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

Published

January 31, 2020

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

Pub Type

Journals

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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, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927173 (Accessed October 18, 2025)