Why is vaporization-induced composition change important during additive manufacturing?

During the Laser Powder Bed Fusion , volatile alloying elements can selectively vaporize from the molten pool, leading to composition change of the final part compared to the original feedstock. Loss of alloying elements impact the microstructure, corrosion resistance, and mechanical properties.
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Why is vaporization-induced composition change important during additive manufacturing?

During the Laser Powder Bed Fusion (LPBF) process, volatile alloying elements can selectively vaporize from the molten pool, leading to variations in the chemical composition of the final part compared to the original feedstock.1-2 The rate at which these elements vaporize depends on their vapor pressure, which is influenced by local temperature and alloy composition. This loss of alloying elements can significantly impact the microstructure, corrosion resistance, and mechanical properties of the manufactured parts. Therefore, an accurate estimation of the composition change is important for manufacturing high-quality parts. Composition changes in nickel-based superalloys during LPBF have been studied using both experimental and computational methods. For example, significant depletion of niobium and chromium was observed during the LPBF of Inconel 718. Similarly, the loss of Ni was found to change the phase transition temperatures in NiTi shape memory alloys. Evaporative losses have been minimized experimentally by adjusting processing parameters. However, the trial-and-error method can be time-consuming and expensive because of the need to adjust several variables. Here a mathematical model is developed and used to simulate the vaporization of alloying elements and the resulting composition change during LPBF of nickel alloys.

A Multi-Physics Framework for Predicting Composition Changes

Many of these models draw from fusion welding literature and utilize established frameworks of element vaporization during welding. For example, Wang et al.3 used a vaporization model for LPBF that incorporates gas-flow and material compositions, while Mukherjee et al.4 applied Knight’s model5 to compute composition changes. Despite advancements in understanding vaporization dynamics during LPBF, existing models often fail to account for spatial variations in evaporative flux based on temperature distributions. Moreover, while high-fidelity simulations have explored detailed physical phenomena, there remains a need for computationally efficient models that can accurately predict composition changes during remelting processes during multilayer deposition processes. To address these gaps, a new multi-physics modeling framework has been developed that integrates heat and fluid flow calculations6 with thermodynamic and evaporation models. This framework allows for detailed estimation of composition changes during LPBF of nickel-based superalloys by considering spatial variations in evaporative flux and elemental activities. The framework was rigorously validated through experiments under varying processing conditions. 

Methodology

The methodology for estimating composition change in the LPBF process involves several steps. First, a heat transfer and fluid flow model calculates the temperature and pool dimensions during the laser powder bed fusion of nickel alloys. This model is detailed in a previous paper. Figure 1 shows the temperature field on the top surface during LPBF of Inconel 939.  Key assumptions include temperature-independent densities for solid and liquid alloys and treating the laser energy as a volumetric source. The model computes 3D temperature and velocity fields and uses a finite difference scheme for calculations.

The second step uses the computed temperature data to estimate the vapor pressures7 of alloying elements, applying a modified Langmuir equation to calculate the composition change due to selective evaporation. Spatial distributions of vapor pressure, partial vapor pressure over alloy, and evaporative flux of Cr, the most susceptible element to composition change, are also shown in Figure 1.  The evaporative flux is derived from the calculated partial vapor pressures and integrated over the molten pool's surface area.

Validation of the methodology involved fabricating parts from four nickel-based superalloys, measuring their compositions, and comparing results to the model predictions. This approach enables accurate estimation of composition changes during the LPBF process for multiple layers.

Results

Key findings indicate that evaporative flux is primarily controlled by temperature fields during LPBF. The modeling results demonstrated that higher temperatures lead to increased vapor pressures of elements like chromium, making it particularly susceptible to evaporation loss. The study also revealed that different nickel alloys exhibit varying susceptibilities to composition change based on their thermophysical properties. The effects of remelting cycles were also significant; areas undergoing multiple remelting showed pronounced composition changes due to non-uniform thermal profiles. Experimental data corroborated these findings, showing that locations subjected to more remelting cycles tended to have reduced amounts of volatile elements like chromium. Additionally, process parameters such as laser power and scanning speed were found to influence composition changes significantly. Higher laser powers and slower scanning speeds resulted in increased peak temperatures and greater evaporative losses.  Calculated changes in the composition of six main constituting elements of different nickel alloys were compared with the corresponding experimental data during LPBF as shown in Figure 2. The experiment for Inconel 939, CM247LC, and ABD-850AM was done using 200 W laser power, 1.8 m/s scanning speed, and 30 µm layer thickness.  Not all nickel alloys are equally susceptible to composition change because of the differences in their thermophysical properties and chemical compositions. Figure 3 compares the relative susceptibilities of ten commonly used nickel alloys to composition change. The calculations are done at a 200 W laser power, a scanning speed of 1.8 m/s, and a layer thickness of 30 µm.

A collage of graphs and diagrams

Description automatically generated

Figure 1: Calculation of evaporative flux during LPBF. The results are for LPBF of Inconel 939 using 200 W laser power, 1.8 m/s scanning speed, and 30 µm layer thickness as an example. A heat transfer and fluid flow model is used to estimate the temperature field. The top surface temperature field and the vapor pressure vs. temperature data are used to compute the spatial distribution of the vapor pressure of pure elements. A thermodynamic model is used to calculate the activities of all alloying elements that are used to predict the partial vapor pressure of elements over the liquid alloy. The partial vapor pressures of elements are then used in the Langmuir equation to calculate the evaporative flux of elements. The results are shown for Cr as an example.

Figure 2: Comparison between the calculated and experimental composition change during LPBF of (a) Inconel 939, (b) Inconel 718, (c) CM247LC, (d) ABD-850AM. Experiments for Inconel 939, CM247LC, and ABD-850AM were done using 200 W laser power, 1.8 m/s scanning speed, and 30 µm layer thickness. The experimental results for Inconel 718 were taken from the literature [8] and the corresponding calculations were done for 300 W laser power and 200 mm/s scanning speed.

 

Main contributions

In summary, this research highlights the importance of understanding how selective vaporization affects composition changes during LPBF of nickel-based superalloys. The newly developed modeling framework provides valuable insights into optimizing processing conditions by minimizing evaporative losses through careful control of temperature fields and remelting cycles. Ultimately, this work aims to enhance the quality and performance of components produced via additive manufacturing techniques like LPBF by ensuring desired chemical compositions are maintained throughout the process.

Figure 3: Absolute values of composition change for the most susceptible elements during LPBF of various nickel alloys using 200 W laser power, 1.8 m/s scanning speed, and 30 µm layer thickness.

 Cite: T. Mukherjee, J. Shinjo, T. DebRoy, C. Panwisawas. Integrated modelling to control vaporisation-induced composition change during additive manufacturing of nickel-base superalloys. npj Computational Materials, (2024). DOI: 10.1038/s41524-024-01418-z

References

[1] Mukherjee, T., & DebRoy, T. Theory and Practice of Additive Manufacturing. John Wiley & Sons (2023).

[2] DebRoy, T., Mukherjee, T., Wei, H.L., Elmer, J.W. & Milewski, J.O. Metallurgy, mechanistic models and machine learning in metal printing. Nature Reviews Materials, 6(1), 48-68 (2021).

[3] Wang, L., Zhang, Y. & Yan, W. Evaporation model for keyhole dynamics during additive manufacturing of metal. Physical Review Applied, 14(6), 064039 (2020).

[4] Mukherjee, T. & DebRoy, T. Printability of 316 stainless steel. Science and Technology of Welding and Joining, 24(5), 412-419 (2019).

[5] Knight CJ. Theoretical modeling of rapid surface vaporization with back pressure. AIAA J, 17, 519–523 (1979).

[6] Mukherjee, T., Wei, H. L., De, A. & DebRoy, T. Heat and fluid flow in additive manufacturing—Part I: Modeling of powder bed fusion. Computational Materials Science, 150, 304-313 (2018).

[7] Mondal, B., Mukherjee, T., Finch, N. W., Saha, A., Gao, M. Z., Palmer, T. A. & DebRoy, T. Vapor Pressure versus Temperature Relations of Common Elements. Materials, 16(1), 50 (2022).

[8] Ahsan, F. and Ladani, L., 2020. Temperature profile, bead geometry, and elemental evaporation in laser powder bed fusion additive manufacturing process. JOM, 72(1), pp.429-439.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Computational Materials Science
Physical Sciences > Materials Science > Computational Materials Science
Materials Engineering
Technology and Engineering > Mechanical Engineering > Materials Engineering
Metals and Alloys
Physical Sciences > Materials Science > Structural Materials > Metals and Alloys
Applied Statistics
Mathematics and Computing > Statistics > Applied Statistics