Wyszukiwarka
Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems
Abstrakt
Honing processes are usually employed to manufacture combustion engine cylinders and hydraulic cylinders. A crosshatch pattern is obtained that favors the oil flow. In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) models were obtained for tool wear, average roughness Ra, cylindricity and material removal rate in finish honing processes. In addition, multi-objective optimization with the desirability function method was applied, in order to determine the process parameters that allow minimizing roughness, cylindricity error and tool wear, while maximizing material removal rate. The results showed that grain size and tangential velocity should be at their minimum levels, while density, pressure and linear velocity should be at their maximum levels. If only roughness, cylindricity error and tool wear are considered, then low grain size, low pressure and low linear velocity are recommended, while density and tangential velocity vary, depending on the optimization algorithm employed. This work will help to select appropriate process parameters in finishing honing processes, when roughness, cylindricity error and tool wear are to be minimized.
Cytowania
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Autorzy (3)
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Irene Buj - Corral Dr.
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Carmelo J. Luis-Pérez
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.triboint.2023.108354
- Licencja
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otwiera się w nowej karcie
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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TRIBOLOGY INTERNATIONAL
nr 182,
ISSN: 0301-679X - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Buj - Corral I., Sender P., Luis-Pérez C. J.: Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems// TRIBOLOGY INTERNATIONAL -Vol. 182, (2023), s.108354-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.triboint.2023.108354
- Źródła finansowania:
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- IDUB
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 44 razy
Publikacje, które mogą cię zainteresować
Modeling of Surface Roughness in Honing Processes by UsingFuzzy Artificial Neural Networks
- I. Buj - Corral,
- P. Sender,
- C. J. L. Luis-Pérez