Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network
Joint Authors
Abbas, Adel Taha
Ragab, Adham Ezzat
Alharthi, Nabeel H.
Bingol, Sedat
Alharbi, Hamad F.
El-Danaf, Ehab A.
Source
Advances in Materials Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-30
Country of Publication
Egypt
No. of Pages
8
Abstract EN
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.
American Psychological Association (APA)
Alharthi, Nabeel H.& Bingol, Sedat& Abbas, Adel Taha& Ragab, Adham Ezzat& El-Danaf, Ehab A.& Alharbi, Hamad F.. 2017. 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،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
Modern Language Association (MLA)
Alharthi, Nabeel H.…[et al.]. 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 No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
American Medical Association (AMA)
Alharthi, Nabeel H.& Bingol, Sedat& Abbas, Adel Taha& Ragab, Adham Ezzat& El-Danaf, Ehab A.& Alharbi, Hamad F.. 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. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1124711
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124711