Energy input effect on morphology and microstructure of selective laser melting single track from metallic powder

Yadroitsev, Igor; Krakhmalev, Pavel; Yadroitsava, Ina; Johansson, Sten; Smurov, I.

doi:10.1016/j.jmatprotec.2012.11.014

Cited by 405 publications

(181 citation statements)

References 14 publications

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“…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%

“…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%

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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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Section: Additive Manufacturingmentioning

confidence: 99%

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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“…The SEM images show that the width of the scan tracks was observed not to be uniform, which might be due to the rotation of the hatches to 74° between the layers. The solidification process is dominated by an epitaxial growth mechanism (Figure 3) [20]. The solidified layer acts as substrate for the solidifying melt leading to the formation of elliptical-shaped grains orientated perpendicular to the solidification front [20].…”

Section: Microstructurementioning

confidence: 99%

“…The solidification process is dominated by an epitaxial growth mechanism (Figure 3) [20]. The solidified layer acts as substrate for the solidifying melt leading to the formation of elliptical-shaped grains orientated perpendicular to the solidification front [20]. The elliptical-shaped grains grow along the same orientation as their neighbors and are separated by thin grain boundaries.…”

Section: Microstructurementioning

confidence: 99%

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Selective Laser Melting of Ti-45Nb Alloy

Schwab

Prashanth

Löber

et al. 2015

Metals

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Ti-45Nb is one of the potential alloys that can be applied for biomedical applications as implants due to its low Young's modulus. Ti-45Nb (wt.%) gas atomized powders were used to produce bulk samples by selective laser melting with three different parameter sets (energy inputs). A β-phase microstructure consisting of elliptical grains with an enriched edge of titanium was observed by scanning electron microscopy and X-ray diffraction studies. The mechanical properties of these samples were evaluated using hardness and compression tests, which suggested that the strength of the samples increases with increasing energy input within the range considered.

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Aspects of the Process and Material Relationships in the Selective Laser Melting of Aluminium Alloys

Simonelli¹,

Aboulkhair²,

Maskery³

et al. 2015

TMS2015 Supplemental Proceedings

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Energy input effect on morphology and microstructure of selective laser melting single track from metallic powder

Cited by 405 publications

References 14 publications

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

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

Selective Laser Melting of Ti-45Nb Alloy

Aspects of the Process and Material Relationships in the Selective Laser Melting of Aluminium Alloys

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