Optimization of Laser Powder Bed Fusion Processing Using a Combination of Melt Pool Modeling and Design of Experiment Approaches: Density Control

Letenneur, Morgan; Kreitcberg, Alena; Braïlovski, Vladimir

doi:10.3390/jmmp3010021

Cited by 53 publications

(54 citation statements)

References 31 publications

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“…For these validations studies, the CR, d xz and GAR processing maps were calculated using the previously presented algorithm (Equations (1)-(6)), and the physical properties of IN625 bulk alloy and powder collected in Table 4 [27]. Note that for the first stage of validation (cooling rate), bulk material properties were used, since experimental data were obtained for a single laser melt track created on an IN625 plate.…”

Section: Validation Strategy and Resultsmentioning

confidence: 99%

“…The LPBF melt pool temperature distribution calculations were carried out using the analytical model of a semi-infinite solid with a moving Gaussian heat source [29]. This model has been successfully used for density prediction in specimens manufactured from Fe [25], Ti-Zr-Nb [26], AlSiMg, IN625, Ti64 and 316L alloy powders [27]. The Gaussian model involves a symmetrical distribution of laser irradiance across the beam.…”

Section: Cooling Rate (Cr) and Thermal Gradient (Tg) Calculationsmentioning

confidence: 99%

“…Firstly, to establish the relationship between the LPBF processing parameters and the printed material density, the algorithm developed in [27] was used to build a so-called "density processing map" (Figure 4a) in the laser energy density (E, J/mm 3 ) (Equation (9))-material build rate (BR, cm 3 /h) (Equation (10)) coordinates:…”

Section: Solidification Cooling Rate and Thermal Gradient-microstructmentioning

confidence: 99%

See 2 more Smart Citations

The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment

Letenneur

Kreitcberg

Braïlovski

2020

JMMP

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The additive manufacturing (AM) process induces high uncertainty in the mechanical properties of 3D-printed parts, which represents one of the main barriers for a wider AM processes adoption. To address this problem, a new time-efficient microstructure prediction algorithm was proposed in this study for the laser powder bed fusion (LPBF) process. Based on a combination of the melt pool modeling and the design of experiment approaches, this algorithm was used to predict the microstructure (grain size/aspect ratio) of materials processed by an EOS M280 LPBF system, including Iron and IN625 alloys. This approach was successfully validated using experimental and literature data, thus demonstrating its potential efficiency for the optimization of different LPBF powders and systems.

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Section: Validation Strategy and Resultsmentioning

confidence: 99%

Section: Cooling Rate (Cr) and Thermal Gradient (Tg) Calculationsmentioning

confidence: 99%

Section: Solidification Cooling Rate and Thermal Gradient-microstructmentioning

confidence: 99%

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The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment

Letenneur

Kreitcberg

Braïlovski

2020

JMMP

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“…The orthogonal experiment in this study was carried out to find the optimized process parameters with a relative density higher than 99%. Letenneur et al [ 20 ] used a set of density calibration artifacts built with laser power values from 160 to 350 W; the scanning speed from 500 to 2800 mm/s; the hatching space from 30 to 550 μm, and the layer thickness from 30 to 60 μm. The model was adapted for the IN625 alloy powder and an M280 EOS system.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Parameters for Additively Produced Tool Steel 1.2709 with a Layer Thickness of 100 μm

et al. 2021

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The purpose of this study was to find and optimize the process parameters of producing tool steel 1.2709 at a layer thickness of 100 μm by DMLS (Direct Metal Laser Sintering). HPDC (High Pressure Die Casting) tools are printed from this material. To date, only layer thicknesses of 20–50 μm are used, and parameters for 100 µm were an undescribed area, according to the state of the art. Increasing the layer thickness could lead to time reduction and higher economic efficiency. The study methodology was divided into several steps. The first step was the research of the single-track 3D printing parameters for the subsequent development of a more accurate description of process parameters. Then, in the second step, volume samples were produced in two campaigns, whose porosity was evaluated by metallographic and CT (computed tomography) analysis. The main requirement for the process parameters was a relative density of the printed material of at least 99.9%, which was achieved and confirmed using the parameters for the production of the samples for the tensile test. Therefore, the results of this article could serve as a methodological procedure for optimizing the parameters to streamline the 3D printing process, and the developed parameters may be used for the productive and quality 3D printing of 1.2709 tool steel.

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“…They proposed a model incorporating a phase function to differentiate the powder phase, melting liquid phase, dense solid phase and vaporized gas phase that also includes the volume shrinkage induced by the density change during the melting process. Letenneur et al proposed a three dimensional analytical model which enables the calculation of the temperature distribution in powder for a Gaussian laser heat source [16]. In [17], Li et al enhanced their proposed approach by including the residual stress field analysis.…”

Section: Introductionmentioning

confidence: 99%

Phase Change with Density Variation and Cylindrical Symmetry: Application to Selective Laser Melting

Fyrillas

Ioannou

Papadakis

et al. 2019

JMMP

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In this paper we introduce an analytical approach for predicting the melting radius during powder melting in selective laser melting (SLM) with minimum computation duration. The purpose of this work is to evaluate the suggested analytical expression in determining the melt pool geometry for SLM processes, by considering heat transfer and phase change effects with density variation and cylindrical symmetry. This allows for rendering first findings of the melt pool numerical prediction during SLM using a quasi-real-time calculation, which will contribute significantly in the process design and control, especially when applying novel powders. We consider the heat transfer problem associated with a heat source of power Q' (W/m) per unit length, activated along the span of a semi-infinite fusible material. As soon as the line heat source is activated, melting commences along the line of the heat source and propagates cylindrically outwards. The temperature field is also cylindrically symmetric. At small times (i.e., neglecting gravity and Marangoni effects), when the density of the solid material is less than that of the molten material (i.e., in the case of metallic powders), an annulus is created of which the outer interface separates the molten material from the solid. In this work we include the effect of convection on the melting process, which is shown to be relatively important. We also justify that the assumption of constant but different properties between the two material phases (liquid and solid) does not introduce significant errors in the calculations. A more important result; however, is that, if we assume constant energy input per unit length, there is an optimum power of the heat source that would result to a maximum amount of molten material when the heat source is deactivated. The model described above can be suitably applied in the case of selective laser melting (SLM) when one considers the heat energy transferred to the metallic powder bed during scanning. Using a characteristic time and length for the process, we can model the energy transfer by the laser as a heat source per unit length. The model was applied in a set of five experimental data, and it was demonstrated that it has the potential to quantitatively describe the SLM process.

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Optimization of Laser Powder Bed Fusion Processing Using a Combination of Melt Pool Modeling and Design of Experiment Approaches: Density Control

Cited by 53 publications

References 31 publications

The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment

The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment

Optimization of Process Parameters for Additively Produced Tool Steel 1.2709 with a Layer Thickness of 100 μm

Phase Change with Density Variation and Cylindrical Symmetry: Application to Selective Laser Melting

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