An experimentally validated dislocation density based computational framework for predicting microstructural evolution in cold spray process

An experimentally validated dislocation density based computational framework for predicting microstructural evolution in cold spray process
Title:
An experimentally validated dislocation density based computational framework for predicting microstructural evolution in cold spray process
Other Titles:
International Journal of Solids and Structures
Publication Date:
24 April 2021
Citation:
Msolli, S., Zhang, Z.-Q., Seng, D. H. L., Zhang, Z., Guo, J., Reddy, C. D., … Loi, Q. (2021). An experimentally validated dislocation density based computational framework for predicting microstructural evolution in cold spray process. International Journal of Solids and Structures, 225, 111065. doi:10.1016/j.ijsolstr.2021.111065
Abstract:
We present an experimentally validated computational model for microstructural evolution in the cold spray additive manufacturing process of Al6061 alloy coating. The microstructural characteristics are determined with a dislocation density-based model that is shown to be applicable to cold spray modeling and is implemented in a Eulerian finite element framework to predict microstructural evolution for both single and multiple particle cold spray deposition. A comparison of the numerical and experimental results from Scanning Electron Microscope (SEM), Electron Backscatter Diffraction (EBSD) and Kernel Average Mis-orientation (KAM) analyses, reveals the evolution of cell size and mis-orientation resulting in grain refinement at the particle–substrate and particle–particle interfaces. Although there is a large plastic deformation due to high speed impact, the dislocation density in the particle decreases with distance from the impacting interface and this feature is observed in both the simulation results and the experimental characterization data. The validated model can be leveraged to predict microstructural evolution under different process conditions including spray angle, pre-heat temperature, and impact velocity.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Machining Learning Assisted Control of Metal Cold Spray and Shot Peening Processes
Grant Reference no. : A1894a0032

This research / project is supported by the A*STAR - Structural Metal Alloy Program
Grant Reference no. : A18b1B0061

This research / project is supported by the A*STAR - Singapore Aerospace Program
Grant Reference no. : A1715a0073
Description:
Please find the link to the article at the publisher's URL: http://dx.doi.org/10.1016/j.ijsolstr.2021.111065
ISSN:
0020-7683
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