Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

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...

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Main Authors: Nabeel H. Alharthi, Sedat Bingol, Adel T. Abbas, Adham E. Ragab, Mohamed F. Aly, Hamad F. Alharbi
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2018/1825291
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spelling doaj-6ceb773c83ee4b779f6b40206d1dfaaf2020-11-24T20:42:11ZengHindawi LimitedAdvances 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
collection DOAJ
language English
format Article
sources DOAJ
author Nabeel H. Alharthi
Sedat Bingol
Adel T. Abbas
Adham E. Ragab
Mohamed F. Aly
Hamad F. Alharbi
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
author_facet Nabeel H. Alharthi
Sedat Bingol
Adel T. Abbas
Adham E. Ragab
Mohamed F. Aly
Hamad F. Alharbi
author_sort Nabeel H. Alharthi
title 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_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_sort prediction of cutting conditions in turning az61 and parameters optimization using regression analysis and artificial neural network
publisher Hindawi Limited
series Advances in Materials Science and Engineering
issn 1687-8434
1687-8442
publishDate 2018-01-01
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.
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
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