Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method
Author
Source
Advances in Materials Science and Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-10
Country of Publication
Egypt
No. of Pages
8
Abstract EN
This study analyzes a variety of significant drilling conditions on aluminum oxide (with L18 orthogonal array) using a diamond drill.
The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size.
An orthogonal array, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effects of these drilling parameters.
The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack.
The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size) and output parameter (cracking area percentage).
The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis.
Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products.
Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina.
American Psychological Association (APA)
Lee, Kingsun. 2015. Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method. Advances in Materials Science and Engineering،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1053185
Modern Language Association (MLA)
Lee, Kingsun. Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method. Advances in Materials Science and Engineering No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1053185
American Medical Association (AMA)
Lee, Kingsun. Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method. Advances in Materials Science and Engineering. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1053185
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1053185