Neural network modeling of the laser material-removal process

Industrial lasers are used extensively in modem manufacturing for a variety of applications because these tools provide a highly focused energy source that can be easily transmitted and manipulated for micro-machining. The quantity of material removed and the roughness of the finished surface are a function of the crater geometry formed by a laser pulse with specific energy (power). Laser micro-machining is, however, a complex nonlinear process with numerous stochastic parameters related to the laser apparatus and the material specimen. Consequently, the operator must manually set the process control parameters by "trial-and-error". This paper describes how an artificial neural network can be used to create a nonlinear model of the laser material-removal process in order to automate micro-machining tasks. The multilayered neural network predicts the pulse energy needed to create a crater of specific depth and average diameter. Laser pulses of different energy levels are impinged on the surface of the test material in order to investigate the effect of pulse energy on the resulting crater geometry and volume of material removed. Experimentally acquired data from several sample materials are used to train and test the network's performance. The key system inputs for the modeler are mean depth of crater and mean diameter of crater, and the system outputs are pulse energy, variance of depth and variance of diameter. The preliminary study using the experimentally acquired data demonstrates that the proposed network can simulate the behavior of the physical process to a high degree of accuracy. Future work involves investigating the effect of different input parameters on the output behavior of the process in hopes that the process performance, and the final product quality, can be improved.