| DOI | Resolve DOI: https://doi.org/10.1109/IPC48725.2021.9593065 |
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| Author | Search for: Wu, Honghe1; Search for: Bordatchev, Evgueni V.1 |
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| Affiliation | - National Research Council of Canada. Automotive and Surface Transportation
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| Funder | Search for: National Research Council of Canada; Search for: Western University |
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| Format | Text, Article |
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| Conference | 2021 IEEE Photonics Conference (IPC), October 18-21, 2021, Vancouver, British Columbia, Canada |
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| Subject | artificial intelligence; recurrent neural network; laser polishing; surface quality; modelling; predicting; visualization; recurrent neural networks; surface emitting lasers; predictive models; tools; laser modes; surface roughness |
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| Abstract | Surface quality improvement by a laser polishing (LP) process is a new innovative technology enabling value-adding functionalities, such as improving visual appearance, wettability, friction, and others through the control and reconfiguration of the surface topography. However, the resultant surface is dependent upon many process parameters which makes selecting optimal process parameters to achieve desired surface topography difficult and unrepeatable. It was proposed and demonstrated that recurrent neural network (RNN) 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 99% and 79%, respectively. |
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| Publication date | 2021-10-18 |
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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 | ec05f584-2ab2-4a1b-a067-8907ef9df1ee |
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| Record created | 2023-01-16 |
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| Record modified | 2023-03-16 |
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