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Advances in Materials Science and Engineering
Volume 2015, Article ID 304691, 8 pages
http://dx.doi.org/10.1155/2015/304691
Research Article

Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method

Department 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.

Citations to this Article [1 citation]

The following is the list of published articles that have cited the current article.

  • Yilun Liu, Songbai Li, Dalian Yang, Jie Tao, Chi Liu, and Jiuhuo Yi, “Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm,” Engineering Computations (Swansea, Wales), vol. 34, no. 4, pp. 1034–1053, 2017. View at Publisher · View at Google Scholar