Prediction of cutting force in turning process by using artificial neural network
Other Title(s)
التنبؤ بقوى القطع في عملية الخراطة باستخدام الشبكة العصبية الاصطناعية
Author
Source
al-Khwarizmi Engineering Journal
Issue
Vol. 16, Issue 2 (30 Jun. 2020), pp.34-46, 13 p.
Publisher
University of Baghdad al-Khwarizmi College of Engineering
Publication Date
2020-06-30
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Topics
Abstract EN
Cutting forces are important factors for determining machine serviceability and product quality.
Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation.
The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map).
The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data.
Twenty-five samples of experimental data were used, including nineteen to train the network.
Moreover six other experimental tests were implemented to test the network.
The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.
American Psychological Association (APA)
Ibrahim, Marwah Qasim. 2020. Prediction of cutting force in turning process by using artificial neural network. al-Khwarizmi Engineering Journal،Vol. 16, no. 2, pp.34-46.
https://search.emarefa.net/detail/BIM-969893
Modern Language Association (MLA)
Ibrahim, Marwah Qasim. Prediction of cutting force in turning process by using artificial neural network. al-Khwarizmi Engineering Journal Vol. 16, no. 2 (Jun. 2020), pp.34-46.
https://search.emarefa.net/detail/BIM-969893
American Medical Association (AMA)
Ibrahim, Marwah Qasim. Prediction of cutting force in turning process by using artificial neural network. al-Khwarizmi Engineering Journal. 2020. Vol. 16, no. 2, pp.34-46.
https://search.emarefa.net/detail/BIM-969893
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 44-45
Record ID
BIM-969893