Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate
In this work, the dry turning of Inconel 601 alloy in a dry environment with PVD-coated cutting inserts was studied. Turning was performed at various cutting speeds, feeds, insert shapes, corner radii, rake angles, and approach angles. After machining, arithmetic mean surface roughness (Ra) and flan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/6/1068 |
| _version_ | 1797593527116365824 |
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| author | Goran Jovicic Aleksandar Milosevic Zeljko Kanovic Mario Sokac Goran Simunovic Borislav Savkovic Djordje Vukelic |
| author_facet | Goran Jovicic Aleksandar Milosevic Zeljko Kanovic Mario Sokac Goran Simunovic Borislav Savkovic Djordje Vukelic |
| author_sort | Goran Jovicic |
| collection | DOAJ |
| description | In this work, the dry turning of Inconel 601 alloy in a dry environment with PVD-coated cutting inserts was studied. Turning was performed at various cutting speeds, feeds, insert shapes, corner radii, rake angles, and approach angles. After machining, arithmetic mean surface roughness (Ra) and flank wear (VB) were measured, and the material removal rate was also calculated (MRR). An analysis of variance (ANOVA) was performed to determine the effects of the turning input parameters. For the measured values, the turning process was modeled using an artificial neural network (ANN). Based on the obtained model, the process parameters were optimized using a genetic algorithm (GA). The objective function was to simultaneously minimize Ra and VB and maximize MRR. The accuracy of the model and the optimal values were further validated by confirmation experiments. The maximum percentage errors, which are less than 2%, indicate the possibility of practical implementation of the hybrid approach for modeling and optimization of dry turning of Inconel 601 alloy. |
| first_indexed | 2024-03-11T02:10:33Z |
| format | Article |
| id | doaj.art-938e5dc8a3a54271815528ec3181309a |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-11T02:10:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-938e5dc8a3a54271815528ec3181309a2023-11-18T11:36:22ZengMDPI AGMetals2075-47012023-06-01136106810.3390/met13061068Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal RateGoran Jovicic0Aleksandar Milosevic1Zeljko Kanovic2Mario Sokac3Goran Simunovic4Borislav Savkovic5Djordje Vukelic6Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaMechanical Engineering Faculty, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, 35000 Slavonski Brod, CroatiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaIn this work, the dry turning of Inconel 601 alloy in a dry environment with PVD-coated cutting inserts was studied. Turning was performed at various cutting speeds, feeds, insert shapes, corner radii, rake angles, and approach angles. After machining, arithmetic mean surface roughness (Ra) and flank wear (VB) were measured, and the material removal rate was also calculated (MRR). An analysis of variance (ANOVA) was performed to determine the effects of the turning input parameters. For the measured values, the turning process was modeled using an artificial neural network (ANN). Based on the obtained model, the process parameters were optimized using a genetic algorithm (GA). The objective function was to simultaneously minimize Ra and VB and maximize MRR. The accuracy of the model and the optimal values were further validated by confirmation experiments. The maximum percentage errors, which are less than 2%, indicate the possibility of practical implementation of the hybrid approach for modeling and optimization of dry turning of Inconel 601 alloy.https://www.mdpi.com/2075-4701/13/6/1068turningarithmetic mean surface roughnessflank wearmaterial removal rateoptimization |
| spellingShingle | Goran Jovicic Aleksandar Milosevic Zeljko Kanovic Mario Sokac Goran Simunovic Borislav Savkovic Djordje Vukelic Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate Metals turning arithmetic mean surface roughness flank wear material removal rate optimization |
| title | Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate |
| title_full | Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate |
| title_fullStr | Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate |
| title_full_unstemmed | Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate |
| title_short | Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate |
| title_sort | optimization of dry turning of inconel 601 alloy based on surface roughness tool wear and material removal rate |
| topic | turning arithmetic mean surface roughness flank wear material removal rate optimization |
| url | https://www.mdpi.com/2075-4701/13/6/1068 |
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