Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy
In this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural net...
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2025-03-01
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| author | İbrahim Turan Barış Özlü Hasan Basri Ulaş Halil Demir |
| author_facet | İbrahim Turan Barış Özlü Hasan Basri Ulaş Halil Demir |
| author_sort | İbrahim Turan |
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| description | In this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods was compared using both experimental results and Signal/Noise (S/N) ratios derived from the experimental results. The experimental design was prepared according to Taguchi L27 orthogonal indexing. As a result, it was observed that increasing the cutting speed and feed rate increases the cutting temperature hole error, circularity error and cylindricity error. Increasing the cutting speed positively affected the surface roughness, while increasing the feed rate led to an increase in the surface roughness. The lowest surface roughness, cutting temperature, hole diameter error and hole circularity error values were measured for the uncoated cutting tool. The minimum cylindricity variation was measured for drilling with TiAlN-coated cutting tools. The optimum cutting parameters were A1B1C3 (Uncoated, 0.11 mm/rev, 200 m/min) for surface roughness, A1B1C1 (Uncoated, 0.11 mm/rev, 120 m/min) for cutting temperature, hole error, circularity error and cylindricity error. In the estimation of the output parameters with Taguchi, ANNs and ANFIS, it was observed that the estimates made by converting the experimental values into S/N ratios were more accurate than the estimates made with the experimental results. The reliability coefficient and prediction ability of the ANN model were found to be higher than Taguchi and ANFIS models in estimating the output parameters. |
| format | Article |
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| spelling | doaj-art-28956ef04cef4263ac4012b685d531fd2025-08-20T01:48:46ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-03-01939210.3390/jmmp9030092Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloyİbrahim Turan0Barış Özlü1Hasan Basri Ulaş2Halil Demir3Department of Manufacturing Engineering, Institute of Graduate Education, Karabük University, 78050 Karabük, TurkeyDepartment of Mechanical Program, Aksaray University, 68100 Aksaray, TurkeyDepartment of Manufacturing Engineering, Gazi University, 06500 Ankara, TurkeyDepartment of Mechanical Engineering, Karabük University, 78050 Karabük, TurkeyIn this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods was compared using both experimental results and Signal/Noise (S/N) ratios derived from the experimental results. The experimental design was prepared according to Taguchi L27 orthogonal indexing. As a result, it was observed that increasing the cutting speed and feed rate increases the cutting temperature hole error, circularity error and cylindricity error. Increasing the cutting speed positively affected the surface roughness, while increasing the feed rate led to an increase in the surface roughness. The lowest surface roughness, cutting temperature, hole diameter error and hole circularity error values were measured for the uncoated cutting tool. The minimum cylindricity variation was measured for drilling with TiAlN-coated cutting tools. The optimum cutting parameters were A1B1C3 (Uncoated, 0.11 mm/rev, 200 m/min) for surface roughness, A1B1C1 (Uncoated, 0.11 mm/rev, 120 m/min) for cutting temperature, hole error, circularity error and cylindricity error. In the estimation of the output parameters with Taguchi, ANNs and ANFIS, it was observed that the estimates made by converting the experimental values into S/N ratios were more accurate than the estimates made with the experimental results. The reliability coefficient and prediction ability of the ANN model were found to be higher than Taguchi and ANFIS models in estimating the output parameters.https://www.mdpi.com/2504-4494/9/3/92Al 6082-T6 alloydrillingtaguchiANNANFIS |
| spellingShingle | İbrahim Turan Barış Özlü Hasan Basri Ulaş Halil Demir Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy Journal of Manufacturing and Materials Processing Al 6082-T6 alloy drilling taguchi ANN ANFIS |
| title | Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy |
| title_full | Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy |
| title_fullStr | Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy |
| title_full_unstemmed | Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy |
| title_short | Prediction and Modelling with Taguchi, ANN and ANFIS of Optimum Machining Parameters in Drilling of Al 6082-T6 Alloy |
| title_sort | prediction and modelling with taguchi ann and anfis of optimum machining parameters in drilling of al 6082 t6 alloy |
| topic | Al 6082-T6 alloy drilling taguchi ANN ANFIS |
| url | https://www.mdpi.com/2504-4494/9/3/92 |
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