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Advances in Materials Science and Engineering - Volume 2015 2015, Article ID 304691, 8 pages -

Research ArticleDepartment of Mechanical Engineering, ChienKuo Technology University, Changhua 500, Taiwan

Received 18 September 2014; Accepted 24 November 2014

Academic Editor: Katsuyuki Kida

Copyright © 2015 Kingsun Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study analyzes a variety of significant drilling conditions on aluminum oxide with 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 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.





Autor: Kingsun Lee

Fuente: https://www.hindawi.com/



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