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Case study on the application of microstructural features extracted by convolutional neural network for cold spray of aluminum alloys

From National Research Council Canada

Alternative titleCase study on the application of microstructural features extracted by a convolutional neural network for cold spray of aluminum alloys
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DOIResolve DOI: https://doi.org/10.31399/asm.cp.itsc2023p0009
AuthorSearch for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada. Automotive and Surface Transportation
FormatText, Article
ConferenceITSC 2023, May 22-25, 2023, Québec City, Canada
Abstract
The use of process-microstructure-property relationships for cold spray can significantly reduce application development cost and time compared to legacy trial and error strategies. However, due to the heterogeneous microstructure of a cold spray deposit, with (prior) particle boundaries outlining consolidated splats (deformed particles) in the as-spray condition, the use of automated analysis methods is challenging. In this work, we demonstrate the utility of quantitative data developed from a convolutional neural network (CNN) for feature extraction of cold spray microstructures. Specifically, the power of CNN is harnessed to automatically segment the deformed particles, which is hardly accessible at scale with traditional image processing techniques. Deposits produced with various processing conditions are evaluated with metallography. Parameters related to particle morphology such as compactness are also quantified and correlated to strength.
Publication date
PublisherASM International
In
  • Thermal Spray 2023: Proceedings from the International Thermal Spray Conference, itsc2023p0009 (22 May 2023): 9–14.
Series
  • International Thermal Spray Conference 84536.
LanguageEnglish
Peer reviewedYes
NRC numberNRC-AST-2022-2023-19
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Record identifiere2da72b1-56c0-4b17-9b97-d0d27a64679e
Record created2023-08-28
Record modified2023-08-28
Date modified: