Control of Directed Energy Deposition Process to Obtain Equal-Height Rectangular Corner

Woo, Young Yun; Han, Sang-Il; Oh, Ilyeong; Moon, Young Hoon; Ha, Wansoo

doi:10.1007/s12541-019-00226-6

Cited by 21 publications

(8 citation statements)

References 28 publications

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“…As energy density is a combination of laser power and scanning speed, the graph showed a similar trend as laser power, as shown in Figure 6 (b). Laser scanning speed directly affects the part shape and dimensional accuracy [27]. Changes in the scanning speed cause variations in the bead size because of different deposition amount at a constant powder feed rate [27].…”

Section: Effects Of Process Variables On Materials Strength Behaviorsmentioning

confidence: 99%

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Era

Grandhi²,

Liu³

2022

Preprint

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Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed, and layer thickness, play an important role in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, data-driven machine learning models using XGBoost and Random Forest have been built and applied to predict the tensile behaviors of the stainless steel 316L parts by DED with variation in process parameters. The results suggest that both models can predict the tensile properties, e.g., yield strength, elongation, and ultimate tensile strength, of the fabricated parts and compute the significance of the factors affecting the quality of the part. The performance of the models was then compared with the Ridge Regression. XGBoost outperformed both Ridge Regression and Random Forest.

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Section: Effects Of Process Variables On Materials Strength Behaviorsmentioning

confidence: 99%

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Era

Grandhi²,

Liu³

2022

Preprint

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“…The PBF process uses thermal energy (laser or electron beam) to selectively fuse regions of a powder bed, layer by layer, allowing the manufacture of complex shapes. The DED process uses a metal wire or powder combined with a thermal energy source to directly deposit material onto a substrate, resulting in excellent mechanical properties such as strength and elongation 2 , 3 . The PBF process fills the bed with powder, whereas the DED process feeds powder or wire only to where it is deposited.…”

Section: Introductionmentioning

confidence: 99%

Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods

Lim

Lee

et al. 2021

Sci Rep

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In the directed energy deposition (DED) process, significant empirical testing is required to select the optimal process parameters. In this study, single-track experiments were conducted using laser power and scan speed as parameters in the DED process for titanium alloys. The results of the experiment confirmed that the deposited surface color appeared differently depending on the process parameters. Cross-sectional view, hardness, microstructure, and component analyses were performed according to the color data, and a color suitable for additive manufacturing was selected. Random forest (RF) and support vector machine multi-classification models were constructed by collecting surface color data from a titanium alloy deposited on a single track; the accuracies of the multi-classification models were compared. Validation experiments were performed under conditions that each model predicted differently. According to the results of the validation experiments, the RF multi-classification model was the most accurate.

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“…This variations cause some problems in terms of precision in positioning and also in the deposition rate in this zones, the common defect is the over deposition of material and subsequent variation of layer height and track width, affecting the tolerance and can cause loss of geometric tolerances requiring additional post-processing or re-work to achieve a good geometric accuracy on the part. Some authors published interesting results developing algorithms to control the scanning-speed to generate smooth and equal-height deposition, in geometries with walls and corners [7]. Therefore, the control method enabling the DED fabrication of an equal-height rectangular acting over bead geometry measured or predicted but only take into consideration the layer height and not the precision of the wall thickness or internal/external radii in the wall's corner.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Thin Walls with Sharp Corners in SS316L and IN718 Alloys Manufactured with Laser Metal Deposition

Pereira

Borovkov

Zubiri

et al. 2021

JMMP

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In this work, the manufacture of thin walls with sharp corners has been optimized by adjusting the limits of a 3-axis cartesian kinematics through data recorded and analyzed off-line, such as axis speed, acceleration and the positioning of the X and Y axes. The study was carried out with two powder materials (SS316L and IN718) using the directed energy deposition process with laser. Thin walls were obtained with 1 mm thickness and only one bead per layer and straight/sharp corners at 90°. After adjusting the in-position parameter G502 for positioning precision on the FAGOR 8070 CNC system, it has been possible to obtain walls with minimal accumulation of material in the corner, and with practically constant layer thickness and height, with a radii of internal curvature between 0.11 and 0.24 mm for two different precision configuration. The best results have been obtained by identifying the correct balance between the decrease in programmed speed and the precision in the positioning to reach the point defined as wall corner, with speed reductions of 29% for a programmed speed of 20 mm/s and 61% for a speed of 40 mm/s. The walls show minimal defects such as residual porosities, and the microstructure is adequate.

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Control of Directed Energy Deposition Process to Obtain Equal-Height Rectangular Corner

Cited by 21 publications

References 28 publications

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods

Optimization of Thin Walls with Sharp Corners in SS316L and IN718 Alloys Manufactured with Laser Metal Deposition

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