The Effect of Spray Distance and Scanning Step on the Coating Thickness Uniformity in Cold Spray Process

Cai, Zhenhua; Deng, Sihao; Liao, Hanlin; Zeng, Chunnian; Montavon, Ghislain

doi:10.1007/s11666-013-0002-0

Cited by 63 publications

(28 citation statements)

References 7 publications

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“…In contrast, the effect of standoff distance was the least influential on the track profiles in Figure 1c. With the larger standoff distance, the track profile became shorter and wider as observed in [31]. This phenomenon was previously confirmed by Pattison et al [45] and indicated that the standoff distance parameter space covered in this study was in the medium region near the optimal deposition efficiency point.…”

Section: Resultssupporting

confidence: 88%

“…Meanwhile, the ANN modelling showed better predictive performance in the region of track profile edges than the Gaussian model. Such better predictive performance at profile edges was most likely due to the ANN model adequately capturing the complex multivariate nature of cold spray process (e.g., bow shock and compressed gas layer [47]), while this was observed to be lacking with the Gaussian model used previously in cold spray [24,31]. In summary, the comparative study of the two modelling approaches in Figure 4 showed that the ANN modelling possessed the potential to provide the prediction of detailed track profiles in CSAM at the same level of accuracy or higher.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, three process variables were chosen as the inputs of the ANN model: spray angle, traverse speed, and standoff distance. These process variables are precisely controllable in real time with the support of an appropriate robotic system [12] and have been shown to be influential on cold spray geometric profiles in previous studies [24,31].…”

Section: Methodsmentioning

confidence: 99%

“…This hybrid modelling approach showed reasonable pictorial agreement with a verification bead profile under the reported experimental conditions. Cai et al employed a Gaussian model with a constant scaling coefficient to approximate the profile of a single symmetric cold spray track under different standoff distance scenarios [31]. The authors integrated the derived model into their Thermal Spray Toolkit, a software package in ABB RobotStudio ® , for offline programming to predict cold spray track profiles.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2019

Materials

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“…In this study, three experimental process variables were selected as inputs to an ANN model: spray angle, traverse speed and standoff distance, together with other input variables subsequently introduced in Section 2.2. This selection was made based on previous studies, demonstrating their influence on the geometry of a track profile in CSAM [24,36] and precise control with a robotic system [37].…”

Section: Methodsmentioning

confidence: 99%

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2021

Applied Sciences

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Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.

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Manufacturing Parameters for Cold Spray Additive Manufacturing

Yin

Lupoi

2021

Springer Tracts in Additive Manufacturing

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The Effect of Spray Distance and Scanning Step on the Coating Thickness Uniformity in Cold Spray Process

Cited by 63 publications

References 7 publications

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Manufacturing Parameters for Cold Spray Additive Manufacturing

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