| DOI | Resolve DOI: https://doi.org/10.1016/j.cirp.2023.04.017 |
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| Author | Search for: Gohari, HosseinORCID identifier: https://orcid.org/0000-0003-4820-7265; Search for: Mohamed, AymanORCID identifier: https://orcid.org/0000-0001-9785-4931; Search for: Hassan, Mahmoud1ORCID identifier: https://orcid.org/0000-0001-6881-3882; Search for: M'Saoubi, Rachid; Search for: Attia, Helmi1ORCID identifier: https://orcid.org/0000-0002-4705-5311 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Funder | Search for: National Research Council Canada; Search for: Mitacs; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | cutting; tool condition monitoring; adaptive control |
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| Abstract | A hybrid offline-online optimization, monitoring and control (HOMC) system was developed for milling processes. Safe cutting regions (SCRs) are defined based on offline analysis considering process dynamic stability, tool deflection and machined part geometric accuracy and surface quality. Near-optimum cutting conditions are defined offline using the cutter-workpiece contact information along the toolpath. A deep-learning tool condition monitoring approach is developed to detect the wear state in real-time using minimal learning efforts. The HOMC monitors the machining power signals and adaptively controls the feedrate within SCR using online predictions. Validation tests proved better process productivity, part quality and extended tool life. |
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| Publication date | 2023-04-12 |
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| Publisher | Elsevier |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S0007850623000392 |
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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 | 49662b53-07ce-4d69-bb04-cc4621737eb1 |
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| Record created | 2024-07-22 |
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| Record modified | 2024-07-22 |
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