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Parts produced via laser powder-bed fusion additive manufacturing exhibit complex microstructures that depend on processing variables and often vary widely in crystallographic texture and grain morphology. The need to understand, predict, and control these microstructural variations motivates the development of modeling tools capable of accurately...
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The effects of 1 wt% HfO2 nano-dispersoid addition on the microstructure of a high-γ' Ni-8.5Cr-5.5Al-1Ti (wt%) model superalloy are investigated after manufacturing via laser-based powder-bed fusion (PBF-LB). Despite their very high melting point, HfO2 dispersoids are not fully stable during their short stay in the melt pool. At the nanoscale, the...
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... In recent years, the topic of grain formation and microstructure representation has gained prominence, underscored by the research presented in the study by Pauza et al. [11]. This research highlights the evolving understanding of how grains are formed and organized within materials, especially in the context of AM processes. ...
... This method involves modeling the material subjected to external loads on a mesoscale and subsequently averaging the effects to predict the behavior of the material at the macroscale. Numerical methods [11,15], including the Crystal Elasticity Finite Element (CEFE) method [16,17], and the Crystal Plasticity Finite Element (CPFE) method [18] offer a sophisticated approach for the calculation of the homogenized mechanical properties. These simulations enable direct comparisons with experimental outcomes or can be utilized autonomously to methodically investigate the influence of microstructural features on material behavior. ...
This study provides a methodology for exploring the microstructural and mechanical properties of the Haynes®282® alloy produced via the Powder Bed Fusion-Electron Beam (PBF-EB) process. Employing 2D Electron Backscatter Diffraction (EBSD) data, we have successfully generated 3D representations of columnar microstructures using the Representative Volume Element (RVE) method. This methodology allowed for the validation of elastic properties through Crystal Elasticity Finite Element (CEFE) computational homogenization, revealing critical insights into the material behavior. This study highlights the importance of accurately representing the grain morphology and crystallographic texture of the material. Our findings demonstrate that created virtual models can predict directional elastic properties with a high level of accuracy, showing a maximum error of only ~5% compared to the experimental results. This precision underscores the potential of our approach for predictive modeling in Additive Manufacturing (AM), specifically for materials with complex, non-homogeneous microstructures. It can be concluded that the results uncover the intricate link between microstructural features and mechanical properties, underscoring both the challenges encountered and the critical need for the accurate representation of grain data, as well as the significance of achieving a balance in EBSD area selection, including the presence of anomalies in strongly textured microstructures.
... Even with intermediate samples, the EBSD measurements alone will provide only a partial picture of the forming process. Incorporating the EBSD measurements into material models is needed to obtain a more detailed understanding of the microstructural evolution at each stage of the additive manufacturing process [26]. ...
Electron backscatter diffraction (EBSD) is an excellent tool for characterizing the crystallographic orientation aspects of the microstructure of polycrystalline material. In some additively manufactured materials, the material may undergo a phase transformation during the forming process. Although EBSD can only characterize the final microstructure, neighbor information from orientation mapping allows the microstructure before the phase transformation to be reconstructed, provided that the parent–child orientation relationship is known. An investigation of the effectiveness of the reconstruction algorithms for capturing the grain size as well as orientation gradients is undertaken with a focus on additively manufactured Ti-alloy. The EBSD results, coupled with reconstruction algorithms, reveal information on the prior grain size as well as the plastic flow of the material.
... As a result, the melt pool depth is identically equal to half the melt pool width. Following the approach outlined by Pauza et al. [33] to account for different melt pool shapes, the equation for R is modified to where z is a scaling factor. Using z < 1 results in the melt pool depth being greater than half the width, which will occur in the transition and keyhole melt pool regimes of interest in the present study [34]. ...
... This approach mimics texture development in cellular automata models, where the preferred-growth directions correspond to the diagonals of expanding decentered octahedra [36]. A similar concept has also been used to describe texture development in a standard Potts Monte Carlo scheme adapted to simulate LPBF builds [33]. ...
... The time step associated with the Monte Carlo flips is linked to a physical time step following the procedure in [37,41], which is based on grain growth kinetics. As noted in [33], the large thermal gradients in the heat-affected zone surrounding the melt pool in LPBF cause the mobility to rapidly drop off to near-negligible values. As a result, only a limited amount of coarsening occurs in the simulations. ...
Metal additive manufacturing (AM) processes produce heterogeneous microstructures, leading to significant uncertainty in mechanical behavior. Process-induced defects cause additional uncertainty and can degrade performance, particularly for local processes like fatigue. However, time and monetary costs impose constraints on using repeated experiments to quantify this uncertainty across the process parameter space. Applying uncertainty quantification methods with computational materials simulations can help to alleviate these costs. In this paper, a physics-based process-structure–property (PSP) simulation framework is developed and applied to laser powder bed fusion AM of Ti-6Al-4V. Microstructures are generated from process-structure simulations that combine an analytical temperature field solution with a kinetic Monte Carlo-based model incorporating crystallographic texture and porosity development. The microstructures are passed to structure-property simulations comprising a fast Fourier transform-based crystal plasticity solver to predict a fatigue indicator parameter. Two uncertainty quantification problems are addressed: (1) probabilistic parameter calibration for the thermal model and (2) prediction of extreme values and the associated uncertainty of the fatigue indicator parameter using the full PSP framework. The simulations demonstrate the detrimental influence of keyhole porosity on fatigue, while also showing that pore-microstructure interaction increases uncertainty in the fatigue indicator parameter extreme values. The uncertainty quantification capabilities of the PSP framework provide a path toward using computational materials simulations to support qualification and certification of AM parts.
... The stochastic CA approach incorporates nucleation, growth, and diffusion of constituent elements and phases to predict the grain orientations originating in the melt pool. Pauza et al. [152] included the crystallographic orientation information in a Monte-Carlo Potts model for microstructure evolution in a PBF process. Phase field (PF) models, on the other hand, predict the 2D crystal growth dynamics (microstructure evolution) during solidification [62]. ...
We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.
... The Rosenthal solution further assumes a point-like heat source of power [19]. Numerous grain structure simulations models are based on this approximation [22,36,80] as it provides with a simple mathematical expression of the temperature field according to the ( , , ) coordinates attached to the heat source that moves at constant velocity on the material-gas surface, = 0, in the direction along a = 0 line: ...
... The am/ellipsoid app was extended further by Pauza, et. al. in [50,51] to incorporate crystallographic orientation effects. Their work demonstrated that by biasing the spin-flip algorithm to preferentially choose a new spin based on the alignment between fast-solidifying crystal directions and the local temperature gradient, experimentally observed textures could be reproduced for a range of conditions. ...
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.
... The methodology utilized to simulate the effect of scan rotation on microstructure evolution can be extended to study the effect of scan strategy or any other parameter that one can expect to influence the microstructure evolution. [49]. The methodology used here will be significantly useful for optimizing process parameters to obtain desired microstructure. ...
Laser powder bed fusion (LPBF) of Haynes 282 is gaining attention recently due to its superior mechanical properties than its conventional counterparts. In spite of the superior mechanical properties, there are significant challenges concerning crystallographic anisotropy. One of the critical but less studied parameters that influences crystallographic anisotropy is the laser scan rotation angle. This study investigates the possibility of controlling the microstructure and crystallographic texture by modifying the laser scan rotation angle. Further, three dimensional Finite difference-Monte Carlo simulations were performed to understand the microstructure evolution with varying process parameters.
... Nowadays, numerical modeling methods for microstructure and microstructure evolution are available, which explicitly include the variation of interface energy with the geometric degrees of freedom of the grain boundary [6,7,8,9,10]. To some extent, this variation can be captured by analytical models based on the Read-Shockley-Wolf (RSW) model [11], see e.g. ...
Many materials processes and properties depend on the anisotropy of the energy of grain boundaries, i.e. on the fact that this energy is a function of the five geometric degrees of freedom (DOF) of the grain boundaries. To access this parameter space in an efficient way and discover energy cusps in unexplored regions, a method was recently established, which combines atomistic simulations with statistical methods 10.1002/adts.202100615. This sequential sampling technique is now extended in the spirit of an active learning algorithm by adding a criterion to decide when the sampling is advanced enough to stop. To this instance, two parameters to analyse the sampling results on the fly are introduced: the number of cusps, which correspond to the most interesting and important regions of the energy landscape, and the maximum change of energy between two sequential iterations. Monitoring these two quantities provides valuable insight into how the subspaces are energetically structured. The combination of both parameters provides the necessary information to evaluate the sampling of the 2D subspaces of grain boundary plane inclinations of even non-periodic, low angle grain boundaries. With a reasonable number of datapoints in the initial design, only a few sequential iterations already influence the accuracy of the sampling substantially and the new algorithm outperforms regular high-throughput sampling.
... However, the original Rosenthal equation defines a semi-circular cross section for the melt pool, which is not completely realistic. Therefore, we have used a modified version of this equation introduced by Pauza et al. [37], which determines a wider and deeper melt pool compared to the original equation. The modified Rosenthal equation can be presented as [37]: ...
... Therefore, we have used a modified version of this equation introduced by Pauza et al. [37], which determines a wider and deeper melt pool compared to the original equation. The modified Rosenthal equation can be presented as [37]: ...
... coefficients take values between 0 and 2 [37]. We can obtain the width and depth of the melt pool for different laser powers and scanning speeds using these equations. ...
Identifying the suitable process parameters is one of the necessities to control the microstructure, and consequently various properties of additively manufactured (AM) parts. To obtain suitable process parameters, it is crucial to understand the effects of each process parameter on various aspects of microstructure of the printed material. In this work, we have conducted a parametric study on the effects of AM process parameters on the grain morphology of the metallic parts, fabricated via directed energy deposition (DED), through the Kinetic Monte Carlo (KMC) simulations. The characteristics of the melt pool and the heat-affected zone (HAZ) were incorporated into the models, and microstructural effects of altering the layer thickness, hatch spacing, scanning speed, and laser power were investigated. We used the Rosenthal equation, to predict the geometry of the melt pool and heat-affected zone formed by certain laser powers. The resulting grain morphology (i.e. the grain size and inclination) was analyzed for a large number of combinations of different process parameters. It was observed that by increasing the laser power, the number of fine and equiaxed grains decreases. On the other hand, increasing the scanning speed had a significant effect on formation of more fine grains in the final model.
... This would effectively interrupt the epitaxial growth of the prior β grains. Along with asymmetric scan rotations, instability of the melt pool and non-uniform thermal gradients [51] could lead to new prior β orientations known as stray grains [52]. Another reason for prior β grain boundary distortion is high laser scan speed which results in high temperature gradients between the edge and inner part of melt pool. ...
