Monte Carlo simulations of solidification and solid-state phase transformation during directed energy deposition additive manufacturing

Zhang, Zhao; Ge, P.; Li, J. Y.; Ren, Daxin

doi:10.1007/s40964-021-00253-8

Cited by 11 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This phenomenon can be also observed in laser additive manufacturing. [64][65][66] When the fifth layer is added, the average grain sizes in SZ on different layers calculated by the MC model are shown in Figure 14 determined by both process temperature and subsequent heated time. A larger average grain size is observed in SZ on bottom layer.…”

Section: Resultsmentioning

confidence: 99%

The simulation of microstructural evolutions in friction stir additive manufacturing

Zhang

Zhou

Tan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Both recrystallization and solid state phase transformation take key role for the determination of final mechanical properties in friction stir additive manufacturing (FSAM) of titanium alloy. Monte Carlo model is developed to simulate the microstructural changes and a two scale strategy is used to simulate both the recrystallization and the solid state phase transformation in FSAM of duplex titanium alloy. Results indicate that the selection of the building direction can lead to different temperature variations in FSAM due to the different heat accumulations. Lower temperature leads to lower cooling rate in FSAM. This is the reason that the volume fraction of α phase is decreased when the process temperature is decreased. Higher temperature leads to the formation of bigger grains when the rotating speed is increased or the transverse speed is decreased.

show abstract

Section: Resultsmentioning

confidence: 99%

The simulation of microstructural evolutions in friction stir additive manufacturing

Zhang

Zhou

Tan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…PF models solve equations for the evolution of order parameters that describe the system state to minimize the free energy of the system [132]. As noted by [133], this has the advantage of directly evolving a system towards thermodynamic equilibrium in real time. PF models have been applied to multiphase solidification, such as eutectic growth [134] and peritectic growth of ferrite and austenite phases in steel [135].…”

Section: Explicit Microstructure Modeling Methods: Sub-grain Scalementioning

confidence: 99%

“…Like CA, kMC was originally applied to casting problems and has recently been applied to AM. Liquid-solid and solid-solid phase transformation during AM processing has been simulated with kMC and successfully predicted multiple aspects of grain AM structures for Ni superalloys, titanium alloys, and steel [171,108,172,133]. However, accurately modeling texture using kMC necessitates that the probabilities allow preferential advance of grains with orientations near the local thermal gradient direction at a given point in time; while tuning of these probabilities may provide the ability to calibrate kMC models to experimental results better than CA (though without a true physical basis), calculation of the interface orientation and probabilities for each lattice point on the interface each time step can be computationally expensive.…”

Section: Explicit Microstructure Modeling Methods: Grain Scalementioning

confidence: 99%

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

Plotkowski¹,

Knapp²,

Coleman³

et al. 2023

View full text Add to dashboard Cite

The Advanced Materials and Manufacturing Technologies (AMMT) [1] program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed.

show abstract

“…However, as not many simulations have been created for this purpose, an analysis of the most outstanding simulations discussed in the above sections will serve as the basis for comparison (Table 1). Monte Carlo (MC) simulations have also been widely used in studying solidification and grain growth processes as they allow scientists to model and predict how microstructures/grains evolve over time [96,97]. The Monte Carlo method involves making random changes to the system and then deciding whether to accept or reject the change based on a probability that depends on the free energy of the system [98].…”

Section: Dendritic Needle Networkmentioning

confidence: 99%

A Review of Large-Scale Simulations of Microstructural Evolution during Alloy Solidification

Cusato,

Nabavizadeh,

Eshraghi

2023

Metals

View full text Add to dashboard Cite

During the past two decades, researchers have shown interest in large-scale simulations to analyze alloy solidification. Advances in in situ X-ray observations of the microstructural evolution of dendrites have shown defects that can be very costly for manufacturers. These simulations provide the basis for understanding applied meso-/macro-scale phenomena with microscale details using various numerical schemes to simulate the morphology and solve for transport phenomena. Methods for simulating methodologies include cellular automaton, phase field, direct interface tracking, level set, dendritic needle networks, and Monte Carlo while finite element, finite difference, finite volume, and lattice Boltzmann methods are commonly used to solve for transport phenomena. In this paper, these methodologies are explored in detail with respect to simulating the dendritic microstructure evolution and other solidification-related features. The current research, from innovations in algorithms for scaling to parallel processing details, is presented with a focus on understanding complex real-world phenomena. Topics include large-scale simulations of features with and without convection, columnar to equiaxed transition, dendrite interactions, competitive growth, microsegregation, permeability, and applications such as additive manufacturing. This review provides the framework and methodologies for achieving scalability while highlighting the areas of focus that need more attention.

show abstract

Monte Carlo simulations of solidification and solid-state phase transformation during directed energy deposition additive manufacturing

Cited by 11 publications

References 44 publications

The simulation of microstructural evolutions in friction stir additive manufacturing

The simulation of microstructural evolutions in friction stir additive manufacturing

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

A Review of Large-Scale Simulations of Microstructural Evolution during Alloy Solidification

Contact Info

Product

Resources

About