Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study
A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2011/183764 |
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| author | Abdel Badie Sharkawy |
| author_facet | Abdel Badie Sharkawy |
| author_sort | Abdel Badie Sharkawy |
| collection | DOAJ |
| description | A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case. |
| format | Article |
| id | doaj-art-526fdc9c980143f3951ee9c7e02ebbd7 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-526fdc9c980143f3951ee9c7e02ebbd72025-02-03T01:23:19ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322011-01-01201110.1155/2011/183764183764Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative StudyAbdel Badie Sharkawy0Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut 71516, EgyptA study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.http://dx.doi.org/10.1155/2011/183764 |
| spellingShingle | Abdel Badie Sharkawy Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study Applied Computational Intelligence and Soft Computing |
| title | Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study |
| title_full | Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study |
| title_fullStr | Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study |
| title_full_unstemmed | Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study |
| title_short | Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study |
| title_sort | prediction of surface roughness in end milling process using intelligent systems a comparative study |
| url | http://dx.doi.org/10.1155/2011/183764 |
| work_keys_str_mv | AT abdelbadiesharkawy predictionofsurfaceroughnessinendmillingprocessusingintelligentsystemsacomparativestudy |