Additive Manufacturing: Building the Pathway towards Process and Material Qualification: Process Qualification Part II
Sponsored by: TMS Extraction and Processing Division, TMS Materials Processing and Manufacturing Division, TMS Structural Materials Division, TMS: Mechanical Behavior of Materials Committee, TMS: Powder Materials Committee, TMS: Process Technology and Modeling Committee, TMS: Additive Manufacturing Bridge Committee
Program Organizers: John Carpenter, Los Alamos National Laboratory; David Bourell, University of Texas - Austin; Allison Beese, Pennsylvania State University; James Sears, GE Global Research Center; Reginald Hamilton, Pennsylvania State University; Rajiv Mishra, University of North Texas; Edward Herderick, GE Corporate
Wednesday 8:30 AM
March 1, 2017
Location: San Diego Convention Ctr
Session Chair: Richard Otis, Penn State; Jonathan Madison, Sandia National Laboratory
8:30 AM Invited
Identification of Defect Signatures in an Additively Manufactured Precipitation Hardened Stainless Steel: Jonathan Madison1; Laura Swiler1; Olivia Underwood1; Brad Boyce1; Bradley Jared1; Jeff Rodelas1; Brad Salzbrenner1; 1Sandia National Laboratories
Utilizing micro-computed tomography with a resolution on the order of 10 micron per voxel edge, a large ensemble set of 100+ additively manufactured tensile bars of a precipitation hardened 17-4 stainless steel are examined prior to high-throughput mechanical testing. Defect populations are quantified both globally and locally where porosity and lack of fusion are the primary features of interest. Distributions of defect populations, size and spatial arrangements are then evaluated and reported in terms of their statistical presence across the entire build as well as in the vicinity of failure locations. Correlations in observed defect presence will be shown and their potential relation to mechanical response will also be highlighted. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000
ALE3D's High-Order Fully-Implicit All-Speed Navier-Stokes Solver for Additive Manufacturing Applications: Brian Weston1; Jean-Pierre Delplanque1; Robert Nourgaliev2; Andy Anderson2; 1University of California, Davis; 2Lawrence Livermore National Laboratory
We present a new high-order fully-implicit all-speed fluid dynamics solver in LLNL’s ALE3D code for simulating compressible multi-material flows with phase change. The work is motivated by laser-induced phase change applications, particularly the selective laser melting (SLM) process in additive manufacturing (AM). Simulations of the SLM process require precise tracking of multi-material solid-liquid-gas interfaces, due to laser-induced phase change of metal powder. These rapid density variations and phase change processes tightly couple the governing equations, requiring a fully-compressible framework. Physics-based simulations of the laser melt dynamics provide a parameter optimization capability, critical for the certification of AM produced parts. In this study, we demonstrate the solver's performance for equilibrium phase change on benchmark and AM-relevant test problems. Results are shown to be very accurate for highly-stiff multi-fluid dynamics, essential for capturing powder melting at high-resolution. Future model enhancements will incorporate material evaporation and rapid-solidification associated with the SLM process. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Optimization Framework for Designing of Scanning Strategies for Microstructure Control in Additive Manufacturing Using Numerical Modeling Aided by High Performance Computing: Narendran Raghavan1; Suresh Babu1; Damien Lebrun-Grandie2; Srdjan Simunovic2; Michael Kirka2; John Turner2; Neil Carlson3; Ryan Dehoff2; 1University of Tennessee Knoxville; 2Oak Ridge National Laboratory; 3Los Alamos National Laboratory
Additive Manufacturing has the potential to revolutionize the manufacturing industries in near future. Solidification texture of additively manufactured components plays significant role in determining the mechanical performance. Solidification texture evolution is the result of spatio-temporal variation of temperature-gradient (G) and velocity (R) at the liquid-solid interface during melt-pool solidification. Scanning strategy has significant influence on G and R. Experimental measurement of these solidification parameters are impossible. In this study, numerical-thermal model, coupled with optimization framework is used to understand the sensitivity of different input parameters and scan strategies on texture evolution. Probability of equiaxed grain formation is the quantity of interest. Smolyak sparse grid algorithm is used to reduce the number of simulations by effectively sparsing the parameter space. Response surface is created and analyzed for different scan strategies. In addition to the reduction of experimental cost, coupling numerical models with effective sparse grid algorithms significantly reduces the computational cost.
Residual Stress Control in Additive Manufacturing through Integration of Physics-based and Data-driven Modeling: Jingran Li1; Ran Jin1; Hang Yu1; 1Virginia Tech
Additive manufacturing of materials always involves non-equilibrium processes that lead to complex co-evolution of the thermal field, microstructure, and most importantly, residual stresses, which give rise to internal defects and significant part distortion. However, a robust strategy for controlling the level and distribution of residual stresses remains elusive. Here, we present a theoretical and experimental framework for residual stress control, in which kinetics, mechanics, and data-driven models are integrated, and are validated by in situ thermal imaging and ex situ dimension measurement. 3D finite element modeling of the thermo-mechanical process captures the underlying physics; Kriging meta-modeling of the process-property relationship accelerates computation speed; Bayesian calibration identifies model discrepancies and enables predication with different geometric designs and processing parameters. This framework enables residual stress control on the component scale with low computation cost, which is demonstrated in processes such as fused deposition modeling and directed energy deposition.
10:00 AM Break
10:20 AM Invited
Surface Topography and the Relationship to Surface and Near-surface Structures in Laser Powder Bed Fusion Additive Manufacturing: Jason Fox1; Mark Stoudt1; Thien Phan1; Zach Reese1; Shawn Moylan1; Brandon Lane1; Lyle Levine1; 1National Institute of Standards and Technology
The development of additive manufacturing has allowed for increased flexibility and complexity of designs over formative and subtractive manufacturing. However, increased design complexity leads to difficulty in maintaining consistent or desired as-built surface texture and microstructure. To better understand the relationship between the as-built surface texture, surface structures that are indicative of defects in the build process, and the near-surface structures, such as microstructure and porosity, samples covering a range of overhang angles and process parameters were built in a laser powder bed fusion system. The surface texture was characterized and samples were sectioned for microstructural analysis. Analysis of the profile and areal surface data has shown relationships to the specific features that make up the surface texture and, ultimately, the mechanisms that created them. Through these relationships, measured surface texture has the potential as a process signature to help determine the cause of various defects.
10:50 AM Invited
Toward a New Generation of Thermodynamic Models for Alloy Additive Manufacturing: Richard Otis1; Lourdes Bobbio1; Allison Beese1; Zi-Kui Liu1; 1Pennsylvania State University
Thermodynamic modeling is now routinely used to predict and understand the observed properties of alloy samples produced by additive manufacturing. In this work the properties of compositionally graded Ti-6Al-4V-to-Invar and 304L stainless steel-to-Inconel 625 alloys are studied by modeling and experiment. While the thermodynamic predictions are shown to be in good agreement with experiments, we show that there are significant limitations in the commercially available thermodynamic databases. These limitations are due to the cumbersome task of maintaining complex multi-component databases. This work describes new software architecture, with a particular focus on uncertainty quantification, for improving these databases. This approach requires little input from the user, making it possible to be integrated into automated, high-throughput modeling infrastructure. With a more robust and efficient method for generating multi-component thermodynamic databases based on the latest available data, thermodynamic studies of non-conventional materials systems such as compositionally graded alloys will become more predictive and accurate.
SLM Process Variables and Part Geometry Optimization Based on Numerical Prediction of Process Induced Distortions: Maria San Sebastian1; Iņaki Setien1; Ane Miren Mancisidor1; Alberto Echeverria1; 1LORTEK
Distortions are critical in metal additive manufacturing, since they increase manufacturing costs, time and generate wastes and scraps due to dimensional inaccuracies. Currently, distortion control is mainly based on trial an error approach, not being part of the design constraints. Alternatively, a newly proposed Design Against Distortion paradigm is pursuing the development of numerical modelling strategies which can anticipate distortions early at the design stage. In line with this paradigm, this work is focused on the development of a rapid distortion prediction methodology applicable to selective laser melting (SLM) process, taking into account the influence of different process variables, (scanning parameters and strategy) This methodology is demonstrated to predict distortions of a real component manufactured by SLM, in order to determine the best supporting strategy and build up orientation. Therefore, current developments may entail an innovative way of designing and manufacturing SLM manufactured parts which are more robust against distortions.
Optimizing, Fabricating and Characterizing Additively Manufactured Process Tubing: Paul Korinko1; Haley McKee2; John Bobbitt1; Frederick List3; Sudarsanam Babu4; 1Savannah River National Laboratory; 2Honeywell Federal Manufacturing and Technology ; 3Oak Ridge National Laboratory; 4University of Tennessee -- Knoxville
Additive Manufacturing brings unique opportunities to the fabrication world, especially for complex, high value added components that are challenging if not impossible to fabricate using traditional technologies. The presentation compares first and second generation heat exchange tubing with a variety of shapes, internal geometries, and lengths of process tubing for a specialized heat exchanger that operates from nominally 140°C to -40°C. The baseline design was fabricated and new more printable heat exchanger tubes were designed and compared using FEM to show improved thermal performance while minimizing thermal strain. Components were built in the horizontal and vertical orientations and subjected to burst and tensile testing as well as microstructural and chemical analysis. This presentation will describe the application, design iterations, the processing conditions, measured properties, and future considerations for this unique application of AM for process tubes.