Predictive modeling of laser and electron beam powder bed fusion additive manufacturing of metals at the mesoscale

Zakirov, Andrey; Belousov, Sergei; Bogdanova, M. V.; Korneev, Boris; Stepanov, A.; Perepelkina, Anastasia; Levchenko, V. D.; Meshkov, Andrey; Потапкин, Б. В.

doi:10.1016/j.addma.2020.101236

Cited by 43 publications

(24 citation statements)

References 49 publications

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“…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 nanometres to micrometres) and at the continuum scale. Some examples of these are front-tracking [28], phase field (PF) [12,19,[29][30][31], level set (LS) [32], lattice Boltzmann (LB) [30,33], Potts kinetic Monte Carlo (kMC) [12,19,30,34], cellular automata [12,19,29,30] and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory-based phenomenological model [35,36]. These varied techniques have unique strengths and weaknesses [12,19,30] which means modellers can select the method best suited to their tasks based on these considerations.…”

Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

“…These are either measured during experiments or estimated at the continuum scale using computational fluid dynamics (CFD) or the finite element method (FEM). Recently, the use of lattice Boltzmann method-based hydrodynamics tools for the purpose was demonstrated [33,42]. These can take into account the random distributions of powder particles by size in a layer, and the propagation of the laser (or electron beam) that includes multiple reflections, phase transitions, thermal conductivity, and detailed liquid dynamics of the molten metal.…”

Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

“…While many of the modelling methods used for microstructure modelling are highly parallelisable [12,33], they still fall short of providing the speeds required to simulate the formation of microstructures in good detail for a reasonable volume of a part. Thus, there is scope to improve on computational efficiency by taking advantage of new hardware paradigms such as petascale and exascale computing that are currently on the horizon (e.g., see [53]).…”

Section: Strategies For Parallelisation and Preparing For Petascale And Exascale Computingmentioning

confidence: 99%

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Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Gunasegaram

Steinbach

2021

Metals

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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.

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Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

Section: Strategies For Parallelisation and Preparing For Petascale And Exascale Computingmentioning

confidence: 99%

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Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Gunasegaram

Steinbach

2021

Metals

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“…Zakirov et al [55] presented the results of 3D modeling of with Gaussian distributed particles [53] Fig. 14: Different regimes of laser absorption during powder bed fusion process in 3D numerical simulations: cross-section along axis of laser movement [55] the laser and electron beam powder bed fusion process at the mesoscale with a self-developed advanced multi-physical numerical tool. The hydrodynamics and thermal conductivity core of the tool is based on the lattice Boltzmann method.…”

Section: Melting and Flow Behavior Of Metal Particles And Defect Formation Mechanism Based On Discrete Powder Particle Heat Flux Couplingmentioning

confidence: 99%

A review on metal additive manufacturing: modeling and application of numerical simulation for heat and mass transfer and microstructure evolution

Liu

Gao

et al. 2021

China Foundry

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Metal additive manufacturing technology has been widely used in prototyping, parts manufacturing and repairing. Metal additive manufacturing is a multi-scale and multi-physical coupling process with complex physical phenomena of heat and mass transfer and microstructure evolution. It is hard to directly observe the dynamic behavior and microstructure evolution of molten pool during additive manufacturing. Therefore, numerical simulation of additive manufacturing process is significant since it can efficiently and pertinently predict and analyze the physical phenomena in the process of metal additive manufacturing, and provide a reference for technological parameters selection. In this review, the research progress of numerical simulation of metal additive manufacturing is discussed. Various aspects of numerical simulation models are reviewed, including: (1) Introduction of basic control method and physical description of numerical simulation models;(2) Comparison of various heat and mass transfer models based on different physical assumptions (heat conduction model; heat flux coupling model; discrete powder particle heat flux coupling model); (3) Applications of various microstructure evolution models [phase field (PF), cellular automata (CA), and Monte Carlo (MC)]. Finally, the development trend of numerical simulation of metal additive manufacturing, including the thermal-flow-solid coupling model and deep learning for numerical model, is analyzed.

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“…In this regard, mathematical modeling methods are rather relevant due to the fact that they can be used to obtain the data on processes that have occurred in a molten pool by conducting computational experiments. Today, a large number of papers have been published on the analysis of heat and mass transfer phenomena in the implementation of additive technologies [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model for Metal Transfer Study in Additive Manufacturing with Electron Beam Oscillation

et al. 2021

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A computer model has been developed to investigate the processes of heat and mass transfer under the influence of concentrated energy sources on materials with specified thermophysical characteristics, including temperature-dependent ones. The model is based on the application of the volume of fluid (VOF) method and finite-difference approximation of the Navier–Stokes differential equations formulated for a viscous incompressible medium. The “predictor-corrector” method has been used for the coordinated determination of the pressure field which corresponds to the continuity condition and the velocity field. The modeling technique of the free liquid surface and boundary conditions has been described. The method of calculating surface tension forces and vapor recoil pressure has been presented. The algorithm structure is given, the individual modules of which are currently implemented in the Microsoft Visual Studio environment. The model can be applied for studying the metal transfer during the deposition processes, including the processes with electron beam spatial oscillation. The model was validated by comparing the results of computational experiments and images obtained by a high-speed camera.

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Predictive modeling of laser and electron beam powder bed fusion additive manufacturing of metals at the mesoscale

Cited by 43 publications

References 49 publications

Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

A review on metal additive manufacturing: modeling and application of numerical simulation for heat and mass transfer and microstructure evolution

Mathematical Model for Metal Transfer Study in Additive Manufacturing with Electron Beam Oscillation

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