Process Monitoring of Internal Temperature Distribution of Powder Bed Fusion Parts
An ensemble kamlan filter (EnKF) state observer algorithm and process monitoring method for more quickly and accurately estimating the internal temperature gradient of Powder Bed Fusion parts for enhanced process monitoring and control.
Powder Bed Fusion (PBF) is a subset of additive manufacturing processes that performs a layer-by-layer fabrication of metal components by selectively melting metal powder disbursed over the earlier layer. PBF processes encompass methods such as selective laser sintering (SLS), direct metal laser sintering (DMLS), and electron beam melting (EBM). The market for PBF printers is expected to reach USD 1.85 billion by 2023 with a estimated compound annual growth rate (CAGR) of 24.1%.
Prof. David Hoelze and Graduate Research Associate Nathaniel Wood have developed a process-monitoring algorithm to estimate the internal temperatures of components manufactured through PBF in near-real time. The algorithm is a type of ensemble kamlan filter (EnKF), a subclass of state-observers that modifies a model of the internal temperature based on measurements of process inputs and outputs such as laser power and melt pool temperature. This approach forces the model's predictions to match the process realities thereby improving the model’s accuracy. The inventors have adopted the EnKF to the realities of PBF, modifying its internal structure to accommodate uncertainty inherent in the PBF process inputs.
1 BCC Research. (2018). Global Markets for 3D Printing. (Report Code IAS102C). Retrieved from https://www.bccresearch.com/market-research/instrumentation-and-sensors/global-markets-for-3d-printing.html