Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring
Farima Liravi,
Sebastian Soo,
Sahar Toorandaz,
Katayoon Taherkhani,
Mahdi Habibnejad-Korayem,
Ehsan Toyserkani
Affiliations
Farima Liravi
Multi-Scale Additive Manufacturing (MSAM) Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Sebastian Soo
Multi-Scale Additive Manufacturing (MSAM) Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Sahar Toorandaz
Multi-Scale Additive Manufacturing (MSAM) Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Katayoon Taherkhani
Multi-Scale Additive Manufacturing (MSAM) Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Mahdi Habibnejad-Korayem
AP&C, Colibrium Additive, A GE Aerospace Company, Montreal, QC J7H 1R8, Canada
Ehsan Toyserkani
Multi-Scale Additive Manufacturing (MSAM) Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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.
