One limiting factor towards the wider adoption of laser powder bed fusion (LPBF) process, in regard to complex geometries, is the time required to optimize the machine parameters. Currently, most commercial LPBF printers include an in-situ photodiode sensor to monitor the melt-pool, which can be leveraged for this optimization in a timely manner. In this talk, we will present a 1D-CNN framework which leverages photodiode data to optimize process parameters. Since this framework leverages experimental data, we will comment on training prints & their limitations, experimental validation of the model on new prints, feature engineering to capture 2d & 3D geometric constraints in a 1D model, and how it relates to improving dimensional accuracy for features such as overhangs, thin walls, down-skins, etc.
Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344.