Modeling and optimization of Nd:YAG laser micro-weld process using Taguchi Method and a neural network

The use of a pulsed Nd:YAG laser in the 0.1 mm- thick aluminum alloy lap micro-weld process was optimized. The welding parameters that influence the quality of the pulsed Nd:YAG laser lap micro-weld were evaluated by measuring of the tensile-shear strength. In this work, the Taguchi method was adopted to perform the initial optimization of the pulsed Nd:YAG laser micro-weld process parameters. A neural network with a Levenberg-Marquardt back-propagation (LMBP) algorithm was then adopted to develop the relationships between the welding process parameters and the tensile-shear strength of each weldment. The optimal parameters of the pulsed Nd:YAG laser micro-weld process were determined by simulating parameters using a well-trained back-propagation neural network model. Experimental results illustrate the proposed approach.

[1]  R. Roy.  A Primer on the Taguchi Method , 1990 .

[2]  Prasad K. Yarlagadda,et al.  Prediction of welding parameters for pipeline welding using an intelligent system , 2002 .

[3]  C. Wang,et al.  Optimization of Nd:YAG laser welding onto magnesium alloy via Taguchi analysis , 2005 .

[4]  Chao-Ton Su,et al.  Parameter design optimization via neural network and genetic algorithm , 2000 .

[5]  B. S. Lim,et al.  Optimal design of neural networks using the Taguchi method , 1995, Neurocomputing.

[6]  Fernando Morgado Dias,et al.  A sliding window solution for the on-line implementation of the Levenberg-Marquardt algorithm , 2006, Eng. Appl. Artif. Intell..

[7]  David W. Coit,et al.  STATIC NEURAL NETWORK PROCESS MODELS : CONSIDERATIONS AND CASE STUDIES , 1998 .

[8]  Martin T. Hagan,et al.  Neural network design , 1995 .

[9]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[10]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[11]  Kuldeep Kumar,et al.  A comparative study for the estimation of parameters in nonlinear models , 1996 .

[12]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  Chao-Ton Su,et al.  Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach , 2003, J. Intell. Manuf..

[15]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[16]  Genichi Taguchi,et al.  Taguchi methods : design of experiments , 1993 .