Image-based monitoring of femtosecond laser machining via a neural network

Abstract

Femtosecond laser machining offers the potential for high-precision materials processing. However, due to the nonlinear processes inherent when using femtosecond pulses, experimental random noise can result in large variations in the machined quality, and hence methods for closed loop feedback are of interest. Here we demonstrate the application of a neural network (NN), acting as a pattern recognition algorithm, for visual monitoring of the target substrate via a camera that observes the sample during machining. This approach has the advantage that it requires zero knowledge of the underlying physical processes, and hence avoids the need for modelling the complex photon–atom interactions that occur with femtosecond laser machining. The NN was shown to accurately determine the type of material, the laser fluence and the number of pulses, directly from a single image of the sample and within ten milliseconds. This approach provides the potential for real-time feedback for femtosecond laser materials processing.