Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network
Author's Department
Mechanical Engineering Department
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http://www.worldcat.org/oclc/7317562381
Document Type
Research Article
Publication Title
Advances in Materials Science and Engineering
Publication Date
2-13-2018
doi
10.1155/2018/1825291
Abstract
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.
Recommended Citation
APA Citation
Mohamed, M. F.
(2018). Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network. Advances in Materials Science and Engineering, 2018,
10.1155/2018/1825291
https://fount.aucegypt.edu/faculty_journal_articles/366
MLA Citation
Mohamed, Mohamed Fawzy Aly
"Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network." Advances in Materials Science and Engineering, vol. 2018, 2018,
https://fount.aucegypt.edu/faculty_journal_articles/366