| DOI | Resolve DOI: https://doi.org/10.1109/CCECE53047.2021.9569066 |
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| Author | Search for: Wu, Honghe; Search for: Bordatchev, Evgueni V.1 |
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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 | 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), September 12-17, 2021, ON, Canada |
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| Subject | artificial intelligence; feed-forward neural network; laser polishing; performance; surface quality; modelling; analysis; visualization; surface emitting lasers; tools; predictive models; reliability engineering; surface roughness; surface topography |
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| Abstract | Modification and reconfiguration of the surface topography by the laser polishing (LP) process is a new innovative, non-material additive nor removal technology enabling new and/or enhancing existing value-adding surface functionalities, such as improving surface quality, visual appearance, wettability, friction, and others. However, the resultant surface is dependent upon many process parameters which makes selecting optimal process parameters to achieve desired surface topography complicated and unrepeatable. This study proposes and demonstrates that feed-forward neural network (FFNN) can reliably model the LP of H13 tool steel and predict the laser polished surface topography parameters such as areal waviness and roughness with a probability of 70%. |
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| Publication date | 2021-09-12 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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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 | f4733981-74fa-4544-a4c0-c6bf7d9047ef |
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| Record created | 2023-01-16 |
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| Record modified | 2023-01-16 |
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