Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network
In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/7560468 |
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| author | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Ehab A. El-Danaf Hamad F. Alharbi |
| author_facet | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Ehab A. El-Danaf Hamad F. Alharbi |
| author_sort | Nabeel H. Alharthi |
| collection | DOAJ |
| description | In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish. |
| format | Article |
| id | doaj-art-4d15cd84c5704276aa819e4ae290c677 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-4d15cd84c5704276aa819e4ae290c6772025-02-03T01:03:22ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422017-01-01201710.1155/2017/75604687560468Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural NetworkNabeel H. Alharthi0Sedat Bingol1Adel T. Abbas2Adham E. Ragab3Ehab A. El-Danaf4Hamad F. Alharbi5Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Mechanical Engineering, Dicle University, 21280 Diyarbakir, TurkeyDepartment of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaIn this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.http://dx.doi.org/10.1155/2017/7560468 |
| spellingShingle | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Ehab A. El-Danaf Hamad F. Alharbi Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network Advances in Materials Science and Engineering |
| title | Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network |
| title_full | Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network |
| title_fullStr | Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network |
| title_full_unstemmed | Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network |
| title_short | Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network |
| title_sort | optimizing cutting conditions and prediction of surface roughness in face milling of az61 using regression analysis and artificial neural network |
| url | http://dx.doi.org/10.1155/2017/7560468 |
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