Laser directed energy deposition of water-atomized iron powder: Process optimization and microstructure of single-tracks

Ansari, Mohammad; Mohamadizadeh, Alireza; Huang, Yuze; Paserin, Vladimir; Toyserkani, Ehsan

doi:10.1016/j.optlastec.2018.11.054

Cited by 37 publications

(8 citation statements)

References 24 publications

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“…The hopper was loaded with approximately 3 kg of GA316 powder and > 200 g of powder was run through the pipework to allow the system to stabilise before collecting data. The maximum hopper rotation speed is 10 rpm, and data was collected at 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 rpm, covering the standard range of powder flow rates found in literature [14][15][16] . The data was collected as a single run, with 5 min at each turntable speed stepping up from 0.5 rpm to 3.0 rpm.…”

Section: Hopper Flow Measurementsmentioning

confidence: 99%

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Multi-Faceted Monitoring of Powder Flow Rate Variability in Directed Energy Deposition

Freeman¹,

Thomas²,

Chechik³

et al. 2021

SSRN Journal

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Powder flow rate is a key parameter in Directed Energy Deposition (DED) processes. During a typical build, if powder flow rate is reduced for just 1 second, 30 mm of melt track is affected. Consequently, even a small variation in powder flow rate can have significant implications on build quality. In this work, the powder flow stability for different types of 316 L steel powders was quantified using a combination of methodologies including offline weight measurements, flow imaging, in-situ build data and coaxial melt pool imaging. Flow rate oscillation was observed, correlated with the periodicity of powder hopper turntable rotation, at sufficient magnitude to cause build quality effects and be identifiable in coaxial melt pool imaging. The implications of flow rate variation on the use of melt pool imaging for closed-loop control are discussed.

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Section: Hopper Flow Measurementsmentioning

confidence: 99%

“…From literature, powder flow rate is generally between 5 g/min and 20 g/min in DED, although some use flow rates outside this range [14][15][16] . As the hoppers are volumetrically controlled, the material density and packing density have an influence on the volume of powder required to achieve a target mass flow rate.…”

Section: Introductionmentioning

confidence: 99%

Multi-Faceted Monitoring of Powder Flow Rate Variability in Directed Energy Deposition

Freeman¹,

Thomas²,

Chechik³

et al. 2021

SSRN Journal

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“…Ansari et al [21] studied the laser water-atomized iron powder deposition. The experimental design and model for optimization model were implemented based on the RSM, and ANOVA was used for testing the regression model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference

Velázquez

Helleno

Fals

et al. 2021

Int J Adv Manuf Technol

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Laser Metal Deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and, for optimizing the LMD process parameters using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each: Lp = 225 W, 250 W, 275 W, and Fl = 4.8 mm, 5.0 mm, 5.2 mm. The factor Pf was used with two levels, 9.40 g/s and 13.35 g/s. Analysis of variance allowed identifying that the Pf affect the Bh, Bh/Bw ratio, d and wa. The increase in Laser power (Lp) resulted in an increase of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead's geometrical characteristics, presented high adequacy (relative error up to 8.43 %) for assessing the experimental conditions. The second FIS indicated the best possible interaction, given the studied operating conditions and the variables evaluated. The maximum Output Defuzzified Index (ODI = 0.845) was obtained with the deposition process parameters Lp = 250 W, Fl = 5 mm, and Pf = 9.40 g/s.

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“…Different process modeling of LDED-PF, including analytical, numerical, and empirical/statistical, have been tried to address the above-mentioned challenges. Empirical/statistical models, which are developed based on the experimental data, have been extensively used for the single-track deposition optimization in LDED-PF, in general [2][3][4][5][6][7][8]. However, the empirical/statistical approach mainly considers a few variables as laser power, beam diameter, moving velocity, and powder feed rate.…”

Section: Introductionmentioning

confidence: 99%

A mathematical model of laser directed energy deposition for process mapping and geometry prediction of Ti-5553 single-tracks

et al. 2020

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Laser directed energy deposition of water-atomized iron powder: Process optimization and microstructure of single-tracks

Cited by 37 publications

References 24 publications

Multi-Faceted Monitoring of Powder Flow Rate Variability in Directed Energy Deposition

Multi-Faceted Monitoring of Powder Flow Rate Variability in Directed Energy Deposition

Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference

A mathematical model of laser directed energy deposition for process mapping and geometry prediction of Ti-5553 single-tracks

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