Additive Manufacturing

Printability maps allow users to plot the likelihood of possible defects that occur during additive manufacturing – keyholing, lack of fusion, and balling. These defects occur based on the speed and power used during the AM process, so printability maps allow you to reduce the risks of these defects by showing the speed and power settings that are optimal (the white area in the image), allowing you to calibrate your system to avoid these issues.

The AM Module in Thermo-Calc makes it possible for users to create printability maps. Printability maps show that a conduction mode melt pool is needed for a fully dense melt pool and to avoid keyholing defects, common in keyhole type melt pools. The plot here shows such a map for a 316L stainless steel performed using a calibrated gaussian heat source. All the experimental variations of power (40-100 W) and scan speed (400-3000 mm/s) are plotted to generate printability maps. The printability map is then overlaid with experimental data from Hu et al., 2019. ‘K’ denotes keyhole mode and ‘C’ denotes conduction mode for a melt pool. 

You can read more about printability maps in Thermo-Calc and watch a video about how we set them up and interpret the results in our blog post, Avoid Defects in the Powder Bed Fusion Process with Printability Maps.

The accuracy of finite element (FE) simulations depends in part on the material property data used by the code. For example, to predict the size, shape, and temperature of the laser melt pool, FE codes need the latent heat and heat capacity of the material. Handbooks are a common source of data, but these typically do not have temperature-dependent data for industrial alloys or data for novel materials. Temperature-dependent data can be calculated using Thermo-Calc for specific chemical compositions of interest.

These figures show a comparison of handbook values and CALPHAD calculated values of Cp (top) and ΔH (bottom) for a 316L Stainless Steel (recalculated from Smith et al. Comput. Mech. 2016).

Most laser-based additive manufacturing processes use a red laser (wavelength : 1.06 µm) where the absorptivity of some metals, such as Al and Cu, is quite high (Kirihara, 2021). Al- alloys also have low viscosity as compared to Fe- or Ni-based alloys. The low viscosity in combination with high fluid flow rates can result in turbulent flows. Additionally, some Al-alloys, such as Al-Si based alloys, solidify with a eutectic microstructure (Thijs et al.). This eutectic microstructure becomes finer or coarser depending on solidification conditions such as cooling rate of solidification front. Due to rapid solidification conditions in additive manufacturing, solute trapping occurs, which leads to increased solubility of Si in FCC-Al (~3 at% instead of 1.5 at% in classic Scheil). 

The Solute Trapping Model in our Scheil Calculator can account for increased solute trapping in the primary phase for high solidification speeds, often seen during the AM process. Thermo-Calc also allows users to account for phase-interface scattering (to account for microsegregations, eutectic solidification) before setting up calculations in the AM Module. 

The first plot here shows the increased solubility of Si in FCC-Al due to rapid solidification conditions, which is captured by the Scheil Solute Trapping Model in Thermo-Calc. The second is a parity plot based on Bogdonova et al., 2024, wherein we represent the experimental to calculated melt pool dimensions calculated for an AlSi10Mg alloy printed by powder bed fusion-laser beam (PBF-LB) process.

When metallic alloys solidify, the resulting primary grain microstructure can be columnar or equiaxed depending on the solidification conditions imposed. These conditions can be characterised by parameters such as the thermal gradient (G) and solidification growth rate (v) of the solidifying melt. The Columnar to Equiaxed Transition (CET) Property Model, which is available as a General Property Model in Thermo-Calc, uses these conditions to predict the fraction of equiaxed grains that correlates with a certain solidification condition.

Users can plot the thermal gradient and solidification growth rate as a scatter plot directly from the AM Module. These results can then be overlaid over the results from the CET General Property Model to identify if the melt pool will solidify with columnar grains or equiaxed grains. The plot here shows an example for an IN718 alloy using AM Module plotting solidification rate and thermal gradient in a log-log plot. This plot is then overlaid on a CET Model based on a similarly produced IN718 alloy (Polonsky, 2020) to demarcate the position for amount of equiaxed fraction of grains. The AM calculation shows that most of the melt pool (the points below the purple line) solidifies in a fully columnar solidification microstructure, which is typical for such materials.