ICME 2023: App: AM Microstructure III
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory
Thursday 11:10 AM
May 25, 2023
Room: Boca I-III
Location: Caribe Royale
11:10 AM
Discrete Dislocation Dynamics Simulation Analysis of Plasticity and Size Effect in Additive Manufactured Metals: Caizhi Zhou1; 1University of South Carolina
Additive manufacturing (AM) of metals is to build metallic components through layer-by-layer that can make the prepared materials with unique geometries. Due to the rapid solidification and high cooling rates during AM process, the microstructure within AM metals has unique features, such as interlayer interfaces, molten pool solidification boundaries and intracrystalline substructure. It is critical to understand the relationship between the spatial heterogeneity of the microstructure and the mechanical properties of AM metals. In this work, discrete dislocation dynamics (DDD) simulations will be used to explore the effect of dislocation cells on the strength and anisotropy of the AM metals. In our DDD simulations, we will vary the dislocation cell size, dislocation density within the cell walls and loading directions to explore how these factors affect the dislocation behavior within the dislocation cells. In addition, our model will also consider the influence of solute concentrations.
11:30 AM
A Physics-Informed Multimodal Conditional Generative Model for Linking Process and Microstructure in Metal Additive Manufacturing: Kang-Hyun Lee1; Min Gyu Chung1; Yeon Su Lee1; Gun Jin Yun1; 1Seoul National University
In metal additive manufacturing (MAM), the thermal history and the grain growth associated with complex physics lead to the formation of distinguished microstructure compared with conventional manufacturing methods. A quantitative and robust process-structure (P-S) linkage for AM-processed alloys must be established to tailor the highly anisotropic as-built microstructure for obtaining desired mechanical properties. This work proposes a novel approach to model the P-S linkage for MAM with a deep-learned multimodal conditional generative model. To model the relationship between the source domain (temperature field) and the target domain (microstructure) in MAM, the training data obtained from high-fidelity thermo-fluid analysis and cellular automata (CA) based grain growth simulation is employed. The model can generate multiple inverse pole figure (IPF) maps, which can be controlled by latent space manipulation, for a given processing condition that agrees with the numerical simulation results in terms of grain morphologies and texture.
11:50 AM
Development of Digital Model Predicting Mechanical Properties of Inconel 718 for Powder Based Additive Manufacturing: Parimal Maity1; Mohit Singhal1; Jacob Kallivayalil2; 1Eaton India Innovation Center; 2Eaton Corporation
Recent evolutions in additive manufacturing (AM) technologies have provided significant opportunities to realize organic shape designs while concurrently achieving material properties to meet functional performance requirements. In this effort, an integrated multi-scale model has been developed to demonstrate process - microstructure - property relations. A numerical method has been established to determine local temperature gradients (TG) and cooling rates (CR) as outputs at the melt pool level. These outputs are taken as input to micro scale phase-field models to predict as-grown solidification microstructures for a range of input AM process parameters. Phase field predictions for solute distribution and evolution of γ' and γ'' precipitates during post process after AM have also been developed. Experimental results for model calibration and property validation are discussed.
12:10 PM
Directed Energy Deposition of Al-0.5Sc-0.5Si Alloy: Effect of Thermal Cycles in Microstructure and Mechanical Properties: Amit Singh1; Yasham Mundada1; Priyanshu Bajaj2; Sushil Mishra3; Amit Arora1; 1Indian Institute of Technology Gandhinagar; 2m4p material solutions GmbH; 3Indian Institute of Technology Bombay
Interest in Additive Manufacturing has significantly increased in aerospace, automobile, and electronic industries. Complex features can be built with the specimen in single step. Similarly, post-processing such as machining and heat treatment can also be eliminated to obtain an engineered specimen with better mechanical properties. However, there are numerous challenges in studying the microstructure and mechanical properties of the deposited specimen due to the very complex heating and cooling cycle. Therefore, the heat transfer and material flow model is developed to compute the thermal cycle and correlate with the structure and mechanical properties of the specimen in the multi-layer deposition of Al-0.5Sc-0.5Si alloy. The thermal cycles, melt pool dimension, and cooling rates are computed around the vicinity of the melt pool for multi-layer deposition, and solidification morphologies are predicted using the CET solidification map. The solidification morphologies are further correlated with the microhardness in the multi-layer deposition of Al-0.5Sc-0.5Si alloy.