Transactions of the Korean Society of Machine Tool Engineers (한국공작기계학회논문집)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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Development of Optimization Methodology for Laser Welding Process Automation Using Neural Network Model and Objective Function

레이저 용접공정의 자동화를 위한 신경망 모델과 목적함수를 이용한 최적화 기법 개발

  • 박영환 (한양대학교 BK21 혁신설계 기계인력 양성사업단)
  • Published : 2006.10.15
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Abstract

In manufacturing, process automation and parameter optimization are required in order to improve productivity. Especially in welding process, productivity and weldablity should be considered to determine the process parameter. In this paper, optimization methodology was proposed to determine the welding conditions using the objective function in terms of productivity and weldablity. In order to conduct this, welding experiments were carried out. Tensile test was performed to evaluate the weldability. Neural network model to estimate tensile strength using the laser power, welding speed, and wire feed rate was developed. Objective function was defined using the normalized tensile strength which represented the weldablilty and welding speed and wire feed rate which represented the productivity. The optimal welding parameters which maximized the objective function were determined.

Keywords

References

  1. Leong, K. H., Sabo, K. R., Altshuller, B., Wilkinson, T. L. and Albright, C. E., 1999, 'Laser Bean Welding of 5182 Aluminum Alloy Sheet,' Journal of Laser Applications, Vol. 11, No. 3, pp. 109-118 https://doi.org/10.2351/1.521881
  2. Pastor, M., Zhao, H., Martukanitz, R. P. and Debroy, T., 1999, 'Porosity, Underfill and Magnesium Loss during Continuous Wave Nd:YAG Laser Welding of Thin Plates of Aluminum Alloys 5182 and 5754,' Welding Journal, Vol. 78, No. 6, pp. 207s-216s
  3. Naeem, M. and Jessett, R., 1998, 'Aluminium Tailored Blank Welding With and Without Wire Feed, Using High Power Continuous Wave Nd:YAG Laser,' SAE Conference Proceedings P, No. 334, pp. 247-256
  4. Yoon, J. W., Lee, Y. S., Lee, K. D. and Park, K. Y., 2003, 'Effect of Filler Wire Composition on the Solidification Cracking of 6061 Aluminum Alloy Laser Welds,' Proceedings of the 2003 Fall Annual Meetings of Korean Welding Society, Vol. 41, pp. 98-100
  5. Salminen, A. S. and Kujanp, V. P., 2003, 'Effect of Wire Feed Position on Laser Welding with Filler Wire,' Journal of Laser Applications, Vol. 15, No. 1, pp. 2-10 https://doi.org/10.2351/1.1514220
  6. Park, H. and Rhee, S., 1999, 'Estimation of Weld Bead Size in $CO_2$ Laser Welding by Using Multiple Regression and Neural Network,' Journal of Laser Applications, Vol. 11, No. 3, pp. 143-150 https://doi.org/10.2351/1.521890
  7. Jeng, J. Y., Mau, T. F. and Leu, S. M., 2000, 'Prediction of Laser Butt Joint Welding Parameters Using Back Propagation and Learning Vector Quantization Networks,' Journal of Materials Processing Technology, Vol. 99, No, 1-3, pp. 207-218 https://doi.org/10.1016/S0924-0136(99)00394-5
  8. Chan, B., Pacey, J. and Bibby, M., 1999, 'Modeling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology,' Canadian Metallurgical Quarterly, Vol. 38, No. 1, pp. 43-51 https://doi.org/10.1016/S0008-4433(98)00037-8
  9. Tarng, Y. S., Tsai, H. L. and Yeh, S. S., 1999, 'Modeling, Optimization and Classification of Weld Quality in Tungsten Inert Gas Welding,' International Journal of Machine Tools & Manufacture, Vol. 39, No. 9, pp. 1427-1438 https://doi.org/10.1016/S0890-6955(99)00013-9
  10. Jung, W. J., Kim, J. L., Kim, J. H., Hong D. S., Kang, H. S. and Kim, D. S., 'Optimization of Expanding Velocity for a High-speed Tube Expander Using a Genetic Algorithm with a Neural Network,' Transactions of the Korean Society of Machine Tool Engineers, Vol. 14, No. 2, pp. 27-32
  11. Sette, S., Boullart, L. and Van Langenhove, L., 1996, 'Optimizing a Production Process by A Neural Network/Genetic Algorithm Approach,' Engineering Applications of Artificial Intelligence, Vol. 9, No. 6, pp. 681-689 https://doi.org/10.1016/S0952-1976(96)00062-0
  12. Demuth, H. B. and Beale, M., 1998, Neural network toolbox user's guide, Version 3, The Math Works Inc, pp. 5-36