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Journal Articles

A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process

M. Shafiqur Rahman, Naw Safrin Sattar, Radif Uddin Ahmed, Jonathan Ciaccio, Uttam K. Chakravarty
Journal: Journal of Engineering Materials and Technology
Publisher: ASME
Article Type: Research Papers
J. Eng. Mater. Technol. October 2024, 146(4): 041006.
Paper No: MATS-24-1050
https://doi.org/10.1115/1.4065687
Published Online: August 6, 2024
... Trees regressor gives the lowest number of errors and the highest amount of correlation coefficient for melt-pool width. Extra Trees regressor also shows promising results for predicting melt-pool depth, but the Gradient Boosting outperformed it with a higher correlation coefficient and lower RMSE...
View Article titled, A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process
Open the PDF for A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process in another window
Topics: Errors, Geometry, Lasers, Machine learning, Optimization, Porosity, Modeling, Sensitivity analysis
Includes: Supplementary data