Predictive visualization of fiber laser cutting topography via deep learning with image inpainting

Laser cutting is a fast, precise, and noncontact processing technique widely applied throughout industry. However, parameter specific defects can be formed while cutting, negatively impacting the cut quality. While light-matter interactions are highly nonlinear are, therefore, challenging to model analytically, deep learning offers capability of modeling these directly from data. Here, we show that used scale up visual predictions for produced in as well predicting parameters not measured experimentally. Furthermore, relationship between laser parameters.