Abstract
A simplified analytical model of the laser powder bed fusion (LPBF) process was used to develop a novel density prediction approach that can be adapted for any given powder feedstock and LPBF system. First, calibration coupons were built using IN625, Ti64 and Fe powders and a specific LPBF system. These coupons were manufactured using the predetermined ranges of laser power, scanning speed, hatching space, and layer thickness, and their densities were measured using conventional material characterization techniques. Next, a simplified melt pool model was used to calculate the melt pool dimensions for the selected sets of printing parameters. Both sets of data were then combined to predict the density of printed parts. This approach was additionally validated using the literature data on AlSi10Mg and 316L alloys, thus demonstrating that it can reliably be used to optimize the laser powder bed metal fusion process.
Highlights
Interest in laser powder bed fusion (LPBF) additive manufacturing (AM) has spiked in many industries, creating a high demand for new AM-ready metallic materials [1]
We investigate the possibility of using a combination of a simplified analytical model of the melt pool and of an experimental calibration routine to create a density control algorithm for the
Note that even though this combined modeling-experiment approach was validated for only one specific LPBF system (EOS M 280), we hypothesize that it can be extended to any LPBF system, provided an adequate calibration experiment is carried out
Summary
Interest in laser powder bed fusion (LPBF) additive manufacturing (AM) has spiked in many industries, creating a high demand for new AM-ready metallic materials [1]. Once the specimens are printed, their mechanical properties are evaluated and a conclusion is drawn on the influence of the different processing parameters on the final part geometric and service attributes This approach yields satisfying results, but requires multiple printing jobs and time-consuming post-processing experiments. It could be realized for a single alloy, but becomes prohibitively expensive if multiple process optimization campaigns are required Another way a new AM material can be introduced is by applying a numerical modeling approach with the objective of finding the appropriate printing parameters, as shown in [11,12,13,14,15]. CTihtye, tehneetregmy pfreormatuthree dlaisterribisuatisosnumT(exd·yt·oz)bienatphpelpieodwodnerthbeepdoiws cdaelcrubleadtesdubrfyacEeqfuoartiaotnims (e1)i–n(t3e)r:val defined by the scanning speed and the laser spot size In this case, for a Gaussian beam moving with a given velocity, the temperature disTtr(ixb·uy·tzio)n=TT(x0·y+·zk) riAnf πPAthP32 e p∞0ow1 +d1eτr2beexdpi(sCc)adlcτulated 01 by Equations (1)–(3()1: ). Λ where λ is the laser wavelength (μm), σ0, the electrical conductivity (S/m), and ρ0, the electrical resistivity of the irradiated material (Ohm·m)
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