A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures

Journal of Intelligent Manufacturing

Volumn 22, Issue 2, 2011, Pages 131-143

  Huang, Wei ^a   Kovacevic, Radovan ^b  

^a SOUTHERN METHODIST UNIVERSITY   (United States)

^b Southern Methodist University   (United States)

Author keywords

Acoustic signal; Depth of weld penetration; Multiple regression; Neural network

Indexed keywords

ACOUSTIC SIGNALS; ACOUSTIC SIGNATURE; DEPTH OF WELD PENETRATION; FREQUENCY DOMAINS; LASER WELDING PARAMETERS; MULTIPLE REGRESSION ANALYSIS; MULTIPLE REGRESSIONS; NOISE REDUCTION METHODS; SPECTRAL SUBTRACTIONS; TIME DOMAIN; WELD PENETRATIONS; WELDING PROCESS;

ACOUSTIC VARIABLES MEASUREMENT; ACOUSTIC WAVES; ELECTRIC WELDING; FREQUENCY DOMAIN ANALYSIS; HIGH STRENGTH STEEL; LASER BEAM WELDING; REGRESSION ANALYSIS; TIME DOMAIN ANALYSIS; WELDS;

NEURAL NETWORKS;

EID: 79952677427     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-009-0267-9     Document Type: Article

References (20)

1. Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. Acoustic, Speech, and Signal Processing, IEEE Transactions, 27, 113-120.
2. Chao, P.Y., &Hwang, Y. D. (1997).An improved neural network model for the prediction of cutting tool life. Journal of Intelligent Manufacturing, 8, 107-115. (Pubitemid 127507754)
3. Chumakov, R. (2008). An artificial neural network for fault detection in the assembly of thread-forming screws. Journal of Intelligent Manufacturing, 19, 327-333.
4. Farson, D., Hillsley, K., Sames, J., &Young, R. (1996). Frequency-time characteristics of airborne signals from laser welds. Journal of Laser Applications, 8, 33-42. (Pubitemid 126650993)
5. Gu, H., & Duley, W. W. (1996a). Resonant acoustic emission during laser welding of metals. Journal of Physics D: Applied Physics, 29, 550-555. (Pubitemid 126548236)
6. Gu, H., & Duley, W. W. (1996b). A statistical approach to acoustic monitoring of laser welding. Journal of Physics D: Applied Physics, 29, 555-560.
7. Gu, H., & Duley, W. W. (1996c). Analysis of acoustic signals detected from different locations during laser beam welding of steel sheet. In ICALEO (pp. 40-48), Detroit, MI.
8. Klein, T., Vicanek, M., Kroos, J., Decker, I., &Simon, G. (1994). Oscillations of the keyhole in penetration laser beam welding. Journal of Physics D: Applied Physics, 27, 2023-2030.
9. Kroos, J., Gratzke, U., Vicanek, M., & Simon, G. (1993). Dynamic behavior of the keyhole in laser welding. Journal of Physics D: Applied Physics, 26, 481-486. (Pubitemid 23622261)
10. Kutschenreiter-Praszkiewicz, I. (2008). Application of artificial neural network for determination of standard time in machining. Journal of Intelligent Manufacturing, 19, 233-240.
11. Kwak, C., Ventura, J. A., & Tofang-Sazi, K. (2000). A neural network approach for detect identification and classification on leather fabric. Journal of Intelligent Manufacturing, 11, 485-499. (Pubitemid 32067224)
12. Manolakis, D. G., Ingle, V. K., & Kogon, S. M. (1999). Statistical and adaptive signal processing (pp. 227-232). Boston: McGraw-Hill.
13. Mohanasundararaju, N., Sivasubramanian, R., Gnanaguru, R., & Alagumurthy, N. (2008). A neural network and fuzzy-based methodology for the prediction of work roll surface roughness in a grinding process. International Journal for Computational Methods in Engineering Science and Mechanics, 9, 103-110. (Pubitemid 351190287)
14. 2 laser welding by using multiple regression and neural network. Journal of Laser Application, 11, 143-150. (Pubitemid 129526840)
15. Saad, E., Wang, H., & Kovacevic, R. (2006). Classification of molten pool modes in variable polarity plasma arcwelding based on acoustic signatures. Journal of Materials Processing Technology, 174, 127- 136. (Pubitemid 44287890)
16. Shimada, W., Ohmine, M., Hoshinouchi, S., & Kobayashi, M. (1982). A study on in-process assessment of joint efficiency in laser welding process. In The fourth international symposium of the Japan Welding Society (pp. 175-180), Osaka, Japan. (Pubitemid 14523640)
17. Sun, A., & Elijah Kannatey-Asibu, J. (1999). Sensor systems for realtime monitoring of laser weld quality. Journal of Laser Applications, 11, 153-168. (Pubitemid 129527039)
18. Sukthomya, W., & Tannock, J. (2005). The training of neural networks to model manufacturing processes. Journal of Intelligent Manufacturing, 16, 39-51. (Pubitemid 40356754)
19. Wang, X., Wang, W., Huang, Y., Nguyen, N., & Krishnakumar, K. (2008).Design of neural network-based estimator for tool wear modeling in hard turning. Journal of Intelligent Manufacturing, 19, 383- 396.
20. Wilcox, J. A. D., & Wright, D. T. (1998). Towards pultrusion process optimization using artificial neural networks. Journal of Materials Processing Technology, 83, 131-141. (Pubitemid 128408293)