A versatile model for the prediction of complex geometry in 3D direct laser deposition

Nenadl, O.; Kuipers, W.; Koelewijn, N.; Ocelík, Václav; Hosson, J.Th.M. De

doi:10.1016/j.surfcoat.2016.08.090

Cited by 38 publications

(10 citation statements)

References 32 publications

(56 reference statements)

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“…From the perspective of geometric control, the development of a high-accuracy process model on the smallest processing unit (e.g., single cold spray track) offers a promising solution to the aforementioned problem since an aggregate of single tracks determines final part geometry. Furthermore, such a single-track model often plays a key role in the modelling of higher processing unit (i.e., overlapping and overlayer models) in the literature [25,29]. Previous studies of the single-track modelling fell into two main approaches: mathematical and data-driven modelling.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2019

Materials

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Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

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Section: Introductionmentioning

confidence: 99%

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2019

Materials

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“…In addition, the last clad presents a sharp curve from the top to the substrate. Both of the situations generate a lack of material and a distortion at the edges (Figure 6a), which increases at higher numbers of layers, as shown by Nenadl et al [30]. Thus, one possible solution to compensate for this absence of material is to add extra clads at the edges of the layer.…”

Section: = × + ×mentioning

confidence: 85%

Strategy Development for the Manufacturing of Multilayered Structures of Variable Thickness of Ni-Based Alloy 718 by Powder-Fed Directed Energy Deposition

Ramiro

Ortiz

Alberdi

et al. 2020

Metals

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In this study, a manufacturing strategy, and guidelines for inclined and multilayered structures of variable thickness are presented, which are based on the results of an own-developed geometrical model that obtains both the coating thickness and dilution. This model is developed for the powder-fed directed energy deposition process (DED) and it only uses the DED single-track cladding characteristics (height, width, area, and dilution depth), the overlap percentage, and the laser head tilting-angle as inputs. As outputs, it calculates both the cladding geometry and the dilution area of the coating. This model for the Ni-based alloy 718 was improved, based on previous studies of the single clad working both vertically and at an inclined angle, adding the equations of the single clad characteristics with respect to the main process parameters. The strategy proposed in this paper for multilayered cladding consisted of both adding an extra clad at the edges of the layer and using a variable value of the overlap percentage between clads for geometric adaptations. With this strategy, the material deposition is more accurate than otherwise, and it shows stable growth. Manufacturing a multilayered wall of wider thicknesses at higher heights was utilized to validate the strategy.

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“…In this paper, prediction of the part height of the DED build is computed by adding up the melt-pool height over all deposited layers, by ignoring any possible overlap of melt-pool height across two adjacent layers. For laser clad depositions, a two-dimensional (2D) cross-sectional model was developed in [ 35 ] to predict the relative coating height in multi-track, multi-layer depositions as a function of hatch overlap ratio, where the relative coating height was defined as the height of the coating measured from the substrate normalized by the height of an individual track. The hatch overlap ratio is defined as

with w denoting the melt-pool width and

denoting the hatch spacing between two individual tracks.…”

Section: Dynamic Model On Melt-pool Heightmentioning

confidence: 99%

“…The hatch overlap ratio is defined as

with w denoting the melt-pool width and

denoting the hatch spacing between two individual tracks. It was shown that for the overlap ratio of

, the relative coating height for 2 layers, 4 layers, and 6 layers was approximately 2, 4, and 6, respectively (see Figure 3 of [ 35 ]). The model prediction was validated by experimental measurements from depositions of multi-track multi-layer of stainless steel when the effect of thermal history was negligible due to a sufficient long inter-layer dwell time (enough time given to the first layer to cool down before applying the second layer) (see Figure 10 of [ 35 ]).…”

Section: Dynamic Model On Melt-pool Heightmentioning

confidence: 99%

“…It was shown that for the overlap ratio of

hatch overlap ratio (more details given in Section 4 ), and thus, based on the study of [ 35 ], it is assumed that the melt-pool height equals to the layer height, by ignoring any possible melt-pool height overlap across two adjacent layers.…”

Section: Dynamic Model On Melt-pool Heightmentioning

confidence: 99%

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Model-Based Feedforward Control of Part Height in Directed Energy Deposition

Wang

Nassar

et al. 2021

Materials

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Control of the geometric accuracy of a metal deposit is critical in the repair and fabrication of complex components through Directed Energy Deposition (DED). This paper developed and experimentally evaluated a model-based feedforward control of laser power with the objective of achieving the targeted part height in DED. Specifically, based on the dynamic model of melt-pool geometry derived from our prior work, a nonlinear inverse-dynamics controller was derived in a hatch-by-hatch, layer-by-layer manner to modulate the laser power such that the melt-pool height was regulated during the simulated build process. Then, the laser power trajectory from the simulated closed-loop control under the nonlinear inverse-dynamics controller was implemented as a feedforward control in an Optomec Laser-Engineered Net Shape (LENS) MR-7 system. This paper considered the deposition of L-shaped structures of Ti-6AL-4V as a case study to illustrate the proposed model-based controller. Experimental validation showed that by applying the proposed model-based feed-forward control for laser power, the resulting build had 24–42% reduction in the average build height error with respect to the target build height compared to applying a constant laser power through the entire build or applying a hatch-dependent laser power strategy, for which the laser power values were obtained from experimental trial and error.

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A versatile model for the prediction of complex geometry in 3D direct laser deposition

Cited by 38 publications

References 32 publications

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Strategy Development for the Manufacturing of Multilayered Structures of Variable Thickness of Ni-Based Alloy 718 by Powder-Fed Directed Energy Deposition

Model-Based Feedforward Control of Part Height in Directed Energy Deposition

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