Neural networks for predictive laser machining capabilities (Conference Presentation)

Abstract

Predictive visualisation for laser-processing of materials can be challenging, as the nonlinear interaction of light and matter is complicated to model, particularly when scaling up from atom-level to bulk material. Here, we demonstrate a predictive visualisation approach that uses a pair of neural networks (NNs) that are trained using data obtained from laser machining using a digital micromirror device (DMD) acting as an intensity spatial light modulator. The DMD enables laser machining using many beam shapes, and hence can be used to produce significant amounts of training data for NNs. Here, the training data corresponds to hundreds of DMD patterns (i.e. beam shapes) and their associated images and 3D depth profiles. The trained NNs are able to generate a surface image and 3D depth profile, showing what the ablated surface would look like, for a wide range of ablating beam shapes. The predicted visualisations are remarkably effective and almost indistinguishable from real experimental data in appearance. Such a NN approach has considerable advantages over modelling techniques that start from first-principles (i.e. light-atom interaction), since zero understanding of the underlying physical processes is needed, as instead the NN learns directly via observation of labelled experimental data. We will show that the NN learns key optical properties such as diffraction, the nonlinear interaction of light and matter, and the statistical distribution of debris and burring of material, all with zero human assistance. This offers a new paradigm in predictive capabilities, which could be applied to almost any manufacturing process.