A novel layer-based optimization method for stabilizing the early printing stage of LPBF process

Abstract

ABSTRACTThe laser power bed fusion (LPBF) forming process introduces heat accumulation and variations in powder layer thickness, which can destabilize the melt track and reduce surface quality. This phenomenon is especially more serious in the early printing stage. To tackle this stability problem, we proposed a novel approach optimizing process parameters on a layer-specific basis. At first, a numerical database was constructed through a set of numerical simulations. Then, a neural network prediction model was trained based on the database. Finally, this prediction model was embedded into a genetic algorithm for layer-based prediction. To verify the prediction results on processing parameters and calibrate the prediction model, physical experiments were prepared. The developed model consistently exhibited relative errors mostly within 6%. It is noteworthy that the relative errors between the numerical simulation results and the expected values were only 0.77% in width and 1.11% in depth. Printing optimization test was applied for an LPBF machine with a Invar alloy powder. The proposed method yielded positive results in both numerical simulations and the printing test. It can be further adopted for new material printing parameter optimization due to an efficient printing stability in the early-stage for LPBF process.

Keywords

• Heat accumulation
• neural network
• genetic algorithm
• process optimization
• laser power bed fusion (LPBF)