8th World Congress on Integrated Computational Materials Engineering (ICME 2025): ICME Application to Advanced Manufacturing II
Program Organizers: Victoria Miller, University of Florida; Stephen DeWitt, Oak Ridge National Laboratory
Tuesday 1:30 PM
June 17, 2025
Room: Platinum Ballroom 3
Location: Anaheim Marriott
Session Chair: Matthew Dantin, Naval Surface Warfare Center Carderock Division
1:30 PM Invited
Leveraging Laser Parameters and Layer Remelting to Tailor Microstructure in Laser Powder Bed Fusion: Nicole Aragon1; Theron Rodgers1; Daniel Moser1; 1Sandia National Laboratories
Additive manufacturing (AM) processes, such as laser powder bed fusion (LPBF), provide unique opportunities to create freeform and complex parts. However, AM processing conditions significantly influence the as-solidified microstructure. In addition, AM processes introduce additional complexities such as remelting due to subsequent laser passes. We present a three-dimensional model that simulates solidification, solid-state evolution phenomena, and texture evolution using Monte Carlo methods. The simulations couple microstructure evolution with an analytical Green’s function-based thermal model to create a fully integrated microstructural prediction tool. The developed tool is used to study LPBF of 316L stainless steel. In this work, several process conditions are considered along with additional layer remelting strategies using low and high energy densities to evaluate the effect on grain morphology and texture. The simulation results are validated with experiments performed at equivalent process parameters. Results show that grain size and texture strength increase with total energy density.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
2:00 PM
A Framework for Efficient Part-Scale Microstructure Prediction in Laser Powder Bed Ti-6Al-4V Using Combined Physics-Based Modeling and Machine Learning Surrogate Methods: Anthony Spangenberger1; Bonnie Whitney1; Diana Lados1; 1Worcester Polytechnic Institute
Microstructure formation in additive manufacturing (AM) spans lengths from centimeter-scale components to micrometer-scale grain sizes, making their simultaneous resolution in computational simulations intractable with current high-fidelity physics-based methods. These models are needed to simulate microstructure sensitivity to process parameters, as the basis for subsequent property prediction models, and to mitigate process defects and anisotropy. A three-part modeling framework for laser powder bed Ti-6Al-4V is proposed to address this deficit: (i) coupled continuum heat transfer and kinetic Monte Carlo simulation of β grain morphology and texture at the part-scale, (ii) phase field (PF) modeling of the β→α/α’ solid-state transformation at the subgrain-scale, and (iii) a scale-bridging surrogate of the PF model for transient α/α’ phase fraction and width predictions. Model calibration and validation are supported by in-situ synchrotron heat treatment studies and electron backscatter diffraction data that inform transformation kinetics and grain morphological/textural variations across a wide range of processing parameters.
2:20 PM
Validating a Simulation Toolchain to Predict Process-Structure-Property Maps for Additively Manufactured Aluminum Alloys: Stephen DeWitt1; John Coleman1; Alex Plotkowski1; 1Oak Ridge National Laboratory
Given the vast processing space attainable through additive manufacturing, a trial-and-error approach to finding acceptable processing conditions is time-consuming and difficult for new alloys. Efficiently finding the optimal processing conditions that balance multiple considerations (e.g. minimize porosity and maximize yield strength) is even more difficult. In this presentation we discuss a simulation toolchain that combines thermal process simulations with analytic structure and property models to map standard laser powderbed fusion process parameters (e.g., power, velocity, hatch spacing, spot size) to common defects and mechanical properties. We demonstrate how this toolchain can be calibrated from simple single-track experiments and create process-structure-property maps for an example aluminum-copper-manganese-zirconium (ACMZ) alloy. Finally, we compare the predictions of the simulation toolchain to experimental characterization data for validation and discuss future research directions given these results.
2:40 PM
A Microstructure Modelling Framework for Multi-Laser Powder Bed Fusion of Ti-6Al-4V: Hugh Banes1; Prashant Jadhav1; Magnus Anderson2; Hector Basoalto1; 1University of Sheffield; 2Thermo-Calc Software AB.
Widespread adoption of additive manufacturing techniques for titanium alloys is currently limited by uncertainty in the microstructure variations introduced by the process. Novel heat sources, including Multiple-Laser Powder Bed Fusion (M-LPBF), have potential to improve part quality, however the relation between the processing parameters and final microstructure is not fully understood. To address this, an ICME framework has been developed, using a Representative Volume Element (RVE) approach to simulate the thermal fields induced by the laser-powder system, and two microstructure models. The Johnson-Mehl-Avrami-Kologorov (JMAK) description of the solid-state transformation in titanium, and a Cellular Automata (CA) description of the solidification and growth kinetics of the β phase structure. This framework was applied to M-LPBF cases, with a series of scanning strategies computed and microstructures examined. The combination of single-track scans and multiple-layer hatches tested with focused and defocused additional lasers revealed that in certain cases beneficial microstructures could be achieved.
3:00 PM Break
3:30 PM
Comparative Study of Melt Pool Dynamics and Keyhole Formation in Laser Powder Bed Fusion Process (LPBF) of SS316L and Ti-6Al-4V: Amrita Dixit1; Amarendra Singh1; 1Indian Institute of Technology Kanpur
Understanding melt pool dynamics during laser-material interaction is critical to achieving high-quality mechanical properties in components produced through laser powder bed fusion (LPBF). This study examines the effects of constant versus variable absorptivity on keyhole formation predictions for Ti-6Al-4V alloy via numerical simulation. A mesoscale model is developed to incorporate key physical phenomena such as Marangoni convection, recoil pressure, convective losses, and vaporization losses, while a ray tracing method with Fresnel absorptivity simulates variable absorptivity. The constant absorptivity model is validated against existing data for SS316L alloy, whereas the variable absorptivity model is validated for Ti-6Al-4V alloy. Using the validated model, experimental data for SS316L alloy are further validated to identify processing parameters for keyhole formation. This will help in the optimization of the processing parameter for the control of melt pool dynamics. These insights enhance the understanding of melt pool dynamics in both conduction and keyhole melting modes.
3:50 PM
Characterizing the Influence of Compositional and Thermocapillary Gradient Variations on the Temperature Profiles and Melt Pool Dimensions of LPBF-Processed Al 7xxx Alloys: Chukwudalu Uba1; 1University of Louisiana Lafayette
Additive manufacturing (LPBF) is central to Industry 4.0, allowing the production of complex, high-precision parts across aerospace and manufacturing sectors. However, LPBF poses challenges in characterizing melt pool dynamics due to current experimental limitations. Concerning the LPBF of Al 7xxx alloys, the thermal–fluidic transport effects on the temperature distribution and melt pool characteristics have been ignored. This study proposes an integrated experimental–computational framework to address these challenges. Building on our previous study of CALPHAD-based Al 7xxx alloy design, the designed alloys’ thermocapillary gradients were characterized using sessile drop experiments and CALPHAD. Finally, finite element-based heat transfer–fluid flow models were developed to simulate the LPBF process, utilizing the characterized thermocapillary gradient data. The results revealed that compositional and thermocapillary gradient variations affected the temperature distributions and melt pool dimensions. This model enhances the understanding of process–structure–property relationships in LPBF, aiding material design and process parameter optimization.
4:10 PM
Multiphysics Modeling of Metal Matrix Composite (MMC) Additive Manufacturing Process including Melt Pool Characterizing and Grain Growth: Mingyu Chung1; Kang-Hyun Lee1; Jae Eun Park1; Gun Jin Yun1; 1Seoul National University
Direct Energy Deposition (DED) is a widely utilized AM process to produce large components and repair damaged parts. Attempts have been made to obtain improved deposition-rates and mechanical properties by applying Metal Matrix Composite (MMC) powders to conventional DED process. However, the Multiphysics phenomena including melt pool evolution, nucleation, and phase transformation which are affected by the reinforcements are highly complex, and it hinders building PSP linkage for the MMC AM process. To fill this gap, computational analysis consisting of Thermo-Fluid and Cellular Automata model is constructed in this study; first one to estimate the melt pool formation and distribution of reinforcements in the melt pool, latter to predict the microstructure. Unlike previous studies, the results demonstrated that the analysis used in this study enables building linkage between Process and Structure for the MMC AM process and gives possibilities of optimizing the process to achieve the targeted quality and performance.