Predictive modeling and optimization of multi-track processing for laser powder bed fusion of nickel alloy 625
Luis Criales, Yigit Arisoy, Brandon Lane, Shawn P. Moylan, Alkan Donmez, Tugrul Ozel
This paper presents a physics-based modeling approach to predict temperature profile and melt pool geometry in multi-track processing of laser powder bed fusion (L-PBF) of nickel 625 alloy powder material. Multi-track laser processing of powder material using L-PBF process has been studied and two-dimensional (2-D) temperature profiles along the scanning and hatch directions for three consecutive tracks are computed for a moving laser heat source to understand the temperature rise due to heating. Based on the predicted temperature distributions, melt pool geometry, i.e., the locations at which melting of the powder material occurs, is determined. Designed experiments on L-PBF of nickel alloy 625 powder material are conducted to measure the relative density and melt pool geometry. Experimental works is reported on the measured density of built coupons and melt pool size. Statistically-based predictive models using response surface regression for relative density, melt pool geometry, peak temperature and time above melting temperature are developed and multi-objective optimization studies are conducted by using genetic algorithm and swarm intelligence.
, Arisoy, Y.
, Lane, B.
, Moylan, S.
, Donmez, A.
and Ozel, T.
Predictive modeling and optimization of multi-track processing for laser powder bed fusion of nickel alloy 625, Additive Manufacturing, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920922
(Accessed September 30, 2023)