Discrete element modeling of particle-based additive manufacturing processes

Steuben, John C.; Iliopoulos, Athanasios; Michopoulos, John G.

doi:10.1016/j.cma.2016.02.023

Cited by 96 publications

(53 citation statements)

References 72 publications

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“…The slicer intersects the CAD model with a series of planes in order to define a set of cross sections. For each plane, both perimeter and infill laser beam trajectories are computed to produce a layer [16]. In the present work, the …”

Section: G-code Interpreter Modulementioning

confidence: 99%

“…For approaches at the powder particles scale, Steuben et al [9] present the development of a methodology with the discrete element method extended to incorporate the physics of laser heating, leading to a computationally efficient approach to simulate SLS of metals at relatively large scale. A 3D mesoscopic powder model has also been developed using a hybrid finite element (FE) and finite volume formulation on unstructured meshes by Khairallah and Anderson [10].…”

Section: Introductionmentioning

confidence: 99%

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Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Zhang

Guillemot

Bernacki

et al. 2018

Computer Methods in Applied Mechanics and Engineering

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A 3D finite element model is developed to study heat exchange during metal selective laser melting (SLM). The approach is conducted on the scale of the part to be formed, using a level set framework to track the interface between the constructed workpiece and non-melted powder, and interface between the gas domain and the successive powder bed layers. In order to keep sustainable the computational efficiency, the powder bed deposition and the energy input are simplified by the scale of an entire layer or fractions of each layer. Layer fractions are identified directly from a description of the global laser scan plan of the part to be built. Each fraction is heated during a time interval corresponding to the exposure time to the laser beam, and then cooled down during a time interval equal to the scan time for the considered layer fraction. The global heat transfer through the part under additive construction and through the powder material non-exposed to the laser beam is simulated. To reduce the computational cost, a refining and de-refining mesh adaptation is carried out with a conform mesh strategy. Mesh sensitivity tests and validation of energy conservation are discussed. The proposed model is able to predict the temperature distribution and evolution in the constructed workpiece and non-melted powder during the SLM process at the macroscale, for parts of complex geometry.Application is shown for a nickel based alloy (IN718), but the numerical model can be easily extended to other materials by using their data sets.

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Section: G-code Interpreter Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Zhang

Guillemot

Bernacki

et al. 2018

Computer Methods in Applied Mechanics and Engineering

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“…Snow et al [3] have developed a similar spreadability metric comprising of the percent build plate coverage, the powder deposition rate and the rate of change of the avalanche angle. There is a consensus in the literature on the applicability of the Discrete Element Method (DEM) to study the problem on AM powder spreading [5][6][7][8][9][11][12][13][14]. DEM was used by Haeri in 2017 [10] to optimize the shape of blade-like spreaders to minimize spread layer defects.…”

Section: Powder Spreading Studiesmentioning

confidence: 99%

“…State of the art 3D printed parts have microscopic defects, at layer-level, like porosity, roughness and over or under fused/bound particles and macroscopic, at part level, defects like poor surface finish, voids, dimensional inaccuracy and shear-induced deformation. Few of these defects are directly related to the spreading step in powder-bed additive manufacturing (AM) [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning

Desai

Higgs

2019

Metals

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The powder bed additive manufacturing (AM) process is comprised of two repetitive steps—spreading of powder and selective fusing or binding the spread layer. The spreading step consists of a rolling and sliding spreader which imposes a shear flow and normal stress on an AM powder between itself and an additively manufactured substrate. Improper spreading can result in parts with a rough exterior and porous interior. Thus it is necessary to develop predictive capabilities for this spreading step. A rheometry-calibrated model based on the polydispersed discrete element method (DEM) and validated for single layer spreading was applied to study the relationship between spreader speeds and spread layer properties of an industrial grade Ti-6Al-4V powder. The spread layer properties used to quantify spreadability of the AM powder, i.e., the ease with which an AM powder spreads under a set of load conditions, include mass of powder retained in the sampling region after spreading, spread throughput, roughness of the spread layer and porosity of the spread layer. Since the physics-based DEM simulations are computationally expensive, physics model-based machine learning, in the form of a feed forward, back propagation neural network, was employed to interpolate between the highly nonlinear results obtained by running modest numbers of DEM simulations. The minimum accuracy of the trained neural network was 96%. A spreading process map was generated to concisely present the relationship between spreader speeds and spreadability parameters.

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“…However, lower-order schemes are shown to be faster than more complex, high-order integration schemes [29]. Therefore, in this work we use an Euler explicit integration method to update the particle temperatures at each time step [30]:…”

Section: Applied Mechanics and Materials Vol 869 75mentioning

confidence: 99%

An Adaptive Discrete Element Method for Physical Modeling of the Selective Laser Sintering Process

Gobal

Ravani

2017

AMM

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Abstract. Selective Laser Sintering (SLS) has recently become one of the fastest growing additive manufacturing processes due to its capability of fabricating metal parts with high dimensional accuracy and surface quality. Physical modeling of selective laser sintering plays an important role in properly controlling the process parameters to achieve desired product properties. In this paper, we present a 3 dimensional, adaptive discrete element method for simulation of the SLS process on personal computers. The presented method models the laser-powder interaction at particle level, achieving high simulation accuracy while adaptively increasing the discrete element size as local temperatures drop inside the powder bed for improved efficiency. Numerical shape functions are developed for calculating individual particle temperatures at any point during the simulation. Results show that this physical model improves the runtime significantly in virtual simulation of SLS process without loss of simulation accuracy .

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Discrete element modeling of particle-based additive manufacturing processes

Cited by 96 publications

References 72 publications

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning

An Adaptive Discrete Element Method for Physical Modeling of the Selective Laser Sintering Process

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