Influence of scan strategy and process parameters on microstructure and its optimization in additively manufactured nickel alloy 625 via laser powder bed fusion

Laser powder bed fusion (L-PBF) as an additive manufacturing process produces nearly fully dense nickel alloy 625 (IN625) parts with complex features. L-PBF generates surfaces and microstructure through directional solidification that can be controlled by scan strategies and selection of process parameters. This study provides experimental investigations on microstructure formation including sizes of cellular grains and growth directions of columnar grains on the nickel alloy 625 test coupons. The effects of process parameters including laser power, scan velocity, hatch distance, and scan strategy that produce various solidification cooling rates and thermal gradients during the process, which also contribute to resultant microstructure, have been analyzed. Optimization studies are conducted on several objectives to improve the productivity while controlling the process effects on the resultant microstructure using response surface regression, desirability functions, and multi-objective genetic algorithm optimization.

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.