Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System
Joint Authors
al-Zubaidi, Salah
Che Haron, Che Hassan
Abdul Ghani, Jaharah
Source
Modelling and Simulation in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Surface roughness is considered as the quality index of the machine parts.
Many diverse techniques have been applied in modelling metal cutting processes.
Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes.
The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end milling Ti6Al4V alloy with coated (PVD) and uncoated cutting tools under dry cutting conditions.
Real experimental results have been used for training and testing of ANFIS models, and the best model was selected based on minimum root mean square error.
A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5.
The findings provided evidence of the capability of ANFIS in modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.
American Psychological Association (APA)
al-Zubaidi, Salah& Abdul Ghani, Jaharah& Che Haron, Che Hassan. 2013. Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System. Modelling and Simulation in Engineering،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-509229
Modern Language Association (MLA)
al-Zubaidi, Salah…[et al.]. Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System. Modelling and Simulation in Engineering No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-509229
American Medical Association (AMA)
al-Zubaidi, Salah& Abdul Ghani, Jaharah& Che Haron, Che Hassan. Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System. Modelling and Simulation in Engineering. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-509229
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-509229