Defect generation and propagation mechanism during additive manufacturing by selective beam melting

Bauereiß, A.; Scharowsky, T.; Körner, Carolin

doi:10.1016/j.jmatprotec.2014.05.002

Cited by 294 publications

(133 citation statements)

References 19 publications

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“…We use numerical simulation in order to understand the correlation between scanning strategy, resulting solidification conditions and microstructure. The numerical model (based on the Lattice Boltzmann Method) which we have developed for simulation of powder bed based additive manufacturing processes has been described in several publications [8][9][10][11][12][13][14][15]. The beam melting process is simulated on the powder scale, i.e.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Tailoring the grain structure of IN718 during selective electron beam melting

Körner

Helmer

Bauereiß

et al. 2014

MATEC Web of Conferences

115

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Abstract. Selective electron beam melting (SEBM) is an additive manufacturing method where complex parts are built from metal powders in layers of about 50 µm. SEBM works under vacuum conditions which results in a perfect protection of the metal alloy. The electron beam is used for heating (about 900 • C building temperature) and selective melting. The high beam velocities allow innovative scanning strategies in order to adapt the local solidification conditions which determine the epitaxial solidification process of IN718. We show how scanning strategies can be used either to produce a columnar grain structure with a high texture in building direction or a complete texture-free fine grained structure. Numerical simulations of the selective melting process are applied to reveal the fundamental mechanisms responsible for the completely different grain structures. In addition the influence of the different grain structures on the mechanical properties of IN718 is briefly discussed.

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Section: Numerical Simulationsmentioning

confidence: 99%

Tailoring the grain structure of IN718 during selective electron beam melting

Körner

Helmer

Bauereiß

et al. 2014

MATEC Web of Conferences

115

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“…Horizontal trusses, on the other hand, show irregular pores of elongated shape, see Figure 8b. As noted before [9], these appear to be due to poor adhesion when melting over loose particles, creating a balling effect and impeding the creation of homogeneous solid sections. Vertical trusses, as shown in Figure 8c, tend to have a better heat dissipation and better powder particle adhesion when melted, thus showing spherical pores only, which presumably originate from the atomization of powder feedstock.…”

Section: Discussionmentioning

confidence: 84%

“…Previous investigations [9] have discussed the mechanisms of formation of high aspect ratio defects propagating across several layers of added material in the form of "tunnel-like" pores. It was reported that coalescence of particles when heated by the beam takes place immediately, before they are fully melted.…”

Section: Discussionmentioning

confidence: 99%

“…Traditionally, the SEBM process involves preheating stages (termed preheat I and II), so that when the melting step takes place, the powder particles should display a gradual liquid-solid transformation so that they are easily incorporated into the solid body, minimising lack-of-fusion defects. In practice, however, this is not usually the case, as factors affecting the adhesion of particles may appear (for example wetting and oxidation effects [8]) resulting in the formation of pores and tunnels [9,10].…”

Section: Introductionmentioning

confidence: 99%

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X-ray Tomography Characterisation of Lattice Structures Processed by Selective Electron Beam Melting

Hernández-Nava

Tammas‐Williams

Léonard

et al. 2017

Metals

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Metallic lattice structures intentionally contain open porosity; however, they can also contain unwanted closed porosity within the structural members. The entrained porosity and defects within three different geometries of Ti-6Al-4V lattices, fabricated by Selective Electron Beam Melting (SEBM), is assessed from X-ray computed tomography (CT) scans. The results suggest that horizontal struts that are built upon loose powder show particularly high (~20 × 10 −3 vol %) levels of pores, as do nodes at which many (in our case 24) struts meet. On the other hand, for struts more closely aligned (0 • to 54 • ) to the build direction, the fraction of porosity appears to be much lower (~0.17 × 10 −3 %) arising mainly from pores contained within the original atomised powder particles.

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“…Compared with the powder induced porosity, the process induced one has received more research attention since it can be eliminated by tuning the process parameters properly. The high viscosity [31] and the solubility decrease [32] of molten powder materials, improper laser scanning strategies [21], laser power, speed [33], spot diameter, powder layer thickness, preheat temperature [23], hatching spacing [24] would detrimentally cause porosity since low energy input would decrease the area of melt pool and lead to incomplete consolidation [25]. The porosity will continuously have influence on surface roughness and finally geometrical accuracy [20,31,[34][35][36].…”

Section: Additive Manufacturingmentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive ManufacturingJingran Li GENERAL AUDIENCE ABSTRACT This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles. iv ACKNOWLEDGEMENTS

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Defect generation and propagation mechanism during additive manufacturing by selective beam melting

Cited by 294 publications

References 19 publications

Tailoring the grain structure of IN718 during selective electron beam melting

Tailoring the grain structure of IN718 during selective electron beam melting

X-ray Tomography Characterisation of Lattice Structures Processed by Selective Electron Beam Melting

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

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