Optimization Welding Process Parameters through Response Surface, Neural Network and Genetic Algorithms

Since the Neural Network (NN) with a Genetic Algorithm (GA) as a complement; are good optimization tools, we compare its performance with the Response Surface Methodology (RSM) that is generally used in the optimization of the process, in this case welding process. For the data used in the comparison, the results show that NN plus GA and RSM have a good results and very well performance, for identify the optimal set of parameters to obtain amaximum response of the process.

[1]  Roger Hoerl,et al.  Ridge Analysis 25 Years Later , 1985 .

[2]  S. Lek,et al.  Macroinvertebrate assemblages in glacial stream systems: A comparison of linear multivariate methods with artificial neural networks , 2007 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  John J. Peterson A General Approach to Ridge Analysis With Confidence Intervals , 1993 .

[5]  Yong-Soo Kim,et al.  Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size , 2008, Expert Syst. Appl..

[6]  A Large-Sample Confidence Region Useful , 1990 .

[7]  Özer Çinar,et al.  Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity , 2006 .

[8]  Hamdy K. Elminir,et al.  Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models , 2007 .

[9]  Lieva Van Langenhove,et al.  Optimising a production process by a neural network/genetic algorithm approach , 1996 .

[10]  Derek J. Pike,et al.  Empirical Model‐building and Response Surfaces. , 1988 .

[11]  Hsiao-Tien Pao,et al.  A comparison of neural network and multiple regression analysis in modeling capital structure , 2008, Expert Syst. Appl..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  V. Gunaraj,et al.  Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes , 1999 .

[14]  S. Bisgaard,et al.  Standard errors for the eigenvalues in second-order response surface models , 1996 .

[15]  H. M. Hosseini,et al.  Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity , 2006 .

[16]  Mitsuo Gen,et al.  Genetic Algorithms & Engineering Optimization , 2000 .

[17]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

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

[19]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .