A more efficient method for calibrating discrete element method parameters for simulations of metallic powder used in additive manufacturing

Geer, S.; Bernhardt-Barry, Michelle L.; Garboczi, Edward J.; Whiting, Justin G.; Dönmez, Alkan

doi:10.1007/s10035-018-0848-4

Cited by 27 publications

(7 citation statements)

References 37 publications

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“…The simulation setup for the calibration tests faithfully reproduces the experimental one. However, to reduce the overall computational time needed to perform the calibration tests, we adopt the `cloud method’ [ 37 ] for the determination of the angle of repose: the particles are not poured through a funnel, but are generated in a loose cloud above the base and let settle until a stable angle is attained. Despite the simplification, this method has shown a good accuracy and a considerable saving of computational time.…”

Section: Calibrationmentioning

confidence: 99%

Discrete Element Method Analysis of the Spreading Mechanism and Its Influence on Powder Bed Characteristics in Additive Manufacturing

2021

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Laser powder bed fusion additive manufacturing is among the most used industrial processes, allowing for the production of customizable and geometrically complex parts at relatively low cost. Although different aspects of the powder spreading process have been investigated, questions remain on the process repeatability on the actual beam–powder bed interaction. Given the influence of the formed bed on the quality of the final part, understanding the spreading mechanism is crucial for process optimization. In this work, a Discrete Element Method (DEM) model of the spreading process is adopted to investigate the spreading process and underline the physical phenomena occurring. With parameters validated through ad hoc experiments, two spreading velocities, accounting for two different flow regimes, are simulated. The powder distribution in both the accumulation and deposition zone is investigated. Attention is placed on how density, effective layer thickness, and particle size distribution vary throughout the powder bed. The physical mechanism leading to the observed characteristics is discussed, effectively defining the window for the process parameters.

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Section: Calibrationmentioning

confidence: 99%

Discrete Element Method Analysis of the Spreading Mechanism and Its Influence on Powder Bed Characteristics in Additive Manufacturing

2021

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“…Haeri (2017) identified the optimum blade tip shape to produce a spread particle layer with volume fraction and surface roughness comparable to a roller at the actual operation conditions. Geer et al (2018) and Han et al (2019) measured the repose angle and pile bulk density of metal powders, and then used DEM simulations to calibrate and characterise the sliding and rolling friction coefficient and surface energy of the powder to be used in their simulations of the spreading process. Desai et al (2019) also developed a DEM calibration method based on angle of repose testing and powder rheometry for AM, which was designed around multiple characterization experiments applicable to the spreading step.…”

Section: Powder Spreadability Testers For Additive Manufacturingmentioning

confidence: 99%

Cohesive Powder Flow: Trends and Challenges in Characterisation and Analysis

Ghadiri

Nan

Hare

et al. 2020

KONA

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Powder processing and manufacturing operations are rate processes for which the bottleneck is cohesive powder flow. Diversity of material properties, particulate form, and sensitivity to environmental conditions, such as humidity and tribo-electric charging, make its prediction very challenging. However, this is highly desirable particularly when addressing a powder material for which only a small quantity is available. Furthermore, in a number of applications powder flow testing at low stress levels is highly desirable. Characterisation of bulk powder failure for flow initiation (quasi-static) is well established. However, bulk flow parameters are all sensitive to strain rate with which the powder is sheared, but in contrast to quasi-static test methods, there is no shear cell for characterisation of the bulk parameters in the dynamic regime. There are only a handful of instruments available for powder rheometry, in which the bulk resistance to motion can be quantified as a function of the shear strain rate, but the challenge is relating the bulk behaviour to the physical and mechanical properties of constituting particles. A critique of the current state of the art in characterisation and analysis of cohesive powder flow is presented, addressing the effects of cohesion, strain rate, fluid medium drag and particle shape.

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“…(1) Experimental measurement of the angle of repose The funnel method [37][38][39][40] and the hollow cylinder method (collapse test method) [41][42][43] are commonly used to measure the angle of repose of particles. In this study, the principle of the collapse test method was applied in the experimental device, shown in Figure 5.…”

Section: Interaction Parameters Between Proppantsmentioning

confidence: 99%

Calibration of the Interaction Parameters between the Proppant and Fracture Wall and the Effects of These Parameters on Proppant Distribution

Liu

Zhang

2020

Energies

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Saltation and reputation (creep) dominate proppant transport rather than suspension during slickwater fracturing, due to the low sand-carrying capacity of the slickwater. Thus, the interaction parameters between proppants and fracture walls, which affect saltation and reputation, play a more critical role in proppant transport. In this paper, a calibration method for the interaction parameters between proppants and walls is built. A three-dimensional coupled computational fluid dynamics-discrete element method (CFD-DEM) model is established to study the effects of the interaction parameters on proppant migration, considering the wall roughness and unevenly distributed diameters of proppants. The simulation results show that a lower static friction coefficient and rolling friction coefficient can result in a smaller equilibrium height of the sand bank and a smaller build angle and drawdown angle, which is beneficial for carrying the proppant to the distal end of the fracture. The wall roughness and the unevenly distributed diameter of the proppants increase the collision between proppant and proppant or the wall, whereas the interactions have little impact on the sandbank morphology, slightly increasing the equilibrium height of the sandbank. low-viscosity fluid. Moreover, experiments on proppant transport in fracture networks [10,12,13] were mostly carried out to answer the debatable question of whether slickwater can transport proppants into secondary fractures or not. In addition to experimental research, computational fluid dynamics (CFD) methods were widely used to study proppant transport in fractures. The most common approach for modeling proppant transport is the Eulerian-Eulerian method in which the interaction between the particles and fluid is fully coupled [14][15][16][17][18][19]. In a recent study, Han et al. [20] used the Eulerian granular model to track proppant movement in various fractures for a better understanding of how proppant transport is influenced by fracture viscosity, proppant density, and pump rate. The second method is the Eulerian-Lagrangian model, which tracks individual particle movement with time and location with the Lagrangian framework and describes fluid flow with the Eulerian framework [21][22][23]. The discrete element method (DEM) [24] is a useful tool to simulate the behavior of particles, and it can be coupled with CFD to model the interaction between particles and the surrounding fluid. In recent years, the coupled CFD-DEM model was used to study fracturing fluid flow in fractures for its advantages of considering proppant-proppant, proppant-boundary, and proppant-fracturing fluid interactions [25][26][27][28][29][30][31][32]. Blyton et al. [26] first used the coupled CFD-DEM model to simulate the motion of particles flowing with a fluid between fracture walls, and the simulations individually determined that particle trajectories such as particle-to-particle and particle-to-wall collisions occur. Zhang et al. [28] used a two-dimensional coupled CFD-DEM model to study ...

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A more efficient method for calibrating discrete element method parameters for simulations of metallic powder used in additive manufacturing

Cited by 27 publications

References 37 publications

Discrete Element Method Analysis of the Spreading Mechanism and Its Influence on Powder Bed Characteristics in Additive Manufacturing

Discrete Element Method Analysis of the Spreading Mechanism and Its Influence on Powder Bed Characteristics in Additive Manufacturing

Cohesive Powder Flow: Trends and Challenges in Characterisation and Analysis

Calibration of the Interaction Parameters between the Proppant and Fracture Wall and the Effects of These Parameters on Proppant Distribution

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