| Download | - View author's version: Ensemble methods for APS in-flight particle temperature and velocity prediction considering torch electrodes ageing (PDF, 813 KiB)
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| DOI | Resolve DOI: https://doi.org/10.31399/asm.cp.itsc2021p0044 |
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| Author | Search for: Yu, Kintak Raymond1; Search for: Cojocaru, Cristian1; Search for: Ilinca, Florin1; Search for: Irissou, Eric1 |
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| Affiliation | - National Research Council of Canada. Automotive and Surface Transportation
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| Format | Text, Article |
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| Conference | International Thermal Spray Conference 2021, May 24-28, 2021, Virtual Event |
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| Subject | atmospheric plasma spraying; in-flight particle analysis; predictive modeling; torch electrode wear |
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| Abstract | In an atmospheric plasma spray (APS) process, in-flight powder particle characteristics, such as the particle velocity and temperature, have significant influence on the coating formation. The nonlinear relationship between the input process parameters and in-flight particle characteristics is thus of paramount importance for coating properties design and quality control. It is also known that the ageing of torch electrodes affects this relationship. In recent years, machine learning algorithms have proven to be able to take into account such complex nonlinear interactions. This work illustrates the application of ensemble methods based on decision tree algorithms to evaluate and to predict in-flight particle temperature and velocity during an APS process considering torch electrodes ageing. Experiments were performed to record simultaneously the input process parameters, the in-flight powder particle characteristics and the electrodes usage time. Various spray durations were considered to emulate industrial coating spray production settings. Random forest and gradient boosting algorithms were used to rank and select the features for the APS process data recorded as the electrodes aged and the corresponding predictive models were compared. The time series aspect of the data will be examined. |
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| Publication date | 2021-05-24 |
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| Publisher | ASM International |
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| Other version | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRC-AST-50 |
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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 | 73d9763a-c940-40f5-8a31-9c25a5c93abc |
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| Record created | 2021-06-09 |
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| Record modified | 2023-08-08 |
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