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A Digital-Based Approach for Characterising Spread Powder Layer in Additive Manufacturing

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A Digital-Based Approach for Characterising Spread Powder Layer in Additive Manufacturing Materials and Design 196 (2020) 109102 Contents lists available at ScienceDirect Materials and Design journal homepage: www.elsevier.com/locate/matdes A digital-based approach for characterising spread powder layer in additive manufacturing Yi He ⁎, Jabbar Gardy, Ali Hassanpour, Andrew E. Bayly School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom HIGHLIGHTS GRAPHICAL ABSTRACT • A general approach to characterise spread powder layer based on space discretization. • Qualitative and quantitative evaluations enabled for packing density, surface profile and pores. • Density pore for less populated area and chamber pore for size of empty patch. • Sensitivity tests conducted on sampling parameters. • Applicability demonstrated for simula- tion-generated and experimentally spread powder layers. article info abstract Article history: Assessing the quality of a spread powder layer is critical to understanding powder spreadability in additive Received 19 July 2020 manufacturing. However, the small layer thickness presents a great challenge for a systematic and consistent Received in revised form 21 August 2020 characterisation of the spread powder layer. In this study, a novel digital-based characterisation approach is pro- Accepted 24 August 2020 posed based on space discretization, with an emphasis on the characteristics that is important to powder-bed- Available online 27 August 2020 based additive manufacturing. With the developed approach, the spread powder layer can be qualitatively illus- trated by contour maps and quantified by statistics of packing density, surface profile and pore characteristics. For Keywords: fi Powder spreading the rst time, two types of pores are proposed for the spread powder layer. The density pore can identify those Additive manufacturing less populated areas while the chamber pore is able to quantify the size of empty patches observed in the spread Discrete element method powder bed. Applicability of this approach is demonstrated via both simulation-generated and experimentally Chamber pore spread powder layers. Sensitivity tests on the sampling parameters are conducted. This digital-based character- Density pore isation method is general and can be applied to both polydisperse and non-spherical particle systems, not only enriching detailed structural analysis of the spread powder layer but also allowing us to quantitatively evaluate powder spreadability in additive manufacturing. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). 1. Introduction In powder-bed-based AM processes, material deposition is often achieved via powder spreading (or recoating) using a blade, rake or ro- Additive manufacturing (AM) has shown its promise in fast tating roller. A thin layer of powder is spread onto a substrate prior to prototyping of near-net-shape structures in many applications, such material consolidation in selective regions using either a high intensity as aerospace, automotive, medical and energy storage industries [1,2]. energy source (e.g. laser, electron beam or plasma) or a binder [3,4]. The structural, mechanical, and thermal properties of final fabricated ⁎ Corresponding author. parts are found to be strongly correlated with the quality of spreading E-mail addresses: [email protected] (Y. He), [email protected] (A.E. Bayly). [4–7]. Considerable efforts have thus been made to develop a better https://doi.org/10.1016/j.matdes.2020.109102 0264-1275/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Y. He, J. Gardy, A. Hassanpour et al. Materials and Design 196 (2020) 109102 understanding of the factors affecting the homogeneity of a spread thin spread powder layer due to variations of surface profiles across powder layer via both modelling [8–15] and experiments [16–18]. the spread powder layer. Recently, Nan et al. [8] presented a method However, a systematic approach to assess the quality of a spread pow- to counting the appearance of empty patches in a numerical sample ob- der layer is still lacking, making it difficult to achieve quantitative un- tained by DEM simulation, where the spread powder layer was divided derstanding and hence process optimisation of powder spreading. into several consecutive bins. An empty patch is identified when the Characterisation of an assembly of particulate material can be made packing density of a bin is smaller than a threshold value (i.e. 0.1). It either from a macroscopic viewpoint or using local structural descrip- should be noted here, however, that although their method captures tors. Packing density (or porosity) is an important global quantity the existence of the local empty patches, it lacks the quantitative infor- which relates closely to local packing structure. Many empirical or mation on the pore characteristics, for example, size, shape, and semi-empirical models have therefore been developed to describe the orientation of these pores. relationship between packing density and particle characteristics In this study, a digital-based characterisation approach is developed [19,20]. In recent years, experimental techniques [21,22] and numerical by discretising working space into voxels, which provides a basis for simulations have been increasingly used to reveal packing structure and extracting both qualitative and quantitative metrics from a spread pow- consequently leading to the development of statistical descriptions of der layer. For the first time, two types of pores of relevance to additive local parameters, such as the radial and angular distribution functions manufacturing are proposed, with their calculation procedures detailed. [23], orientational orders [22], coordination number and neighbour The so-called density pore allows less populated regions to be identified number [20]. More detailed structural information can be obtained by and characterised in terms of size, shape, and orientation while the geometrical tessellations, such as Voronoi tessellation [24,25]ornaviga- chamber pore quantitatively evaluate the size of empty patches tion map (an extension of Voronoi tessellation to polydisperse systems) observed in a spread powder layer. The applicability of this approach [26]. Particularly, Voronoi tessellation can effectively characterise both is first demonstrated on a simulation-simulated spread powder layers. particle and pore networks, allowing us to model transport properties Sensitivity tests are then carried out, aiming to provide guidance on or to build-up statistical mechanics theory for particle packing [27]. the selection of sampling parameters. Combined with imaging analysis, These approaches have been successfully applied to systems consisting this approach is also demonstrated to be useful for extracting structural of mono-sized [20] and polydisperse [28], spherical and non-spherical information from an experimentally spread powder layer. The rest of [25], coarse and fine [23,29], friction and frictionless [28] particles. the paper is structured as follows: a detailed description of the digital- However, these methods could not be applied to a spread powder based approach is first presented in Section 2. This is followed by a layer without modification, as the small layer thickness may introduce brief description of DEM approach and simulation conditions in large uncertainties to the calculation of local cell volumes. Moreover, Section 3. In Section 4, sensitivity tests are carried out on a DEM- at present it remains difficult to accurately capture the positions of simulated spread powder layer. Application of the proposed method fine particles within a spread powder layer using the conventional ex- to experimentally spread powder layer is shown in Section 5. perimental methods. For example, the commonly used optical imaging systems may only able to provide reliable data on a single-layered pow- 2. A digital-based characterisation method der layer, although no results have been reported yet. X-ray tomogra- phy is a possible solution for multi-layered spread powder bed but an The proposed characterisation method is based on space discre- in-situ spreading rig is required. With the rapid increase of computa- tisation, which allows the quality of a spread powder layer to be tional powder, discrete element modelling (DEM) has become an indis- assessed in terms of packing density, surface roughness and pore char- pensable alternative, as it provides detailed information at particle scale. acteristics. For a given spread powder layer (either from simulations or fl It has been applied to study the in uence of material properties, such as experiments), the working space is first discretised into voxels, with particle size [9], shape [13] and cohesion [10,14], and operational condi- voxel size denoted as Δvoxel. Voxels located within particles are then la- tions, such as blade clearance [10,30], spreading speed [10,12], coater belled as solid voxels (vs) while those outside of particles are labelled as type [11–13,30,31] and gas-particle interaction [32,33] on the quality void voxels (ve). of spread layer. However, to date, characterisations of a spread powder fi layer are largely limited to the packing density and surface pro les, 2.1. Packing density and surface roughness which limits the quantitative information that can be extracted. To the best of our knowledge, detailed pore characteristics
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