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Focus variation measurement and prediction of surface texture parameters using machine learning in laser powder bed fusion

Published

Author(s)

Tugrul Ozel, Ayca Altay, Bilgin Kaftanoglu, Richard Leach, Alkan Donmez

Abstract

The powder bed fusion based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.
Citation
Journal of Manufacturing Science and Engineering-Transactions of the ASME
Volume
142

Keywords

Additive Manufacturing, Laser, Surface Analysis, Powder Bed Fusion

Citation

Ozel, T. , Altay, A. , Kaftanoglu, B. , Leach, R. and Donmez, A. (2020), Focus variation measurement and prediction of surface texture parameters using machine learning in laser powder bed fusion, Journal of Manufacturing Science and Engineering-Transactions of the ASME (Accessed April 18, 2024)
Created January 31, 2020, Updated January 19, 2022