Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Defect Detection in Laser Welding Using Multidimensional Discretization and Event-Codification

Multidimensional Discretization과 Event-Codification 기법을 이용한 레이저 용접 불량 검출

  • Baek, Su Jeong (Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology) ;
  • Oh, Rocku (Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Duck Young (Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology)
  • 백수정 (울산과학기술원 인간 및 시스템 공학과) ;
  • 오록규 (울산과학기술원 인간 및 시스템 공학과) ;
  • 김덕영 (울산과학기술원 인간 및 시스템 공학과)
  • Received : 2015.02.09
  • Accepted : 2015.08.20
  • Published : 2015.11.01
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Abstract

In the literature, various stochastic anomaly detection methods, such as limit checking and PCA-based approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by $Na{\ddot{i}}ve$ Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC's LWM$^{TM}$ and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).

Keywords

References

  1. Kim, J.-D. and Kim, Y.-S., "The State of in-Process Quality Monitoring Technology in Laser Welding," Korean Welding and Joining Society, Vol. 18, No. 4, pp. 20-27, 2000.
  2. Shao, J. and Yan, Y., "Review of Techniques for on-Line Monitoring and Inspection of Laser Welding," Journal of Physics: Conference Series, Vol. 15, pp. 101-107, 2005. https://doi.org/10.1088/1742-6596/15/1/017
  3. Qin, S. J., "Survey on Data-Driven Industrial Process Monitoring and Diagnosis," Annual Reviews in Control, Vol. 36, No. 2, pp. 220-234, 2012. https://doi.org/10.1016/j.arcontrol.2012.09.004
  4. Park, Y.-W., "Weld Process Monitoring Technology in Laser Welding," Journal of Welding and Joining, Vol. 30, No. 1, pp. 27-32, 2012. https://doi.org/10.5781/KWJS.2012.30.1.27
  5. Kwon, S. J., Seo, J. W., Kim, J. C., and Jun, H. K., "Defect Evaluation for Weld Specimen of Bogie Using Infrared Thermography," J. Korean Soc. Precis. Eng., Vol. 32, No. 7, pp. 619-625, 2015. https://doi.org/10.7736/KSPE.2015.32.7.619
  6. Kim, D. H., Shin. H. J., and Yoo, Y. T., "A Study on the Digital Filter and Wavelet Transform of Monitoring for Laser Welding," J. Korean Soc. Precis. Eng., Vol. 30, No. 1, pp. 67-76, 2013. https://doi.org/10.7736/KSPE.2013.30.1.67
  7. Macgregor, J. and Kourti, T., "Statistical Process Control of Multivariate Processes," Control Engineering Practice, Vol. 3, No. 3, pp. 403-414, 1995. https://doi.org/10.1016/0967-0661(95)00014-L
  8. You, D., Gao, X., and Katayama, S., "Multisensor Fusion System for Monitoring High-Power Disk Laser Welding Using Support Vector Machine," IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 1285-1295, 2014. https://doi.org/10.1109/TII.2014.2309482
  9. Park, H., Rhee, S., and Kim, D., "A Fuzzy Pattern Recognition Based System for Monitoring Laser Weld Quality," Measurement Science and Technology, Vol. 12, No. 8, pp. 1318-1324, 2001. https://doi.org/10.1088/0957-0233/12/8/345
  10. You, D., Gao, X., and Katayama, S., "A Novel Stability Quantification for Disk Laser Welding by Using Frequency Correlation Coefficient between Multiple-Optics Signals," IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 1, pp. 327-337, 2015. https://doi.org/10.1109/TMECH.2014.2311097
  11. Oh, R. and Kim, D. Y., "A Fault Detection Method of Laser Welding based on PDF Estimation and Dempster-Shafer Theory," Proc. of the Society of CAD/CAM Engineers Conference, pp. 1011-1016, 2014.
  12. Baek, S. J. and Kim, D. Y., "A Comprative Study of Engine Fault Detection Methods," Proc. of the Society of CAD/CAM Engineers Conference, pp. 243-248, 2013.
  13. Jiang, S.-Y., Li, X., Zheng, Q., and Wang, L.-X., "Approximate Equal Frequency Discretization Method," Proc. of the IEEE on Intelligent Systems, Vol. 3, pp. 514-518, 2009.
  14. Cheng, J., Bell, D. A., and Liu, W., "Learning Belief Networks from Data: An Information Theory Based Approach," Proc. of the 6th International Conference on Information and Knowledge Management, pp. 325-331, 1997.
  15. Baek, S. J., Kim, D. Y., and Baek, W. S., "On the Detection Sensitivity of Multi-Dimensional Discretization Parameters for Automotive Engine Diagnostics," Proc. of KSPE Autumn Conference, p. 72, 2014.