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

Abstract

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.

Influences of powder characteristics and recoating conditions on surface morphology of powder bed in metal additive manufacturing

“…Moreover, Vock et al [9] reviewed the influence of the powder properties on the part properties considering the powder properties and bulk powder behavior during recoating. Although the investigation of the relationship between the powder properties and the powder behavior during recoating is important, there are few reports on such experiments [10], excepting those that consider numerical simulations [10][11][12][13][14][15][16][17][18]. In addition, the various process parameters of the powder properties, surface morphology of the powder bed, and laser radiation conditions are essentially correlated with the final product properties.…”

“…Numerous studies have been conducted to investigate the recoating behavior using the discrete element method (DEM) in PBF [11][12][13][14][15][16][17][18]. The relationship among the surface roughness, the recoating speed, and the powder size distribution has been investigated via DEM simulation [13][14][15][16].…”

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

“…A numerical model was developed to analyze the effect of the layer thickness on the powder bed density and the optimum parameters were used to produce samples whose microstructure and tensile strength were assessed. Desai et al [ 21 ] employed an interesting approach to overcome the limitation of the high computational power required by DEM method by using the DEM-based simulations to train a feed forward, back propagation neural network which was then used to study the relationship between spreading parameters and process results.…”

Computation of Hydrodynamic and Capillary Phenomena in Binder Jet Three-Dimensional Printing

“…The majority of computational modeling as it relates to BJ3DP has been deployed for investigating particle dynamics in the powder spreading stage with the discrete element method (DEM) [17][18][19][20][21]. Direct numerical simulation of fluid-particle interaction in BJ3DP using a coupled computational fluid dynamics (CFD) and DEM approach has currently not been published.…”