Abstract
This work describes an application of an integrated approach using the Taguchi method (TM), neural network (NN) and genetic algorithm (GA) for optimizing the lap joint quality of aluminum pipe and flange in automotive industry. The proposed approach (Taguchi-Neural-Genetic approach) consists of two phases. In first phase, the TM was adopted to collect training data samples for the NN. In second phase, a NN with a Levenberg-Marquardt back-propagation (LMBP) algorithm was adopted to develop the relationship between factors and the response. Then, a GA based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the Taguchi-Neural-Genetic approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bhadeshia H. K. D. H., Mackay D. J. C., Svensson L. E. (1995) Impact toughness of C-Mn steel arc welds-bayesian neural network analysis. Materials Science and Technology 11: 1046–1051
Cakar T., Yildirim M. B. (2005) A neuro-genetic approach to design and planning of a manufacturing cell. Journal of Intelligent Manufacturing 16: 453–462
Cary H. B. (1994) Modern welding technology (3rd ed.). Prentice Hall, Englewood Cliffs, NJ
David G. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company Inc, Reading, MA
Demuth H., Beale M. (1998) Neural network toolbox-for use with MATLAB. The Math Works, Inc, Natick, MA
Funahashi K. (1989) On the approximate realization of continuous mapping by neural network. Neural Networks 2: 183–192
Hagan M. T., Demuth H., Beale M. (1996) Neural network design. PWS Publishing Co, Boston, MA
Hagan M. T., Menhaj M. B. (1994) Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6): 989–993
Haykin S. (1994) Neural Networks-A comprehensive foundation. Macmillan College Publishing Co, New York
Khaw J. F. C., Lim B. S., Lim L. E. N. (1995) Optimal design of neural networks using Taguchi method. Neurocomputing 7: 225–245
Kim I. S., Jeong Y. J., Lee C. W. (2003) Prediction of welding parameters for pipeline welding using an intelligent system. International Journal of Advanced Manufacturing Technology 22: 713–719
Ross P. J. (1996) Taguchi techniques for quality engineering. McGraw-Hill, New York
Su C. T., Chiang T. L. (2003) Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach. Journal of Intelligent Manufacturing 14: 229–238
Su C. T., Chiu C. C., Chang H. H. (2000) Parameter design optimization via neural network and genetic algorithm. International Journal of Industrial Engineering 7(3): 224–231
Taguchi G., Elsayed E. A., Hsiang T. C. (1989) Quality engineering in production systems. McGraw-Hill, New York
Yu J. C., Chen X. X., Hung T. R. (2004) Optimization of extrusion blow molding processes using soft computing and Taguchi’s method. Journal of Intelligent Manufacturing 15: 625–634
Zhu J. H., Young B. (2007) Effects of transverse welds on aluminum alloy columns. Thin-Walled Structures 45: 321–329
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper has not been published elsewhere nor has it been submitted for publication elsewhere.
Rights and permissions
About this article
Cite this article
Lin, HL. Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi-Neural-Genetic approach. J Intell Manuf 23, 679–686 (2012). https://doi.org/10.1007/s10845-010-0418-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-010-0418-z