Reducing porosity in AlSi10Mg parts processed by selective laser melting

Aboulkhair, Nesma T.; Everitt, Nicola M.; Ashcroft, Ian; Tuck, Christopher

doi:10.1016/j.addma.2014.08.001

Cited by 772 publications

(558 citation statements)

References 13 publications

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“…Thijs et al [116] suggested that the large pores with the dimensions of 100∼200 µm result mostly from (a) the accumulations of the powder denudation within the melt pools within a layer, and (b) the surface roughness across the layers. The balling phenomenon (i.e., the melt pool is solidified into discontinuous balls due to poor wettability) can also introduce a large number of irregular pores enclosing non-molten powder and worse surface roughness [117][118][119].…”

Section: Porositymentioning

confidence: 99%

Additively Manufactured Inconel 718 : Microstructures and Mechanical Properties

Deng

2018

Linköping Studies in Science and Technology. Licentiate Thesis

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Linköping 2018Cover image: A fracture surface of EBM IN718 after tensile test at room temperature.During the course of research underlying this thesis, Dunyong Deng was enrolled in Agora Materiae, a multidiciplinary doctoral program at Linköping University, Sweden. Printed by LiU-Tryck 2018 © Dunyong Deng AbstractAdditive manufacturing (AM), also known as 3D printing, has gained significant interest in aerospace, energy, automotive and medical industries due to its capabilities of manufacturing components that are either prohibitively costly or impossible to manufacture by conventional processes. Among the various additive manufacturing processes for metallic components, electron beam melting (EBM) and selective laser melting (SLM) are two of the most widely used powder bed based processes, and have shown great potential for manufacturing high-end critical components, such as turbine blades and customized medical implants. The futures of the EBM and SLM are doubtlessly promising, but to fully realize their potentials there are still many challenges to overcome.Inconel 718 (IN718) is a nickel-base superalloy and has impressive combination of good mechanical properties and low cost. Though IN718 is being mostly used as a turbine disk material now, the initial introduction of IN718 was to overcome the poor weldability of superalloys in 1960s, since sluggish precipitation of strengthening phases γ ′ /γ ′′ enables good resistance to strain-age cracking during welding or post weld heat treatment. Given the similarity between AM and welding processes, IN718 has been widely applied to the metallic AM field to facilitate the understandings of process-microstructure-property relationships.The work presented in this licentiate thesis aims to better understand microstructures and mechanical properties EBM and SLM IN718, which have not been systematically investigated. Microstructures of EBM and SLM IN718 have been characterized with scanning electron microscopy (SEM), transmission electron microscopy (TEM) and correlated with the process conditions. Monotonic mechanical properties (e.g., Vickers microhardness and tensile properties) have also been measured and rationalized with regards to the microstructure evolutions before and after heat treatments.For EBM IN718, the results show the microstructure is not homogeneous but dependant on the location in the components, and the anisotropic mechanical properties are probably attributed to alignment of porosities rather than texture.iii Post heat treatment can slightly increase the mechanical strength compared to the as-manufactured condition but does not alter the anisotropy. SLM IN718 shows significantly different microstructure and mechanical properties to EBM IN718. The as-manufactured SLM IN718 has very fine dendritic microstructure and Laves phases in the interdendrites, and is "work-hardened" by the residual strains and dislocations present in the material. Mechanical properties are different between horizontally and vertically built samples, and heat treatment can m...

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Section: Porositymentioning

confidence: 99%

Additively Manufactured Inconel 718 : Microstructures and Mechanical Properties

Deng

2018

Linköping Studies in Science and Technology. Licentiate Thesis

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“…The correct choice of laser scan strategy is critical in generating the desired microstructure [10,11] and is also known to affect the build-up of residual stress in components [12]. Previous studies have observed that the largest planar residual stress component is generated parallel to the scan vector and increases with scan vector length [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation

Parry

Ashcroft

Wildman

2016

Additive Manufacturing

421

287

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract: Selective laser melting (SLM) is an attractive technology, enabling the manufacture of customised, complex metallic designs, with minimal wastage. However, uptake by industry is currently impeded by several technical barriers, such as the control of residual stress, which have a detrimental effect on the manufacturability and integrity of a component. Indirectly, these impose severe design restrictions and reduce the reliability of components, driving up costs. This paper uses a thermo-mechanical model to better understand the effect of laser scan strategy on the generation of residual stress in SLM. A complex interaction between transient thermal history and the build-up of residual stress has been observed in the two laser scan strategies investigated. The temperature gradient mechanism was discovered for the creation of residual stress. The greatest stress component was found to develop parallel to the scan vectors, creating an anisotropic stress distribution in the part. The stress distribution varied between laser scan strategies and the cause has been determined by observing the thermal history during scanning. Using this, proposals are suggested for designing laser scan strategies used in SLM. Abstract:Selective laser melting (SLM) is an attractive technology, enabling the manufacture of customised, complex metallic designs, with minimal wastage. However, uptake by industry is currently impeded by several technical barriers, such as the control of residual stress, which have a detrimental effect on the manufacturability and integrity of a component. Indirectly, these impose severe design restrictions and reduce the reliability of components, driving up costs. This paper uses a thermomechanical model to better understand the effect of laser scan strategy on the generation of residual stress in SLM. A complex interaction between transient thermal history and the build-up of residual stress has been observed in the two laser scan strategies investigated. The temperature gradient mechanism was discovered for the creation of residual stress. The greatest stress component was found to develop parallel to the scan vectors, creating an anisotropic stress distribution in the part. The stress distribution varied between laser scan strategies and the cause has been determined by observing the thermal history during scanning. Using this, proposals are suggested for designing laser scan strategies used in SLM. Nomenclature:Specific

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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%

“…As illustrated in the process mapping flowchart in [20], the significant process inputs include AM machine settings, part geometrical designs, build environment factors, and feedstock qualities. There have been numerous researches on the influence of AM machine settings like laser printing strategies [21], laser power, speed, spot diameter [22], powder layer thickness, preheat temperature [23], and hatching spacing [24] [25] on part quality as summarized in [26].…”

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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Reducing porosity in AlSi10Mg parts processed by selective laser melting

Cited by 772 publications

References 13 publications

Additively Manufactured Inconel 718 : Microstructures and Mechanical Properties

Additively Manufactured Inconel 718 : Microstructures and Mechanical Properties

Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation

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

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