Multi Criteria Optimization of Laser Percussion Drilling Process Using Artificial Neural Network Model Combined with Genetic Algorithm

ABSTRACT This is a study of laser percussion drilling optimization by combining the neural network method with the genetic algorithm. First, optimum input parameters of the process were obtained in order to optimize every single output parameter (response) of the zprocess regardless of their effect on each other (single criterion optimization). Then, optimum input parameters were obtained in order to optimize the effect of all output parameters in a multicriteria manner. Artificial neural network (ANN) method was employed to develop an experimental model of the process according to the experimental results. Then optimum input parameters (peak power, pulse width, pulse frequency, number of pulses, assist gas pressure, and focal plane position) were specified by using the genetic algorithm (GA). The output parameters include the hole entrance diameter, circularity of hole entrance and hole exit, and hole taper. The tests were carried out on mild steel EN3 sheets, with 2.5 mm thickness. The sheets were drilled by a 400 w pulsed Nd:YAG laser emitting at 1.06 µm wave length. Oxygen was employed as the assist gas. Considering the accuracy of the optimum numerical results and the high capability of the neural network in modeling, this method is reliable and precise and confirms the qualitative results in the previous studies. As a result, one can use this method to optimally adjust input parameters of the process in multicriteria optimization mode, which indicates substitute application of the method for optimizing the laser percussion drilling process.

[1]  M. Ghoreishi,et al.  Statistical modelling of laser percussion drilling for hole taper and circularity control , 2002 .

[2]  C. K. Kwong,et al.  A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Moulding , 2001 .

[3]  Franci Cus,et al.  Optimization of cutting conditions during cutting by using neural networks , 2003 .

[4]  Lin Li,et al.  Comparative statistical analysis of hole taper and circularity in laser percussion drilling , 2002 .

[5]  Lin Li,et al.  The effect of laser peak power and pulse width on the hole geometry repeatability in laser percussion drilling , 2001 .

[6]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[7]  D. Hand,et al.  Investigation of the Nd:YAG laser percussion drilling process using factorial experimental design , 1999 .

[8]  Ali M. S. Zalzala,et al.  Hybridisation of neural networks and a genetic algorithm for friction compensation , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[9]  Masayuki Yamamura,et al.  A Genetic Algorithm for Function Optimization , 2002 .

[10]  M. Ghoreishi,et al.  Statistical analysis of repeatability in laser percussion drilling , 2006 .

[11]  B. Yilbas.  Parametric study to improve laser hole drilling process , 1997 .

[12]  Lin Li,et al.  Hole Taper Characterisation and Control in Laser Percussion Drilling , 2002 .

[13]  S. Joshi,et al.  Geometrical features and metallurgical characteristics of Nd:YAG laser drilled holes in thick IN718 and Ti–6Al–4V sheets , 2002 .

[14]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[15]  Lin Li,et al.  Repeatability characteristics of laser percussion drilling of stainless-steel sheets , 2003 .