Predictive modeling and optimization of multi-track processing for laser powder bed fusion of nickel alloy 625

This paper presents an integrated physics-based and statistical modeling approach to predict temperature field and meltpool geometry in multi-track processing of laser powder bed fusion (L-PBF) of nickel 625 alloy. Multi-track laser processing of powder material using L-PBF process has been studied using 2-D finite element simulations to calculate temperature fields along the scan and hatch directions for three consecutive tracks for a moving laser heat source to understand the heating and melting process. Based on the predicted temperature fields, width, depth and shape of the meltpool is determined. Designed experiments on L-PBF of nickel alloy 625 powder material are conducted to measure the relative density and meltpool geometry. Experimental work is reported on the measured density of built coupons and meltpool size. Statistically-based predictive models using response surface regression for relative density, meltpool geometry, peak temperature, and time above melting point are developed and multi-objective optimization studies are conducted by using genetic algorithm and swarm intelligence.