ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Abstract

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

“…Parallel implementation of MEUMAPPS-SS on the ORNL supercomputer Summit was accomplished via Message Passing Interface and the use of a parallel three-Dimensional fast Fourier transforms (P3DFFT) package [5]. MEUMAPPS-SS shows excellent scaling over thousands of CPU cores in Summit [6].…”

Fast-throughput simulations of laser-based additive manufacturing in metals to study the influence of processing parameters on mechanical properties

“…To reduce the overhead of iterative experimental testing, computational modeling is increasingly being used to simulate the microstructures resulting from LPBF processing [17] , [18] , [19] , [20] , [21] , [22] . Models that take a physics-based approach to couple printing parameters (e.g., laser power, scan speed, hatch spacing, layer height) to solidification characteristics of the deposited material (e.g., specific heat, density, enthalpy, diffusion rates) can be utilized to predict features of the resulting microstructure, and in some cases, predict the mechanical behavior of the as-built components.…”

“…However, high-fidelity modeling of the additive manufacturing process has proven difficult due to the influence of multi-scale and multi-physics phenomena such as nucleation and solidification, powder packing and multi-pass effects, fluid flow and Marangoni effects, martensitic transformations, as well as the contribution from defects such as key-holing, lack of fusion, vaporization, solute segregation, and hot cracking. As such, various simulation techniques have been employed to capture different mechanisms of the fabrication process, including phase field modeling (PFM) [23] , [24] , [25] , [26] , kinetic Monte Carlo (kMC) [27] , [28] , the finite element method (FEM) [21] , [29] , [30] , [31] , [32] , [33] , computational fluid dynamics (CFD) [20] , [34] , and cellular automata (CA) [35] , [36] , [37] , [38] , [39] .…”

Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes

“…In recent years, numerous approaches for the AM process modeling have been proposed, varying the model's complexity from simple heat conduction to a complicated thermal-fluid model. The usual physics-based model requires extensive computational effort to reasonably solve the problem considering the AM process's inherent length and time scale 20,21 . The simplest of these modeling approaches was to consider the heat conduction equation 22,23 .…”