Additive Manufacturing of Metals: Applications of Solidification Fundamentals: Physics-based and Data-based Modeling I
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Additive Manufacturing Committee, TMS: Solidification Committee
Program Organizers: Wenda Tan, The University of Michigan; Alex Plotkowski, Oak Ridge National Laboratory; Lang Yuan, University of South Carolina; Lianyi Chen, University of Wisconsin-Madison

Tuesday 8:00 AM
March 21, 2023
Room: 21
Location: SDCC

Session Chair: Wenda Tan, University of Michigan; Lang Yuan, University of South Carolina


8:00 AM  
Challenges in Wire-Arc Additive Manufacturing of Fe-Based Shape Memory Alloy: Soumyajit Koley1; Kuladeep Rajamudili1; Supriyo Ganguly1; 1Cranfield University
     Iron based shape-memory alloys are considered as an inexpensive alternative to Ni-Ti alloy suitable for seismic isolation application in civil structures. Fe-17Mn-10Cr-5Si-4Ni-0.5V-0.5C alloy contains 37 wt.% of total solute elements. Such rich multi-component metallurgical system leads to wide solidification temperature range which often subsequently leads to severe solute segregation and solidification cracking. Wire-arc additive manufacturing (WAAM) of Fe-17Mn-10Cr-5Si-4Ni-0.5V-0.5C alloy was attempted using a cold-wire fed plasma arc torch attached to a CNC gantry. Self-standing walls were manufactured. The process conditions were modelled using finite-element method to generate the cooling rates at different location of the wall. This information was used in diffusion-based calculation using commercial DICTRA software to generate the solute segregation profiles and solidification path. Later, different solidification cracking theories were used to calculate the cracking propensity at different location of the wall or at different process conditions.

8:20 AM  
A Machine Learning Approach to Fast Microstructure Predictions in Laser Powder Bed Fusion: Mason Jones1; Jean-Pierre Delplanque1; Theron Rodgers2; Daniel Moser2; 1University of California Davis; 2Sandia National Laboratories
    This work investigates the application of machine learning techniques developed for computer vision to the prediction of microstructures produced in additive manufacturing, with a goal of creating a model fast enough for effective optimization of process parameters. The approach taken is to couple a fast physics-based surrogate thermal model with a machine learning model to predict local and global grain size statistics produced during the laser powder bed fusion additive manufacturing process. The machine learning model is trained on ensemble data generated with the thermally coupled SPPARKS microstructure application. This work focuses specifically on the development of the machine learning model.

8:40 AM  
Assessment of Phase Evolution in Titanium-Niobium based Alloys During Rapid-Solidification: Theo Mossop1; David Heard2; Mert Celikin1; 1University College Dublin; 2Stryker
    The effect of varying cooling rates is studied on the microstructural evolution of Titanium-Niobium (Ti-Nb) based alloys with Tantalum (Ta) additions. A combined simulation and experimental approach is used to investigate the predictability of differences in microstructural evolution between rapid casting and additive manufacturing (AM) processes. Rods of Ti-25Nb and Ti-20Nb-10Ta (wt% and hereafter) were manufactured in diameters from 3mm to 10mm using suction casting into copper moulds. Finite element (FE) and thermodynamic modelling was used to calculate the cooling rates and temperature gradients of the alloys. Laser remelted surfaces were produced using varying AM parameters (laser power, scan speed, hatch spacing), the solidification parameters of which were estimated using the Rosenthal model for a moving heat source. The microstructural and mechanical differences between rapid casting and AM processing conditions were determined via SEM/EDS, XRD, and mechanical testing.

9:00 AM  
Bayesian Optimization of an Exponentially Modified Gaussian Heat Source Model for Laser-Based Additive Manufacturing: John Coleman1; Gerry Knapp1; Matt Rolchigo1; Benjamin Stump1; Alex Plotkowski1; 1ORNL
    Melt pool scale models of additive manufacturing (AM) processes can provide insight into process-structure-property relationships for AM parts. These models generally assume a Gaussian distribution of power density, which tend to overpredict peak temperatures in the melt pool, especially in the keyhole regime. This assumption can lead to erroneous predictions of the solidification conditions, and in turn, the grain structure development. An exponentially modified Gaussian power density model provides a more uniform distribution of heat in the melt pool and is shown to better approximate thermal conditions during keyhole formation. A surrogate-based Markov Chain Monte Carlo (MCMC) algorithm is used to calibrate the heat source shape, effective absorption, and power density distribution against experimental melt pool shapes over a wide range of processing conditions in both the conduction and keyhole processing modes. Temperature data from calibrated models is used in a cellular-automata grain structure prediction code and compared to experiments.

9:20 AM  
Effect of Laser Dwell Time on Pore Elimination in Selective Laser Melting of Metal Matrix Composites: Experimentally Validated Modeling: Ifeanyichukwu Olumor1; Andrii Maximenko1; Eugene Olevsky1; 1San Diego State University
     Many studies have been conducted to better understand the relationship between additive manufacturing process parameters and the resulting structure and properties of manufactured parts. In this study, Single-track experiments and modeling are conducted, with the aim to investigate the influence of process parameters such as laser dwell time in mitigating pore formation during selective laser melting of SS316L-WC composites. Results show that the filling of the pores between ceramic particles by the molten SS316L is dependent on the laser dwell time, which in turn, depends on the volume fraction of ceramic reinforcement and initial pore sizes between inclusions. Our experiments, in agreement with our model analysis, with special consideration of inertia effects, show that with the information of the materials property of the matrix phase in a composite system, the printing parameters can be chosen to yield the appropriate dwell time for pore free composites.

9:40 AM Break

9:55 AM  
In-situ Alloying of High Entropy Alloys by Laser Powder Bed Fusion: Insights from Molecular Dynamics Simulations: Yulia Klunnikova1; Arne J. Klomp1; Karsten Albe1; Marie Charrier1; 1TU Darmstadt
     Conventional additive manufacturing by laser powder bed fusion (LPBF) produces a complete part by successive sintering of layers of pre-alloyed particles. Alternatively, one can also start with a powder blend of single element particles and harness in-situ alloying, which could significantly accelerate processing. Multi-component systems, including high entropy alloys (HEA), are of particular interest due to their good printability with high consolidation and uniform high hardness.We present results from large scale molecular dynamics and finite difference method simulations of LPBF of the CoCrFeMnNi Cantor HEA. These simulations reveal that the individual elements mix in the melt pool and solidify partially in crystalline and amorphous states. Resulting elemental distributions, microstructures, quantity of crystal defects (stacking faults, twinning, and vacancies) are investigated with an eye on the influence of powder particles size, processing parameters, heat transport, and interdiffusion.

10:15 AM  
Microstructure Evolution in an As-Built IN625 Thin-wall Fabricated Via Laser Powder Bed Fusion: Pardis Mohammadpour1; Andre Phillion1; Hui Yuan1; 1McMaster University
    The unique thermal and solidification conditions during manufacturing of Laser Powder Bed Fusion (LPBF) parts results in multiscale microstructure heterogeneity leading to a variability in mechanical properties of LPBF parts. This study focuses on the process-structure-properties relationship in an as-built multi-layer LPBF IN625 part. Numerical thermal simulation, modern electron microscopy techniques, nanohardness test, and DIffusion-Controlled TRAnsformations (DICTRA) methods were utilized to investigate the spatial heterogeneity in terms of grain size and morphology, Primary Dendrite Arm Spacing (PDAS), microsegregation pattern, precipitation, and hardness along the build. It was found that the as-built microstructure contains mostly columnar Nickel–Chromium (γ) dendrites growing epitaxially along the build direction. Moreover, smaller grains, melt pools, and PDAS, and higher cooling rate and hardness were observed in the bottom layers in comparison to those in the top layers. Microsegregation patterns in multiple layers were also simulated using DICTRA and were compared with the microscopy results.

10:35 AM  
Modeling Microstructural Evolution during Laser Processing of Metallic Powders using a Hybrid Mesoscale-Continuum Approach: Ching Chen1; Sergey Galitskiy1; Dmitry Ivanov2; Ranadip Acharya3; Vijay Jagdale3; Avinash Dongare1; 1University of Connecticut; 2Lebedev Physical Institute; 3Collins Aerospace
    Laser melting is an emerging technique of additive manufacturing that enables the fabrication of complex products. The interactions of metallic powders with lasers can lead to evaporation (ablation), melting, and subsequent solidification. Understanding the microstructure evolution during this process requires capabilities to model laser energy absorption/dissipation of the powders with length scales of microns. This talk presents a hybrid mesoscale-continuum approach that combines quasi-coarse-grained-dynamics (QCGD) method with two-temperature model (TTM) to study melting and solidification behaviors of metal powders during laser processing at experimental scales. The QCGD/TTM method retains laser energy absorption, melting, and microstructure (defect) evolution behaviors, as predicted using an atomistic-continuum approach combining molecular dynamics (MD) with TTM. The QCGD/TTM simulations are validated by reproducing the MD/TTM predicted melting and ablation behavior of metal powders at nanoscales. The applicability of QCGD/TTM simulations to model laser-induced melting and solidification behavior of Ti powders at experimental scales will be presented.

10:55 AM  
Modeling Non-Equilibrium Partitioning in Concentrated Cu-Fe Alloys Manufactured by Laser Powder-fed Directed Energy Deposition: Daniel Yin1; Amit Misra1; 1University of Michigan
    Laser powder-fed directed energy deposition (DED-LB) produces high cooling rates within a small melt pool which can lead to high amounts of solute trapping during solidification. In this work, we test concentrated Cu-Fe alloys which are homogenous in the liquid phase and normally have negligible solid solubility at room temperature. A numerical model is used to quantify the solute trapping defined as the degree of non-equilibrium partitioning. Simulations were run to match the as-printed samples’ DED-LB processing parameters. Composition characterization by WDS and STEM-EDS confirmed high amounts of solute trapping in the form of nanoprecipitates in both the Cu and Fe grains, a nanoscale hierarchical microstructure with yield strengths much higher than Hall-Petch predicted strengthening. Our model produced good agreement with the measured Cu solute concentrations in the Fe grains but overpredicts the Fe solute concentration in the Cu grains due to deviation in the solidified phase fractions from equilibrium.

11:15 AM  Cancelled
Modeling the Hot Crack Susceptibility of Nickel-Based Superalloys by Laser Powder Bed Fusion.: Marcus Lam1; 1Monash University
    One key obstacle to the laser powder bed fusion (LPBF) of a broader range of nickel-based superalloys, especially the highest strength ones, is the lack of a hot crack indicators specifically for the LPBF process. The rapid cooling in the LPBF process induces sophisticated solidification phenomena such as the lack of back diffusion and solid trapping. This presentation reports a crack susceptibility study with experimental and computational methods on mainly the LPBF-produced IN738LC and other superalloys. Several classic and new solidification crack indicators were used to model the hot crack susceptibilities with the aid of calculation of phase diagram (CALPHAD) techniques. Some indicators considered rapid solidification physics explicitly to increase their modeling accuracies. The modeling results are compared to the experimental ones from literature or in-house experiments, indicating that the LPBF hot cracking tendency can generally be modeled for nickel-based superalloys, if the modeling assumptions and settings are appropriate.