Fluid Dynamics Effects on Microstructure Prediction in Single-Laser Tracks for Additive Manufacturing of IN625
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N RAGHAVAN, MATTHEW BEMENT, SRDJAN SIMUNOVIC, and JOHN A. TURNER are with the Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830. Contact e-mail: [email protected] LANG YUAN is with the Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29201. VIPUL K. GUPTA is with GE Research, Niskayuna, NY 12309. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan). Manuscript submitted September 22, 2019.
METALLURGICAL AND MATERIALS TRANSACTIONS B
INTRODUCTION
ADDITIVE manufacturing (AM) is a revolutionary manufacturing process with clear advantages such as lower energy usage, minimum scrap waste, lower buy-to-fly ratio and shorter lead time to market.[1] The laser powder-bed fusion additive manufacturing (LPBF) process is one of the most successful process to realize lightweight and cost-effective production of complex, high-performance end-use parts. The widespread and economical use of metal AM relies on the ability to predict and control microstructures and resulting mechanical properties.[2] To date, the cost and time associated with process development for LPBF of new alloys is high and deployed processes were not optimized. A large gap exists in the AM process development to link process parameters to microstructure and final part performance. This prevents the full exploration of the component design space to the realization of full benefits of AM components.
Process maps for laser-engineered net shaping (LENS) process were developed.[3–5] However, the extension to LPBF of those process maps is constrained by computational resources and consolidated solidification theory with insufficient validation data, due to much higher spatial and temporal resolution specific to LPBF. By varying particle size distribution (PSD) of powder, laser parameters (power, scan speed, beam diameter, and hatch spacing), metal alloys can experience large range of solidification conditions,[6] namely temperature gradient (G) and solidification velocity (V), from ~ 103 to 106 K/m and ~ 0.001 to 10 m/s, respectively. The resultant solidification microstructure will vary dramatically based on the process parameters.[7] Current approaches to predict thermal history in LPBF are solving mainly for the energy equation using analytical approaches, e.g., Rosenthal solution[6] or commercially available software solving the energy equation without the fluid flow.[8] Recently, open-s
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