skip to main content


Title: Physics-embedded graph network for accelerating phase-field simulation of microstructure evolution in additive manufacturing
Abstract

The phase-field (PF) method is a physics-based computational approach for simulating interfacial morphology. It has been used to model powder melting, rapid solidification, and grain structure evolution in metal additive manufacturing (AM). However, traditional direct numerical simulation (DNS) of the PF method is computationally expensive due to sufficiently small mesh size. Here, a physics-embedded graph network (PEGN) is proposed to leverage an elegant graph representation of the grain structure and embed the classic PF theory into the graph network. By reformulating the classic PF problem as an unsupervised machine learning task on a graph network, PEGN efficiently solves temperature field, liquid/solid phase fraction, and grain orientation variables to minimize a physics-based loss/energy function. The approach is at least 50 times faster than DNS in both CPU and GPU implementation while still capturing key physical features. Hence, PEGN allows to simulate large-scale multi-layer and multi-track AM build effectively.

 
more » « less
NSF-PAR ID:
10372196
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
npj Computational Materials
Volume:
8
Issue:
1
ISSN:
2057-3960
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Multi-dimensional direct numerical simulation (DNS) of the Schrödinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high degrees of freedom (DoF). This study employs a physics-based reduced-order learning algorithm, enabled by the first principles, for simulation of the Schrödinger equation to achieve high accuracy and efficiency. The proposed simulation methodology is applied to investigate two quantum-dot structures; one operates under external electric field, and the other is influenced by internal potential variation with periodic boundary conditions. The former is similar to typical operations of nanoelectronic devices, and the latter is of interest to simulation and design of nanostructures and materials, such as applications of density functional theory. In each structure, cases within and beyond training conditions are examined. Using the proposed methodology, a very accurate prediction can be realized with a reduction in the DoF by more than 3 orders of magnitude and in the computational time by 2 orders, compared to DNS. An accurate prediction beyond the training conditions, including higher external field and larger internal potential in untrained quantum states, is also achieved. Comparison is also carried out between the physics-based learning and Fourier-based plane-wave approaches for a periodic case.

     
    more » « less
  2. Context. Molecular filaments and hubs have received special attention recently thanks to new studies showing their key role in star formation. While the (column) density and velocity structures of both filaments and hubs have been carefully studied, their magnetic field (B-field) properties have yet to be characterized. Consequently, the role of B-fields in the formation and evolution of hub-filament systems is not well constrained. Aims. We aim to understand the role of the B-field and its interplay with turbulence and gravity in the dynamical evolution of the NGC 6334 filament network that harbours cluster-forming hubs and high-mass star formation. Methods. We present new observations of the dust polarized emission at 850 μ m toward the 2 pc × 10 pc map of NGC 6334 at a spatial resolution of 0.09 pc obtained with the James Clerk Maxwell Telescope (JCMT) as part of the B-field In STar-forming Region Observations (BISTRO) survey. We study the distribution and dispersion of the polarized intensity ( PI ), the polarization fraction ( PF ), and the plane-of-the-sky B-field angle ( χ B_POS ) toward the whole region, along the 10 pc-long ridge and along the sub-filaments connected to the ridge and the hubs. We derived the power spectra of the intensity and χ B POS along the ridge crest and compared them with the results obtained from simulated filaments. Results. The observations span ~3 orders of magnitude in Stokes I and PI and ~2 orders of magnitude in PF (from ~0.2 to ~ 20%). A large scatter in PI and PF is observed for a given value of I . Our analyses show a complex B-field structure when observed over the whole region (~ 10 pc); however, at smaller scales (~1 pc), χ B POS varies coherently along the crests of the filament network. The observed power spectrum of χ B POS can be well represented with a power law function with a slope of − 1.33 ± 0.23, which is ~20% shallower than that of I . We find that this result is compatible with the properties of simulated filaments and may indicate the physical processes at play in the formation and evolution of star-forming filaments. Along the sub-filaments, χ B POS rotates frombeing mostly perpendicular or randomly oriented with respect to the crests to mostly parallel as the sub-filaments merge with the ridge and hubs. This variation of the B-field structure along the sub-filaments may be tracing local velocity flows of infalling matter in the ridge and hubs. Our analysis also suggests a variation in the energy balance along the crests of these sub-filaments, from magnetically critical or supercritical at their far ends to magnetically subcritical near the ridge and hubs. We also detect an increase in PF toward the high-column density ( N H 2 ≳ 10 23  cm −2 ) star cluster-forming hubs. These latter large PF values may be explained by the increase in grain alignment efficiency due to stellar radiation from the newborn stars, combined with an ordered B-field structure. Conclusions. These observational results reveal for the first time the characteristics of the small-scale (down to ~ 0.1 pc) B-field structure of a 10 pc-long hub-filament system. Our analyses show variations in the polarization properties along the sub-filaments that may be tracing the evolution of their physical properties during their interaction with the ridge and hubs. We also detect an impact of feedback from young high-mass stars on the local B-field structure and the polarization properties, which could put constraints on possible models for dust grain alignment and provide important hints as to the interplay between the star formation activity and interstellar B-fields. 
    more » « less
  3. Abstract

    SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.

     
    more » « less
  4. Microstructure evolution modeling using finite element crystal plasticity (FECP), Monte- Carlo (MC), and phase field (PF) methods are being used to simulate microstructure evolution in Ti-6Al-4V under thermomechanical loading conditions. FECP is used to simulate deformation induced evolution of the microstructure and compute heterogeneous stored energy providing additional source of energy to MC and PF models. The MC grain growth model, calibrated using literature and experimental data, is used to simulate α+𝛽 grain growth. A multi-phase field, augmented with crystallographic symmetry and orientation relationship between α-𝛽, is employed to model simultaneous evolution and growth of all twelve α-variants in 3D. The influence of transformation and coherency strain energy on α-variant selection is studied by coupling the model with the Khachaturyan-Shatalov formalism for elastic strain calculation. This FECP/MC/PF suite will be able to simulate evolution of grains in the microstructure and within individual 𝛽- grains during typical thermomechanical processing conditions. 
    more » « less
  5. The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control. 
    more » « less