2024/08/06
Tailoring Laser Beam Powder Bed Fusion parameters for range of Ti6Al4V powders
An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.
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Tailoring Laser Beam Powder Bed Fusion parameters for range of Ti6Al4V powders
2024/08/06
Researchers from the University of Waterloo, Ontario, Canada, and Colibrium Additive, a GE Aerospace company, have published a study focused on optimising parameters for Laser Beam Powder Bed Fusion (PBF-LB) Additive Manufacturing via machine learning and melt pool monitoring data collected with photodiode sensors.