Abstract
Laser machining can depend on the combination of many complex and nonlinear physical processes. Simulations of laser machining that are built from first-principles, such as the photon-atom interaction, are therefore challenging to scale-up to experimentally useful dimensions. Here, we demonstrate a simulation approach using a neural network, which requires zero knowledge of the underlying physical processes and instead uses experimental data directly to create the model of the experiment. The neural network modelling approach was shown to accurately predict the 3D surface profile of the laser machined surface after exposure to various spatial intensity profiles, and was used to discover trends inherent within the experimental data that would have otherwise been difficult to discover.
Highlights
The physical processes that govern laser machining of a surface are complex, including the light-matter interactions that transfer energy to the sample surface, but subsequent heat conduction, phase-changes of the sample material, dynamics of material in non-solid phases, re-deposition of material and so on [1–4]
This approach is achieved via the use of a neural network (NN) that is trained on experimental images of laser machined surfaces, and which subsequently encodes a description of the laser machining processes
The trained NN offers significant potential for predictive capabilities, as it can be used to produce the 3D surface profile that would likely result from laser machining using a wide variety of spatial intensity profiles, including, of particular interest, ones not used in the experiment that generated the training data
Summary
The physical processes that govern laser machining of a surface are complex, including the light-matter interactions that transfer energy to the sample surface, but subsequent heat conduction, phase-changes of the sample material, dynamics of material in non-solid phases, re-deposition of material and so on [1–4]. Rather than starting from fundamental physical processes and scaling up to simulate the effect of laser machining at experimentally useful size scales, the technique presented in this paper follows an empirical approach; starting from experimental data to model lasermachined surface profiles after exposure to various spatially shaped laser intensity patterns. Rather than constructing a model from first principles, this NN approach required a data set of experimental images of the material surface when machined with a random selection of spatial intensity profiles. While the CAN outputs, in the bottom two rows, show raised burr at the perimeter interface between regions exposed and not exposed to the shaped intensity profile at this point of training (albeit overly uniform), as occurs in real laser machining, the CNN output shows little to no burr
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