Hyvarinen, A. and Oja, E., “A fast fixed-point algorithm for independent component analysis,” Neural Computation, Vol. 9, No. 7, pp. 1483–1492, 1997.

    Article  Google Scholar 

  12. Hyvarinen, A., “Fast and robust fixed-point algorithm for independent component analysis,” IEEE Transaction on Neural Networks, Vol. 10, No. 3, pp. 626–634, 1999.

    Article  Google Scholar 

  13. Taguchi, G. and Jugulum, R., “The Mahalanobis-Taguchi strategy: A pattern technology system,” John Wiley & Sons, pp. 6–8, 2002.

  14. Avishek, P. and Maiti, J., “Development of a hybrid methodology for dimensionality reduction in Mahalanobis-Taguchi system using Mahalanobis distance and binary particle swarm optimization,” Expert System with applications, Vol. 37, No. 2, pp. 1286–1293, 2010.

    Article  Google Scholar 

  15. Watanabe, Y., “Practice Taguchi methods,” JUSE Press, pp. 169–204, 2006.

  16. Rhee, B. O., Park, C. S., Chang, H. K., Jung, H. W. and Lee, Y. J., “Automatic generation of optimum cooling circuit for large injection molded parts,” Int. J. Precis. Eng. Manuf., Vol. 11, No. 3, pp. 439–444, 2010.

    Article  Google Scholar 

  17. Itagaki, M., Takamiya, E., Watanabe, K., Nukaga, T. and Umemura, F., “Diagnosis of quality of fresh water for carbon steel corrosion by Mahalanobis distance,” Corrosion Science, Vol. 49, No. 8, pp. 3408–3420, 2007.

    Article  Google Scholar 

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Correspondence to Shengqiang Feng.

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Feng, S., Hiroyuki, O., Hidennori, T. et al. Qualitative and quantitative analysis of gmaw welding fault based on mahalanobis distance. Int. J. Precis. Eng. Manuf. 12, 949–955 (2011). https://doi.org/10.1007/s12541-011-0127-3

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  • DOI: https://doi.org/10.1007/s12541-011-0127-3

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