Adaptability investigations on bottom modified blade in powder spreading process of additive manufacturing

Abstract Binder jetting, also known as 3D printing, is an additive manufacturing (AM) technology utilizing a liquid-based binding agent to selectively join the material in a powder bed. It is capable of manufacturing complex-shaped parts with a variety of materials. This paper provides an overview of binder jetting of metals with a discussion about the knowledge gaps and research opportunities. The review deals with two parameter categories in terms of the material and process and their impacts. The achieved density, dimensional accuracy, and mechanical strength are summarized and analyzed. Further in-depth consideration of densification is discussed corresponding to various attributes of the packing, printing, and sintering behaviors. Though binder jetting has attracted increasing attention in the past several years, this fabrication process is not well studied. The understanding of powder spreading process and binder-powder interaction is crucial to the development of binder jetting but insufficient. In addition, the lack of investigation on the mechanical behavior of binder jetting metal part restricts the actualization of its wide-range applications.

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Spreading of Powders in Powder Bed Additive Manufacturing: an Experimental Approach

10.25518/esaform21.2433 ◽

2021 ◽

Author(s):

Valerio Lampitella ◽

Marco Trofa ◽

Antonello Astarita ◽

Gaetano D’Avino

Keyword(s):

Mechanical Properties ◽

Additive Manufacturing ◽

Process Parameters ◽

Experimental Approach ◽

Physical Mechanism ◽

Industrial Applications ◽

Powder Bed Fusion ◽

Spreading Process ◽

Powder Bed ◽

Optimal Window

Powder bed additive manufacturing allows for the production of fully customizable parts and is of great interest for industrial applications. However, the repeatability of the parts and the uniformity of the mechanical properties are still an issue. More specifically, the physical mechanism of the spreading process of the powders, which significantly affects the characteristics of the final part, is not completely understood. In powder bed fusion technologies, the spreading is performed by a device, typically a roller or a blade, that collects the powders from the feedstock and successively deposits them in a layer of several dozens of microns that is then processed with a laser beam. In this work, an experimental approach is developed and employed to study the powder spreading process and analyze in detail the motion of the powders from the accumulation zone to the deposition stage. The presented experiments are carried out on a home-made device that reproduces the spreading process and enables the measurement of the characteristics of the powder bed. Furthermore, the correlation with the process parameters, e.g., the speed of the spreading device, is also investigated. These results can be used to obtain useful insights on the optimal window for the process parameters.

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

Micromachines ◽

10.3390/mi12040392 ◽

2021 ◽

Vol 12 (4) ◽

pp. 392

Author(s):

Valerio Lampitella ◽

Marco Trofa ◽

Antonello Astarita ◽

Gaetano D’Avino

Keyword(s):

Additive Manufacturing ◽

Discrete Element Method ◽

Ad Hoc ◽

Low Cost ◽

Discrete Element ◽

Spreading Process ◽

Powder Bed ◽

Physical Phenomena ◽

Element Method

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

Metals ◽

10.3390/met9111176 ◽

2019 ◽

Vol 9 (11) ◽

pp. 1176 ◽

Cited By ~ 2

Author(s):

Prathamesh S. Desai ◽

C. Fred Higgs

Keyword(s):

Neural Network ◽

Machine Learning ◽

Additive Manufacturing ◽

Spreading Process ◽

Powder Bed ◽

Dem Simulations ◽

Model Based ◽

Highly Nonlinear ◽

Physics Model ◽

The Relationship

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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Binder Jetting Additive Manufacturing of Ceramics: Analytical and Numerical Models for Powder Spreading Process

Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing ◽

10.1115/msec2019-2925 ◽

2019 ◽

Cited By ~ 1

Author(s):

Guanxiong Miao ◽

Wenchao Du ◽

Zhijian Pei ◽

Chao Ma

Keyword(s):

Additive Manufacturing ◽

Numerical Models ◽

Ceramic Materials ◽

Low Density ◽

Forming Process ◽

Spreading Process ◽

Powder Bed ◽

Complex Shapes ◽

Part Density ◽

Binder Jetting

Abstract Binder jetting additive manufacturing is a promising way to process ceramic materials which are hard to be manufactured into complex shapes using conventional methods. However, the application of binder jetting is limited by the relatively low density of manufactured parts. Powder bed forming process is a critical step that determines the powder bed density and consequently the part density. Thus, investigating and understanding the power spreading process is necessary to improve the part density. A numerical model is developed to predict the powder bed density under different spreading conditions using the discrete element method (DEM). The predicted DEM results are compared with the prediction of an analytical model. The results show that under different layer thicknesses (50 μm, 70 μm, 100 μm) and roller diameters (12 mm, 14 mm, and 16 mm), the predicted maximum powder bed density by these two models has nearly the same value and the predicted maximum packing stress has the same trend.

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