An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system
This research presents a compensated fuzzy logic system that integrates an interval type-2 fuzzy logic system (IT2FLS) with the Gaussian mixture model (GMM) to model the turning process. First, an IT2FLS is elicited to model the turning process by mapping its input variables to the cutting force and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2022.2064359 |
| _version_ | 1818049364263698432 |
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| author | Abdallah Alalawin Wafa’ H. AlAlaween Mohammad A. Shbool Omar Abdallah Lina Al-Qatawneh |
| author_facet | Abdallah Alalawin Wafa’ H. AlAlaween Mohammad A. Shbool Omar Abdallah Lina Al-Qatawneh |
| author_sort | Abdallah Alalawin |
| collection | DOAJ |
| description | This research presents a compensated fuzzy logic system that integrates an interval type-2 fuzzy logic system (IT2FLS) with the Gaussian mixture model (GMM) to model the turning process. First, an IT2FLS is elicited to model the turning process by mapping its input variables to the cutting force and the surface quality. Second, the GMM is incorporated in the IT2FLS structure to compensate for the error residuals. The idea of such an incorporation stems from the fact that the majority of the models are constructed based on the normality assumption of the error. The GMM is developed in a way that refines the extracted rules and considers stochastic unmodelled behaviours. Validated on real experiments, it has been demonstrated that the compensated fuzzy logic system has the ability to accurately predict the cutting force and the surface quality; deal with uncertainties; and provide users with comprehensive understanding of the turning process. |
| first_indexed | 2024-12-10T10:36:24Z |
| format | Article |
| id | doaj.art-c2805fd690ec4c76a0ae6eda1c556873 |
| institution | Directory Open Access Journal |
| issn | 2169-3277 |
| language | English |
| last_indexed | 2024-12-10T10:36:24Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj.art-c2805fd690ec4c76a0ae6eda1c5568732022-12-22T01:52:26ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772022-12-011018910710.1080/21693277.2022.2064359An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic systemAbdallah Alalawin0Wafa’ H. AlAlaween1Mohammad A. Shbool2Omar Abdallah3Lina Al-Qatawneh4Department of Industrial Engineering, Faculty of Engineering, The Hashemite university, Zarqa, JordanDepartment of Industrial Engineering, The University of Jordan, Amman, JordanDepartment of Industrial Engineering, The University of Jordan, Amman, JordanDnata, Queen Alia Airport, Amman, JordanDepartment of Industrial Engineering, The University of Jordan, Amman, JordanThis research presents a compensated fuzzy logic system that integrates an interval type-2 fuzzy logic system (IT2FLS) with the Gaussian mixture model (GMM) to model the turning process. First, an IT2FLS is elicited to model the turning process by mapping its input variables to the cutting force and the surface quality. Second, the GMM is incorporated in the IT2FLS structure to compensate for the error residuals. The idea of such an incorporation stems from the fact that the majority of the models are constructed based on the normality assumption of the error. The GMM is developed in a way that refines the extracted rules and considers stochastic unmodelled behaviours. Validated on real experiments, it has been demonstrated that the compensated fuzzy logic system has the ability to accurately predict the cutting force and the surface quality; deal with uncertainties; and provide users with comprehensive understanding of the turning process.https://www.tandfonline.com/doi/10.1080/21693277.2022.2064359Compensated fuzzy logic systemcutting forceGaussian mixture modelinterval type-2 fuzzy logic systemsurface qualityturning process |
| spellingShingle | Abdallah Alalawin Wafa’ H. AlAlaween Mohammad A. Shbool Omar Abdallah Lina Al-Qatawneh An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system Production and Manufacturing Research: An Open Access Journal Compensated fuzzy logic system cutting force Gaussian mixture model interval type-2 fuzzy logic system surface quality turning process |
| title | An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| title_full | An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| title_fullStr | An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| title_full_unstemmed | An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| title_short | An interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| title_sort | interpretable predictive modelling framework for the turning process by the use of a compensated fuzzy logic system |
| topic | Compensated fuzzy logic system cutting force Gaussian mixture model interval type-2 fuzzy logic system surface quality turning process |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2022.2064359 |
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