N21B-T022 TITLE: Integrated Computational Materials Engineering (ICME) Modeling Tool for Optimum Gas Flow in Metal Additive Manufacturing Processes

RT&L FOCUS AREA(S): General Warfighting Requirements (GWR);Hypersonics;Space

TECHNOLOGY AREA(S): Air Platforms;Materials / Processes;Weapons

OBJECTIVE: Develop an Integrated Computational Materials Engineering (ICME) modeling tool to predict the effect of gas flow on metal additive manufacturing processes for improvement in the quality of the parts.

DESCRIPTION: Additive manufacturing (AM) processes, such as powder bed fusion (PBF) and directed energy deposition (DED), have the potential to revolutionize the manufacturing and repairing of complex metal components in aerospace, medical, and automotive industries. Current processes are not yet fully matured. There is a great need for the processes to produce parts that are free from defects, such as pores, lack of fusion, metal oxidation, and fusion of splattered condensate.

To prevent the parts from oxidizing, AM processes blow inert gases - such as argon and nitrogen - to shield the fusion zone from oxygen. In PBF processes, the shielding gas flow is directed over the build layer to remove metal condensate and spatter from the fusion zone and then is pulled out of the chamber through filters to remove the splattered particle. Improper shielding and removal of spatter particles lead to defects in a PBF process. For example, it has been shown that:

1. the condensed metal vapor particles could attenuate the laser beam up to 10%,
2. spatter falling back on the powder bed could locally increase the layer thickness, and
3. spatter falling onto the consolidated surface could fuse resulting in poor surface finish [Ref 1].

The direction of the flow relative to the laser scanning direction plays a significant role in the quality of the product [Ref 2]. Similarly, the DED processes are also strongly dependent on the flow rates of carrying and shielding gases. Higher flow rates could result in higher cooling rates and reduced heat-affected zone, but could also cause discontinuities and gaps in the deposition. Microhardness could vary with the changes in flow rates [Ref 3]. Current literature surveys show limitations in the modeling efforts. Adam Philo et al. (2017) have developed a computational model of gas-flow effects in the inlet design for the Renishaw AM250 to predict spatter particulate accumulation [Refs 4]. Florian Wirth et al. (2017) have shown the interaction of powder jet and laser beam in a powder-blown machine and cases for laser beam attenuation [Ref 5]. Praveen BidareI et al. (2017) use Schlieren imaging and multiphysics modeling to investigate the inert atmosphere and laser plume in PBF [Ref 6]. References 7 through 14 provide additional experimental and computational efforts. However, a comprehensive modeling tool for gas flow interacting with all major AM process parameters is not available for designing and developing better AM processes.

An ICME framework is needed to represent the process-structure-property-performance relationship in metallic AM. The tool sought in this STTR topic will be part of the framework. It should integrate critical fundamental physics, such as mass, fluid and heat transport, phase transition, surface tension, Marangoni stress, recoil pressure, and melt pool fluid dynamics, into one comprehensive framework. With manufacturing parameters and material properties as the inputs, the framework should quantify the effect of gas flow on melt pool dimension, surface morphology, temperature profile, solidification rate, powder spattering, and pore formation/propagation. The framework should provide mitigation strategies for the gas-induced powder spattering and pore formation, which degrade the property of the fabricated metallic part.

Overall, the model should enable optimizing the gas flow including improvement in nozzle designs; gas circulation to match the design of the AM machine offering optimum shielding of the fusion area and the melt pool; and the efficient removal of the gas and debris from the chamber. The model should provide ways to set print parameters for optimum part performance for the raw material used and the scan patterns for the part.

PHASE I: Demonstrate the feasibility of a multiphysics model gas flow interaction with metal fusion in the PBF or DED additive manufacturing process. Show that the model works efficiently within the ICME framework to enable proper design and control of gas flow for producing defect-free AM products. Carry out experiments for the chosen AM process to validate the simulated results. Evaluate the model based on the AM products, such as surface finish, defects (size, density, and distribution), and/or microhardness. Demonstrate the potential for this prototype to address factors additional to the subset chosen above for a fully developed modeling system in the ICME framework in Phase II.

PHASE II: Based on the prototype modeling tool developed in Phase I, fully develop and validate the predictive modeling tool to fine-tune the gas flow and the associated process parameters to improve AM part quality, such as fewer defects, better surface finish, and desirable microhardness. Demonstrate its capability of additive manufacturing of aircraft components with complex geometry and tailored performance.

PHASE III DUAL USE APPLICATIONS: Mature the modeling tool further by extending the capability for common airframe metal alloys, such as aluminum, steel, and titanium. Demonstrate the capability to optimize the AM process for multiple metals. Validate the tool in final testing of the capability by printing parts of more than one metal alloy and carrying out component tests demonstrating strength and durability.

AM in the commercial sector is progressing with individual companies developing limited capabilities using ICME tools. The commercial sector broadly treats material qualification and part certification for AM as separate processes, one followed by the other. ICME tools integrate them to have a seamless process. Hence, this tool will open the possibilities for the commercial sector to take advantage of developing quality products for their customers.

REFERENCES:

1. Schniedenharn, M., Wiedemann, F. and Schleifenbaum, J. H. "Visualization of the shielding gas flow in SLM machines by space-resolved thermal anemometry." Rapid Prototyping Journal, 24(8), November 12, 2018, pp. 1296-1304. https://doi.org/10.1108/RPJ-07-2017-0149
2. Anwar, A. B. and Pham, Q. C. "Effect of inert gas flow velocity and unidirectional scanning on the formation and accumulation of spattered powder during selective laser melting" [Paper presentation]. Proceedings of the 2nd International Conference on Progress in Additive Manufacturing (Pro-AM 2016), Singapore, May 16-19, 2016. https://hdl.handle.net/10220/41780
3. Koruba, P., Wall, K. and Reiner, J. "Influence of processing gases in laser cladding based on simulation analysis and experimental tests." 10th CIRP Conference on Photonic Technologies [LANE 2018], 74, pp. 719-723. https://doi.org/10.1016/j.procir.2018.08.025
4. Philo, A. M., Sutcliffe, C. J., Sillars, S. A., Sienz, J., Brown, S. G. R. and Lavery, N. P. "A study into the effects of gas flow inlet design of the Renishaw AM250 laser powder bed fusion machine using computational modelling." [Paper presentation]. Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International, Austin, TX, United States. https://pdfs.semanticscholar.org/c3d8/2fe33631879d919bc37729f0895d5004dd9c.pdf?ga=2.227959966.248001110.1594670984-1201775627.1589487702
5. Wirth, F., Freihse, S., Eisenbarth, D. and Wegener, K. "Interaction of powder jet and laser beam in blown powder laser deposition processes: Measurement and simulation methods." [Paper presentation]. Proceedings of Lasers in Manufacturing Conference 2017, Munich, Germany, June 26-29, 2017. http://hdl.handle.net/20.500.11850/211852
6. Bidare, P., Bitharas, I., Ward, R. M., Attallah, M. M. and Moore, A. J. "Fluid and particle dynamics in laser powder bed fusion." Acta Materialia, 142, January 1, 2018, pp. 107-120. https://doi.org/10.1016/j.actamat.2017.09.051
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8. Guo, Q., Zhao, C., Qu, M., Xiong, L., Hojjatzadeh, S. M. H., Escano, L. I., Parab, N. D., Fezzaa, K., Sun, T. and Chen, L. "In-situ full-field mapping of melt flow dynamics in laser metal additive manufacturing." Additive Manufacturing, 31, 2020, pp. 100939. https://doi.org/10.1016/j.addma.2019.100939
9. Yan, J., Lin, S., Bazilevs, Y. and Wagner, G. J. "Isogeometric analysis of multi-phase flows with surface tension and with application to dynamics of rising bubbles." Computers & Fluids, 179, 2019, pp. 777-789. https://doi.org/10.1016/j.compfluid.2018.04.017
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11. Lin, S., Yan, J., Kats, D. and Wagner, G. J. "A volume-conserving balanced-force level set method on unstructured meshes using a control volume finite element formulation." Journal of Computational Physics, 380, March 1, 2019, pp. 119-142. https://doi.org/10.1016/j.jcp.2018.11.032
12. Zhu, Q., Xu, F., Xu, S., Hsu, M.-C. and Yan, J. "An immersogeometric formulation for free-surface flows with application to marine engineering problems." Computer Methods in Applied Mechanics and Engineering, 361, April 1, 2020, p. 112748. Https://doi.org/10.1016/j.cma.2019.112748
13. Lin, S., Gan, Z., Yan J. and Wagner, G. J. "A conservative level set method on unstructured meshes for modeling multiphase thermo-fluid flow in additive manufacturing processes." Computer Methods in Applied Mechanics and Engineering (in review). https://www.sciencedirect.com/science/article/abs/pii/S0045782520305338
14. Yan, W., Lin, S., Kafka, O. L., Lian, Y., Yu, C., Liu, Z., Yan, J., Wolff, S., Wu, H., Ndip-Agbor, E., Mozaffar, M., Ehmann, K., Cao, J., Wagner, G. J. and Liu W. K. "Data-driven multi-scale multi-physics models to derive process�structure�property relationships for additive manufacturing." Computational Mechanics, 61(5), January 12, 2018, pp. 521- 541. https://doi.org/10.1007/s00466-018-1539-z

KEYWORDS: Additive Manufacturing; AM; Laser Powder Bed Fusion; Directed Energy Deposition; Inert Gas Shield; multiphysics model gas flow; powder splatter