Parametric Optimization of Pulsed Nd:YAG Laser Micro-Grooving of Alumina through an Artificial Neural Network Model

Pulsed Nd:YAG laser beam has great ability for micro-machining of ceramic materials because of high laser beam intensity at low mean beam power, good focusing characteristics due to very small pulse duration, small kerf widths and narrow heat affected zones. In the present research, micro-grooving of alumina ceramic plate by pulsed Nd:YAG laser beam machine is studied. Selection of optimum machining parameter combinations for obtaining accuracy is the challenging task in laser micro-grooving operation. Nowadays several numerical methods are widely used for either modeling or optimizing the performance of the manufacturing technologies. That has been advanced due to the large diffusion of the personal computer and the numerical algorithms. This paper represents an attempt to develop an appropriate machining strategy for obtaining most accurate dimension of the micro-groove and to prepare a database for assistance during laser micro-grooving operation. A feed-forward back-propagation neural network is developed to model machining process. The three most important process parameters-lower width, upper width and depth of the groove-have been considered as measures of process performance. The model is capable of predicting the response parameters as a function of five different control parameters i.e. lamp current, pulse frequency, pulse width of the duty cycle, assist air pressure and cutting speed.