Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network
All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turn...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/1825291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549096958722048 |
|---|---|
| author | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Mohamed F. Aly Hamad F. Alharbi |
| author_facet | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Mohamed F. Aly Hamad F. Alharbi |
| author_sort | Nabeel H. Alharthi |
| collection | DOAJ |
| description | All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coefficient of determination. |
| format | Article |
| id | doaj-art-6ceb773c83ee4b779f6b40206d1dfaaf |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-6ceb773c83ee4b779f6b40206d1dfaaf2025-02-03T06:12:10ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422018-01-01201810.1155/2018/18252911825291Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural NetworkNabeel H. Alharthi0Sedat Bingol1Adel T. Abbas2Adham E. Ragab3Mohamed F. Aly4Hamad F. Alharbi5Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Mechanical Engineering, Dicle University, Diyarbakir 21280, 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, School of Sciences and Engineering, American University in Cairo, AUC Avenue, P.O. Box 11835, New Cairo, EgyptDepartment of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaAll manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coefficient of determination.http://dx.doi.org/10.1155/2018/1825291 |
| spellingShingle | Nabeel H. Alharthi Sedat Bingol Adel T. Abbas Adham E. Ragab Mohamed F. Aly Hamad F. Alharbi Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network Advances in Materials Science and Engineering |
| title | Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network |
| title_full | Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network |
| title_fullStr | Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network |
| title_full_unstemmed | Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network |
| title_short | Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network |
| title_sort | prediction of cutting conditions in turning az61 and parameters optimization using regression analysis and artificial neural network |
| url | http://dx.doi.org/10.1155/2018/1825291 |
| work_keys_str_mv | AT nabeelhalharthi predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork AT sedatbingol predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork AT adeltabbas predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork AT adhameragab predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork AT mohamedfaly predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork AT hamadfalharbi predictionofcuttingconditionsinturningaz61andparametersoptimizationusingregressionanalysisandartificialneuralnetwork |