Multi criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm

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 mu m 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.