| Alternative title | Case study on the application of microstructural features extracted by a convolutional neural network for cold spray of aluminum alloys |
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| Download | - View author's version: Case study on the application of microstructural features extracted by convolutional neural network for cold spray of aluminum alloys (PDF, 1.4 MiB)
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| DOI | Resolve DOI: https://doi.org/10.31399/asm.cp.itsc2023p0009 |
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| Author | Search for: Tu, Siyu1; Search for: Vo, Phuong1 |
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| Affiliation | - National Research Council Canada. Automotive and Surface Transportation
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| Format | Text, Article |
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| Conference | ITSC 2023, May 22-25, 2023, Québec City, Canada |
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| 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. |
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| Publication date | 2023-05-22 |
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| Publisher | ASM International |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRC-AST-2022-2023-19 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e2da72b1-56c0-4b17-9b97-d0d27a64679e |
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| Record created | 2023-08-28 |
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| Record modified | 2023-08-28 |
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