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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
...) process with Ti-6Al-4V alloy. Unlike many ML models, the presented method incorporates five key features, including three process parameters (laser power, scanning speed, and spot size) and two material parameters (layer thickness and powder porosity). The target variables are the melt-pool width...
View Article titled, A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process
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Topics: Errors, Geometry, Lasers, Machine learning, Optimization, Porosity, Modeling, Sensitivity analysis
Includes: Supplementary data