Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics

Zhenxing Cheng, Hu Wang () and Gui-Rong Liu
Additional contact information
Zhenxing Cheng: Hunan University
Hu Wang: Hunan University
Gui-Rong Liu: University of Cincinnati

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 4, No 7, 1009-1023

Abstract: Abstract This study proposed a deep convolutional neural network (DCNN) aided optimization (DCNNAO) method to improve the quality of deposition during the cold spray process which was simulated by molecular dynamics (MD). The idea of the DCNNAO is to extract the value of the objective function from the MD simulation snapshots directly by DCNN aided image process technique. Considering the huge memory requirement for MD result files, the main superiority of DCNNAO is to reduce the memory requirement and improve the efficiency of the optimization process by using a contour image (several hundred kilobytes) as the input instead of an MD result file (several hundred gigabytes). To complete this strategy, a Python script is written to generate required snapshots from result files automatically. Moreover, three boosted decision trees based optimization methods including surrogate optimization and heuristic algorithms are also implemented for comparison study. A detailed optimization result demonstrates that all the above methods can obtain an acceptable solution. The comparison is also given for an informed selection of them based on the trade-off between efficiency and accuracy.

Keywords: Molecular dynamics; Convolutional neural network; Optimization; Cold spray; Boosted decision trees (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01599-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01599-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01599-6

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().