| DOI | Resolve DOI: https://doi.org/10.1109/IPC48725.2021.9593073 |
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| Author | Search for: Cvijanovic, Srdjan J.1; Search for: Bordatchev, Evgueni V.1; Search for: Tutunea-Fatan, O. Remus |
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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, BC, Canada |
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| Physical description | 2 p. |
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| Subject | artificial intelligence; convolutional neural network; laser polishing; process conditions; surface quality; laser modes; surface roughness; surface topography; rough surfaces; complexity theory; convolutional neural networks |
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| Abstract | Convolutional neural network (CNN) enhances laser polishing (LP) by reducing the complexity of thermophysical process identification through post-LP topography modelling. Furthermore, the laborious process of achieving the desired surface roughness through heuristic searches is avoided by means of AI-enhanced predictions targeting LP process parameters. |
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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 | c31840f7-0145-4758-8ea8-2dc315dec80b |
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| Record created | 2022-09-16 |
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| Record modified | 2023-03-16 |
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