Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts
The purpose of this investigation is to optimize minimum quantity lubrication (MQL) variables, including the nozzle diameter (D), inclined angle (A), air pressure (P), oil quantity (F), and spraying distance (S) for decreasing the energy consumption in the burnishing time (EB) and particulate matter...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2023-01-01
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| Series: | Tehnički Vjesnik |
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| Online Access: | https://hrcak.srce.hr/file/417624 |
| _version_ | 1797206586770325504 |
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| author | An-Le Van Trung-Thanh Nguyen |
| author_facet | An-Le Van Trung-Thanh Nguyen |
| author_sort | An-Le Van |
| collection | DOAJ |
| description | The purpose of this investigation is to optimize minimum quantity lubrication (MQL) variables, including the nozzle diameter (D), inclined angle (A), air pressure (P), oil quantity (F), and spraying distance (S) for decreasing the energy consumption in the burnishing time (EB) and particulate matter index (PI) of the interior burnishing process. The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models of the performance measures were proposed in terms of the MQL variables with the aid of the Taguchi method. The non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) and TOPSI were employed to produce feasible solutions and determine the best optimal point. The obtained results indicated that the optimal values of the D, A, P, F, and S are 1.0 mm, 35 deg., 3 Bar, 70 ml/h, and 10 mm, respectively, while the EB and PI are decreased by 8.0% and 15.7% at the optimal solution. The optimal ANFIS models were trustworthy and ensure accurate predictions. The optimization technique comprising the ANFIS, NSGA-G, and TOPSIS could be extensively utilized to determine the optimal outcomes instead of the trial-error and/or human experience. The outcomes could help to decrease environmental impacts in the practical burnishing process. |
| first_indexed | 2024-04-24T09:09:23Z |
| format | Article |
| id | doaj.art-53e85f409b354a3c8ed72320e98358fa |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:09:23Z |
| publishDate | 2023-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-53e85f409b354a3c8ed72320e98358fa2024-04-15T18:11:24ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392023-01-0130116917710.17559/TV-20220709090615Multi-Response Optimization of Burnishing Variables for Minimizing Environmental ImpactsAn-Le Van0Trung-Thanh Nguyen1Faculty of Engineering and Technology, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, Ward 13, District 4, Ho Chi Minh City 70000, VietnamFaculty of Mechanical Engineering, Le Quy Don Technical University, 236 Hoang Quoc Viet, Ha Noi 100000, VietnamThe purpose of this investigation is to optimize minimum quantity lubrication (MQL) variables, including the nozzle diameter (D), inclined angle (A), air pressure (P), oil quantity (F), and spraying distance (S) for decreasing the energy consumption in the burnishing time (EB) and particulate matter index (PI) of the interior burnishing process. The optimal adaptive neuro-based-fuzzy inference system (ANFIS) models of the performance measures were proposed in terms of the MQL variables with the aid of the Taguchi method. The non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) and TOPSI were employed to produce feasible solutions and determine the best optimal point. The obtained results indicated that the optimal values of the D, A, P, F, and S are 1.0 mm, 35 deg., 3 Bar, 70 ml/h, and 10 mm, respectively, while the EB and PI are decreased by 8.0% and 15.7% at the optimal solution. The optimal ANFIS models were trustworthy and ensure accurate predictions. The optimization technique comprising the ANFIS, NSGA-G, and TOPSIS could be extensively utilized to determine the optimal outcomes instead of the trial-error and/or human experience. The outcomes could help to decrease environmental impacts in the practical burnishing process.https://hrcak.srce.hr/file/417624ANFISburnishing processenergy savingsgenetic algorithmparticulate matter index |
| spellingShingle | An-Le Van Trung-Thanh Nguyen Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts Tehnički Vjesnik ANFIS burnishing process energy savings genetic algorithm particulate matter index |
| title | Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts |
| title_full | Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts |
| title_fullStr | Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts |
| title_full_unstemmed | Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts |
| title_short | Multi-Response Optimization of Burnishing Variables for Minimizing Environmental Impacts |
| title_sort | multi response optimization of burnishing variables for minimizing environmental impacts |
| topic | ANFIS burnishing process energy savings genetic algorithm particulate matter index |
| url | https://hrcak.srce.hr/file/417624 |
| work_keys_str_mv | AT anlevan multiresponseoptimizationofburnishingvariablesforminimizingenvironmentalimpacts AT trungthanhnguyen multiresponseoptimizationofburnishingvariablesforminimizingenvironmentalimpacts |