A new criterion for quality monitoring of pulsed laser spot welding using an infrared sensor Part 2: Quality estimation using an artificial neural network

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 213, Issue 1, 1999, Pages 51-57

  Lim, D C ^a   Gweon, D G ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Artificial neural network; In process monitoring; IR sensor; Nd:YAG laser; Pulsed laser spot welding; Weld strength estimation

Indexed keywords

CORRELATION METHODS; LEARNING SYSTEMS; NEODYMIUM LASERS; NEURAL NETWORKS; OPTICAL SENSORS; PULSED LASER APPLICATIONS; SPOT WELDING; WELDS;

POINT INFRARED SENSORS; RADIATION FEATURES; WELD STRENGTH ESTIMATION;

LASER BEAM WELDING;

EID: 0032629258     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/0954405991516642     Document Type: Article

References (14)

1. Lim, D.-C.,Cho, Y.-B. and Gweon, D.-G. A nondestructive inspection of laser spot welding using IR sensor. In Proceedings of PCM'96, Seoul, Korea, 28-30 October 1996, Vol. 2,pp. 617-622.
2. Lim, D.-C.,Cho, Y.-B.and Gweon, D.-G. A robust in-process monitoring of pulsed laser spot welding using a point infrared sensor. Proc. Instn Mech. Engrs, Part B, Journal of Engineering Manufacture, 1998, 212(B3), 241-250.
3. Lim, D.-C.,Cho, Y.-B.and Gweon, D.-G. A development of non-destructive inspection system for electron gun using IR sensor (in Korean). Research Report, KAIST, 1996.
4. Rdilly, R.,Xu, X.and Jones, J. E. Neural network application to acoustic emission signal processing. InProceedings of International Conference on Computerization of Welding Information IV, Orlando, Florida, 3-6 November 1992, pp. 146-160.
5. Matteson, A.,Morris, R.and Tate, R. Real-time GMAW quality classification using artificial neural network with airborne acoustic signal as input. InProceedings of International Conference on Computerization of Welding Information IV, Orlando, Florida, 3-6 November 1992, pp. 189-206.
6. Stroud, R.,Vincent, D.and McCardle, J. The application of neural networks to the classification of metal transfer mode in metal inert gas welding. InProceedings of International Conference on Joining of Material, Helsingor, Denmark, 31 May-2 June 1995, pp. 459-468.
7. Hertz, J.,Krogh, A.and Palmer, R. G. Introduction to the Theory of Neural Computation, 1992,pp. 214-310 (Addison-Wesley, Reading, Massachusetts).
8. Freeman, J. A.and Shapura, D. M.Neural Networks, 1994 (Addison-Wesley, Reading, Massachusetts).
9. Cichochi, A.and Unbehauen, R.Neural Networks for Optimization and Signal Processing, 1992,pp. 424-478 (John Wiley, Chichester).
10. Hecht-Nielsen, R. Kolmogorov mapping neural network existence theorem. InProceedings of IEEE International Conference on Neural Networks, 1987, Vol. 3, pp. 11-13.
11. Hornik, K.,Stinchcombe, M.and White, H. Multilayer feedforward networks are universal approximators.Neural Networks, 1998, 2, 359-366.
12. Haralick, R. M.and Shapiro, L. G. Computer and Robot Vision, Vol. 1, 1990,pp. 393-401 (Addison-Wesley, Reading, Massachusetts).
13. Jain, A. K. Fundamentals of Digital Image Processing, 1995,pp. 347-357 (Prentice-Hall International, Inc.,Englewood Cliffs, New Jersey).
14. Newland, D. E. Random Vibrations. Spectral and Wavelet Analysis, 3rd edition, 1993 (Longman, London).