Discrete element modeling of particle-based additive manufacturing processes

Abstract

A critical element for the design, characterization, and certification of materials and products produced by additive manufacturing processes is the ability to accurately and efficiently model the associated materials and processes. This is necessary for tailoring these processes to endow the associated products with proper geometrical and functional features. In an effort to address these needs in a computationally elegant and at the same time physically realistic manner, this paper presents the development of a methodology for simulating particle-based additive manufacturing processes which employs the Discrete Element Method (DEM). The details of the DEM-based methodology are presented first and the approach is demonstrated on a pair of test problems involving laser sintering of metal powders. The paper concludes with a discussion on how this approach may be generalized to broader classes of additive manufacturing systems, and details are given regarding future work which must be accomplished in order to further develop the present methodology.

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

“…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 …”

“…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].…”

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

“…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.…”

“…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].…”

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

“…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]:…”