| Abstract | Lasers are used for a variety of micro-machining applications because these tools provide a highly focused energy source that can be easily transmitted and manipulated to create geometric micro-features, often as small as the laser wavelength. Micro-machining with a laser beam is, however, a complex dynamic process with numerous non-linear and stochastic parameters (1-3). At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. Furthermore, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can greatly influence the machining process and the quality of the part geometry. This paper describes how a multi-layered neural network (4,6) can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a crater, or dent, with the desired depth and diameter. ... |
|---|
