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.
Highlights
Powder-bed additive manufacturing has been rapidly evolving over the last decade, due in part to the design freedom it offers [1]
There is a consensus in the literature on the applicability of the Discrete Element Method (DEM) to study the problem on additive manufacturing (AM) powder spreading [5,6,7,8,9,11,12,13,14]
Regression results from the final Back Propagation Neural Network (BP-NN) with parameters listed in Table 2 are shown in Figures 6–9 for spreading simulations over a rough substrate (Sq = 79 μm)
Summary
Powder-bed additive manufacturing has been rapidly evolving over the last decade, due in part to the design freedom it offers [1]. It is defined as the process of joining materials to make objects from. Authors of this study have previously defined powder spreadability as the ease with which a powder will spread under a set of load conditions [15] and have introduced four spread layer properties. Metals 2019, 9, 1176 which can be used to quantify spreadability—mass of powder retained in the sampling region after spreading, spread throughput, roughness and porosity of the spread layer [4]. Few studies [16,17,18] have tried to use high energy X-rays to visualize the powder spreading in situ, these studies had to make use of a thin slice of powder in direction perpendicular to the X-rays due to the low penetration depth of view offered by X-rays
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