| DOI | Resolve DOI: https://doi.org/10.1016/j.cirp.2020.04.039 |
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| Author | Search for: Sadek, A.1; Search for: Hassan, M.1ORCID identifier: https://orcid.org/0000-0001-6881-3882; Search for: Attia, M. H.1 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Subject | cutting; adaptive control; predictive model |
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| Abstract | A new cyber-physical adaptive control system (CPACS) is presented for drilling hybrid stacks. A generalized approach for features recognition, and learning, of spindle power signals was developed for real-time detection of tool wear level. Subsequently, a high fidelity model predicts the drilling forces and the damage at the layers and interfaces of the stacked panel. The CPACS uses these predictions to maximize the feedrate while maintaining the damage-free force limit at each layer. The system communicates with the CNC machine controller to continuously update the drilling conditions. Validation test showed improved productivity and extended tool life while preserving part quality. |
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| Publication date | 2020-05-17 |
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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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| 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 | 9771bf9c-b874-4d87-97ad-cde53ba74fff |
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| Record created | 2020-10-28 |
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| Record modified | 2021-09-17 |
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