Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): Metals Multi-Scale Simulation
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology
Wednesday 1:30 PM
August 17, 2022
Room: Regency Ballroom III & IV
Location: Hyatt Regency Bethesda
Session Chair: Matthew Rolchigo, Oak Ridge National Laboratory
1:30 PM Invited
The Additive Manufacturing Moment Measure Approach to Laser Powder Bed Fusion Process Qualification: Samuel Hocker1; Brodan Richter1; Joseph Zalameda1; Wesley Tayon1; Erik Frankforter1; Peter Spaeth1; Edward Glaessgen1; 1NASA Langley Research Center
Qualification of a laser powder bed fusion additive manufacturing (LPBF-AM) process requires knowledge of the multi-scale material physics during the process, per part. As the LPBF-AM build occurs, each moment is influenced by the process history. Knowledge of the build sequence can be used to generate a discretized time-space-condition point field that when coupled with a nearest neighbors’ calculation results in a generalized and fully parallel process model computation. This GPU accelerated approach was developed for part-scale analysis of build files along with in-situ process monitoring sensor data and is termed the “Additive Manufacturing Moment Measure” (AM3). The AM3 approach will be presented and then used to evaluate an AM Bench relevant geometry with synchronized in-situ process data, ex-situ nondestructive evaluation, and optical microscopy observations. These comparisons permit a better understanding of how the process actions can affect the LPBF-AM build quality and the signals generated during in-situ process monitoring.
2:00 PM
DigitalClone for Additive Manufacturing (DC-AM): an Integrated Computational Materials Engineering Platform to Model Metal AM Process and Performance: Jingfu Liu1; Ziye Liu1; Behrooz Jalalahmadi1; 1Sentient Science
DigitalClone for Additive Manufacturing (DC-AM) is a multi-scale and multi-physics SaaS software to computationally assess the quality and performance of additively manufactured metal parts. DC-AM currently focuses on laser power bed fusion process (LPBF), with potential to extend to direct energy deposition (DED) and other AM processes. It uniquely links process-microstructure-fatigue modules to provide design and computational testing. Process module is to simulate as-build part distortion and residual stress; Microstructure module is to simulate grain morphology, grain size, and porosity; Fatigue module is to simulate component fatigue life using Sentient’s patented microstructure-based modeling approach. In this talk, we will present detailed features of DC-AM software along with the underlying modeling methodology. Case studies and experimental validation of different materials (e.g., Ti64, IN718, 17-4 PH, AlSi10Mg) and machines will be presented to demonstrate the feasibility of DC-AM. Additionally, we will share our vision of industrial needs and future development.
2:20 PM
Formation of Crystal Defects in Rapid Solidification: Tatu Pinomaa1; Matti Lindroos1; Paul Jreidini2; Matias Haapalehto1; Kais Ammar3; Lei Wang4; Samuel Forest3; Nikolas Provatas2; Anssi Laukkanen1; Napat Vajragupta1; 1VTT Technical Research Centre of Finland Ltd; 2McGill University; 3CNRS Mines ParisTech; 4Federal Institute for Materials Research and Testing (BAM)
Rapid solidification leads to unique microstructural features, where a less studied topic is the formation of various crystal defects, including point defects, high dislocation densities, as well as gradients and splitting of the crystalline orientation. As these defects critically affect the material’s mechanical properties and performance features, it is important to understand the defect formation mechanisms, and how they depend on the solidification conditions and alloying.To illuminate the formation mechanisms of the rapid solidification induced crystal defects, we conduct a multiscale modeling analysis consisting of bond-order potential based molecular dynamics (MD), phase field crystal based amplitude expansion (PFC-AE) simulations, and sequentially coupled phase field– crystal plasticity (PF–CP) simulations. The resulting dislocation densities are quantified and compared to past experiments. The atomistic approaches (MD,PFC) can be used to calibrate continuum level crystal plasticity models, and the framework adds mechanistic insights arising from the multiscale analysis.
2:40 PM
Multiphysics Modeling of Multicomponent Powder Beds for Metal Additive Manufacturing: Arash Samaei1; Zhongsheng Sang1; Jon-Erik Mogonye2; Gregory Wagner1; 1Northwestern University; 2Army Research Laboratory
Multicomponent powder beds are being actively explored to address common issues in additively-manufactured aluminum parts such as hot cracking, pore formation, and surface oxidation. In particular, we will discuss the modeling of multicomponent Al-Zr powder beds for light-weight aluminum alloys. Multiphysics models are often used to investigate the underlying physics controlling AM processes using high-fidelity simulations of individual powder melting into the substrate. Here, we will present a novel multiphysics-multiphase model to simulate multicomponent powder beds during AM process. A convection-diffusion formulation for component fraction, in conjunction with the Navier-Stokes equations and a new enthalpy form of the energy equation, enable us to understand the phase evolution in multicomponent beds and the dispersion of solute metals in the melt pool during the process. Results of the presented models will be discussed and compared with available experimental data from literature and our collaborators.
3:00 PM
Advanced Computational Module for Microstructural Prediction in Metal Additive Manufacturing: Hamedreza Hosseinzadeh1; Mark Horstemeyer2; 1University of South Carolina; 2Liberty University
One of the challenges in metal additive manufacturing is the prediction of microstructure achieved by the complex heat input during 3D printing. The local grain topology and precipitates’ formation/distribution are the features that our codes can simulate. However, all these features cannot be predictable by one computational method. So, this code uses discrete (Cellular Automata) and continuous (Fickian model) methods. The code has been designed and developed to accelerate microstructural engineering in metal additive manufacturing, focusing on the direct metal deposition method.
3:20 PM Break