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Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems
Abstract
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.
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Authors (3)
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Irene Buj - Corral Dr.
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Carmelo J. Luis-Pérez
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.triboint.2023.108354
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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TRIBOLOGY INTERNATIONAL
no. 182,
ISSN: 0301-679X - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.1016/j.triboint.2023.108354
- Sources of funding:
-
- IDUB
- Verified by:
- Gdańsk University of Technology
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