Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.6
Journals
Metals
Special Issues
Simulation of Microstructure Evolution in Additive Manufacturing
share announcement

Simulation of Microstructure Evolution in Additive Manufacturing

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (10 May 2021) | Viewed by 31989

Special Issue Editors

Dr. Dayalan Gunasegaram

E-Mail Website
Guest Editor
Commonwealth Scientific and Industrial Research Organization, Clayton, Australia
Interests: additive manufacturing; multiphysics modeling; machine learning; digital twins; real-time control of processes
Special Issues, Collections and Topics in MDPI journals
Prof. Ingo Steinbach

E-Mail Website
Guest Editor
Interdisciplonary Centre for Advanced Materials Simulation, Ruhr-University Bochm, Bochum, Germany
Interests: phase-fild modelling and simulation; kinetics and thermodynamics of materials; multi-scale problems; pattern formation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling and simulation of microstructure evolution in a process such as additive manufacturing (AM) is highly beneficial for several reasons. Firstly, the permutations and combinations of alloy compositions and process parameters available in the field are so numerous that obtaining insights purely through experimental means becomes a costly and time-consuming exercise. Secondly, AM comprises a rapid solidification phenomenon that happens at a microscopic scale, which makes in situ monitoring of the process challenging and expensive. However, when we faithfully reconstruct the process in the virtual domain and create robust and reliable models that are validated, we increase our understanding of how alloy composition and processing route combine to influence the resulting microstructure. There are additional benefits; once the microstructural features are known, the local properties can be estimated as a function of these. In turn, the performance during the service of the manufactured part that comprises this microstructure can be predicted, along with other useful information such as its durability and fatigue life. In addition, a reliable microstructure model can also eliminate the need for destructive testing of expensive AM builds or, at the very least, reduce the sample sizes required for such testing.

Several numerical methods are capable of simulating microstructures in AM at the required level of resolution, i.e., at the mesoscopic scale that spans from nanometers to micrometers. Some examples of these are phase field, front-tracking, level set, lattice Boltzmann, and cellular automata. These varied techniques have unique advantages and disadvantages, which means modelers can select the best-suited method for their tasks based on a consideration of these. Each method also has its own set of unique challenges that modelers need to address—especially when simulating a highly non-equilibrium process like AM, where solidification is so rapid that many traditional theories break down.

Often, the temperature histories that are required for these mesoscopic models are obtained from continuum-scale simulations of the process. Therefore, modelers also deal with challenges relating to the efficient passing of information across the length and time scales.

In this Special Issue, we provide a platform for computational modelers to share their efforts on modeling microstructure evolution, using any suitable method, in the diverse ecosystem comprising several AM alloys and various AM processes. We invite contributions that cover a broad spectrum of areas that fall within this category, including but not restricted to solidification, solid-state transformations, nucleation strategies, new theories, modeling techniques, multiscale coupling, non-equilibrium methods, creation of input data such as properties, and experimental validation.

Dr. Dayalan Gunasegaram
Prof. Ingo Steinbach
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • phase field
  • cellular automata
  • lattice Boltzmann
  • level set
  • microstructure modeling
  • multiscale modeling
  • additive manufacturing
  • rapid solidification
  • non-equilibrium solidification

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

20 pages, 10631 KiB  
Editorial
Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives
by Dayalan R. Gunasegaram and Ingo Steinbach
Metals 2021, 11(9), 1425; https://doi.org/10.3390/met11091425 - 9 Sep 2021
Cited by 16 | Viewed by 5180
Abstract
Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is [...] Read more.
Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Figure 1

Research

Jump to: Editorial

12 pages, 3340 KiB  
Article
Numerical Study of Epitaxial Growth after Partial Remelting during Selective Electron Beam Melting in the Context of Ni–Al
by Helge Schaar, Ingo Steinbach and Marvin Tegeler
Metals 2021, 11(12), 2012; https://doi.org/10.3390/met11122012 - 13 Dec 2021
Cited by 3 | Viewed by 2691
Abstract
In the selective electron beam melting approach an electron beam is used to partially melt the material powder. Based on the local high energy input, the solidification conditions and likewise the microstructures strongly deviate from conventional investment casting processes. The repeated energy input [...] Read more.
In the selective electron beam melting approach an electron beam is used to partially melt the material powder. Based on the local high energy input, the solidification conditions and likewise the microstructures strongly deviate from conventional investment casting processes. The repeated energy input into the material during processing leads to the partial remelting of the already existing microstructure. To closer investigative this effect of partial remelting, in the present work the phase-field model is applied. In the first part the solidification of the referenced Ni–Al system is simulated in respect to selective electron beam melting. The model is calibrated such to reproduce the solidification kinetics of the superalloy CMSX-4. By comparison to experimental observations reported in the literature, the model is validated and is subsequently applied to study the effect of partial remelting. In the numerical approach the microstructures obtained from the solidification simulations are taken as starting condition. By systematically varying the temperature of the liquid built layer, the effect of remelting on the existing microstructure can be investigated. Based on these results, the experimental processing can be optimized further to produce parts with significantly more homogenous element distributions. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Graphical abstract

17 pages, 2571 KiB  
Article
Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses
by Akshay Bhutada, Sunni Kumar, Dayalan Gunasegaram and Alankar Alankar
Metals 2021, 11(8), 1167; https://doi.org/10.3390/met11081167 - 22 Jul 2021
Cited by 13 | Viewed by 3857
Abstract
The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material. Typically, large temperature gradients during the build stage are known to result in significant thermally induced residual [...] Read more.
The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material. Typically, large temperature gradients during the build stage are known to result in significant thermally induced residual stresses in parts made using the process. Such stresses are influenced by the underlying local microstructures. Given the extensive range of variations in microstructures, it is useful to have an efficient method that can detect and quantify cause and effect. In this work, an efficient workflow within the machine learning (ML) framework for establishing microstructure–thermal stress correlations is presented. While synthetic microstructures and simulated properties were used for demonstration, the methodology may equally be applied to actual microstructures and associated measured properties. The dataset for ML consisted of images of synthetic microstructures along with thermal stress tensor fields simulated using a finite element (FE) model. The FE model considered various grain morphologies, crystallographic orientations, anisotropic elasticity and anisotropic thermal expansion. The overall workflow was divided into two parts. In the first part, image classification and clustering were performed for a sanity test of data. Accuracies of 97.33% and 99.83% were achieved using the ML based method of classification and clustering, respectively. In the second part of the work, convolution neural network model (CNN) was used to correlate the microstructures against various components and measures of stress. The target vectors of stresses consisted of individual components of stress tensor, principal stresses and hydrostatic stress. The model was able to show a consistent correlation between various morphologies and components of thermal stress. The overall predictions by the model for all the microstructures resulted into R2≈0.96 for all the stresses. Such a correlation may be used for finding a range of microstructures associated with lower amounts of thermally induced stresses. This would allow the choice of suitable process parameters that can ensure that the desired microstructures are obtained, provided the relationship between those parameters and microstructures are also known. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Figure 1

16 pages, 6981 KiB  
Article
Simulation of Primary Particle Development and Their Impact on Microstructural Evolution of Sc-Modified Aluminum Alloys during Additive Manufacturing
by Mohammad Sadegh Mohebbi and Vasily Ploshikhin
Metals 2021, 11(7), 1056; https://doi.org/10.3390/met11071056 - 30 Jun 2021
Cited by 8 | Viewed by 2619
Abstract
The microstructures of additively manufactured Sc- and Zr-modified aluminum alloys are significantly influenced by the nucleation role of solid intermetallic particles in undercooled liquid. To replicate such effects, a precipitation model relying on L12-Al3Sc particles is developed. An initiation criterion is proposed based [...] Read more.
The microstructures of additively manufactured Sc- and Zr-modified aluminum alloys are significantly influenced by the nucleation role of solid intermetallic particles in undercooled liquid. To replicate such effects, a precipitation model relying on L12-Al3Sc particles is developed. An initiation criterion is proposed based on the precipitation kinetics of primary particles to address solute trapping under high solidification rates. Avrami’s equation is then used to estimate the progress of precipitation. The model is integrated into a cellular automata (CA) analysis to simulate the resulting solidified microstructure, in that the precipitation model is performed implicitly within the CA cells. It is shown that, in accordance with the experimental findings, the proposed simulation approach can predict the distinct fine- (FG) and coarse-grained (CG) zones at the fusion boundary and the meltpool core, respectively. The model can also deliver the reported enhancement of the FG zone under lower scanning speed and higher platform temperatures. These findings are explained in terms of particle number densities at different meltpool regions. Moreover, a semi-2D simulation with a very small cell size is suggested to address the extremely fine grain structure within the FG zone. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Figure 1

15 pages, 7458 KiB  
Article
A Coupled DEM/SPH Computational Model to Simulate Microstructure Evolution in Ti-6Al-4V Laser Powder Bed Fusion Processes
by Sharen Cummins, Paul W. Cleary, Gary Delaney, Arden Phua, Matthew Sinnott, Dayalan Gunasegaram and Chris Davies
Metals 2021, 11(6), 858; https://doi.org/10.3390/met11060858 - 24 May 2021
Cited by 19 | Viewed by 4590
Abstract
A new multi-stage three-dimensional transient computational model to simulate powder bed fusion (L-PBF) additive manufacturing (AM) processes is presented. The model uses the discrete element method (DEM) for powder flow simulation, an extended smoothed particle hydrodynamics (SPH) for melt pool dynamics and a [...] Read more.
A new multi-stage three-dimensional transient computational model to simulate powder bed fusion (L-PBF) additive manufacturing (AM) processes is presented. The model uses the discrete element method (DEM) for powder flow simulation, an extended smoothed particle hydrodynamics (SPH) for melt pool dynamics and a semi-empirical microstructure evolution strategy to simulate the evolving temperature and microstructure of non-spherical Ti-6Al-4V powder grains undergoing L-PBF. The highly novel use of both DEM and SPH means that varied physics such as collisions between non-spherical powder grains during the coating process and heat transfer, melting, solidification and microstructure evolution during the laser fusion process can be simulated. The new capability is demonstrated by applying a complex representative laser scan pattern to a single-layer Ti-6Al-4V powder bed. It is found that the fast cooling rate primarily leads to a transition between the β and α martensitic phases. A minimal production of the α Widmanstatten phase at the outer edge of the laser is also noted due to an in situ heat treatment effect of the martensitic grains near the laser. This work demonstrates the potential of the coupled DEM/SPH computational model as a realistic tool to investigate the effect of process parameters such as powder morphology, laser scan speed and power characteristics on the Ti-6Al-4V powder bed microstructure. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Figure 1

21 pages, 47300 KiB  
Article
Non- and Quasi-Equilibrium Multi-Phase Field Methods Coupled with CALPHAD Database for Rapid-Solidification Microstructural Evolution in Laser Powder Bed Additive Manufacturing Condition
by Sukeharu Nomoto, Masahito Segawa and Makoto Watanabe
Metals 2021, 11(4), 626; https://doi.org/10.3390/met11040626 - 13 Apr 2021
Cited by 17 | Viewed by 4124
Abstract
A solidification microstructure is formed under high cooling rates and temperature gradients in powder-based additive manufacturing. In this study, a non-equilibrium multi-phase field method (MPFM), based on a finite interface dissipation model, coupled with the Calculation of Phase Diagram (CALPHAD) database, was developed [...] Read more.
A solidification microstructure is formed under high cooling rates and temperature gradients in powder-based additive manufacturing. In this study, a non-equilibrium multi-phase field method (MPFM), based on a finite interface dissipation model, coupled with the Calculation of Phase Diagram (CALPHAD) database, was developed for a multicomponent Ni alloy. A quasi-equilibrium MPFM was also developed for comparison. Two-dimensional equiaxed microstructural evolution for the Ni (Bal.)-Al-Co-Cr-Mo-Ta-Ti-W-C alloy was performed at various cooling rates. The temperature-γ fraction profiles obtained under 105 K/s using non- and quasi-equilibrium MPFMs were in good agreement with each other. Over 106 K/s, the differences between the non- and quasi-equilibrium methods grew as the cooling rate increased. The non-equilibrium solidification was strengthened over a cooling rate of 106 K/s. Columnar-solidification microstructural evolution was performed at cooling rates of 5 × 105 K/s to 1 × 107 K/s at various temperature gradient values under a constant interface velocity (0.1 m/s). The results show that, as the cooling rate increased, the cell space decreased in both methods, and the non-equilibrium MPFM was verified by comparing with the quasi-equilibrium MPFM. Our results show that the non-equilibrium MPFM showed the ability to simulate the solidification microstructure in powder bed fusion additive manufacturing. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Graphical abstract

12 pages, 15073 KiB  
Article
Integration of Processing and Microstructure Models for Non-Equilibrium Solidification in Additive Manufacturing
by Noah Sargent, Mason Jones, Richard Otis, Andrew A. Shapiro, Jean-Pierre Delplanque and Wei Xiong
Metals 2021, 11(4), 570; https://doi.org/10.3390/met11040570 - 1 Apr 2021
Cited by 23 | Viewed by 6819
Abstract
Integration of models that capture the complex physics of solidification on the macro and microstructural scale with the flexibility to consider multicomponent materials systems is a significant challenge in modeling additive manufacturing processes. This work aims to link process variables, such as energy [...] Read more.
Integration of models that capture the complex physics of solidification on the macro and microstructural scale with the flexibility to consider multicomponent materials systems is a significant challenge in modeling additive manufacturing processes. This work aims to link process variables, such as energy density, with non-equilibrium solidification by integrating additive manufacturing process simulations with solidification models that consider thermodynamics and diffusion. Temperature histories are generated using a semi-analytic laser powder bed fusion process model and feed into a CALPHAD-based ICME (CALPHAD: Calculation of Phase Diagrams, ICME: Integrated Computational Materials Engineering) framework to model non-equilibrium solidification as a function of both composition and processing parameters. Solidification cracking susceptibility is modeled as a function of composition, cooling rate, and energy density in Al-Cu Alloys and stainless steel 316L (SS316L). Trends in solidification cracking susceptibility predicted by the model are validated by experimental solidification cracking measurements of Al-Cu alloys. Non-equilibrium solidification in additively manufactured SS316L is investigated to determine if this approach can be applied to commercial materials. Modeling results show a linear relationship between energy density and solidification cracking susceptibility in additively manufactured SS316L. This work shows that integration of process and microstructure models is essential for modeling solidification during additive manufacturing. Full article
(This article belongs to the Special Issue Simulation of Microstructure Evolution in Additive Manufacturing)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop