Empirical models to predict mean cutting forces on point-attack pick cutters

[1]  Cemal Balci,et al.  Control of rock cutting efficiency by muck size , 2008 .

[2]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[3]  Yi Huang,et al.  Application of artificial neural networks to predictions of aggregate quality parameters , 1999 .

[4]  M. Hood,et al.  An application of linearised dimensional analysis to rock cutting , 1999 .

[5]  Bulent Tiryaki,et al.  Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees , 2008 .

[6]  I. Evans.  A theory of the cutting force for point-attack picks , 1984 .

[7]  N. Goktan,et al.  A semi-empirical approach to cutting force prediction for point-attach picks , 2005 .

[8]  Y. Nishimatsu,et al.  The mechanics of rock cutting , 1972 .

[9]  Bulent Tiryaki,et al.  Application of artificial neural networks for predicting the cuttability of rocks by drag tools , 2008 .

[10]  Dimitris Kaliampakos,et al.  Modelling TBM performance with artificial neural networks , 2004 .

[11]  H. Copur,et al.  Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results , 2006 .

[12]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[13]  Cemal Balci,et al.  Correlative study of linear small and full-scale rock cutting tests to select mechanized excavation machines , 2007 .

[14]  Gerard V. Middleton,et al.  Data Analysis in the Earth Sciences Using MATLAB , 1999 .

[15]  Glenn W. Ellis,et al.  Neural Network Modeling of the Mechanical Behavior of Sand , 1992 .

[16]  M. Alvarez Grima,et al.  Modeling tunnel boring machine performance by neuro-fuzzy methods , 2000 .

[17]  J H Garrett,et al.  KNOWLEDGE-BASED MODELLING OF MATERIAL BEHAVIOUR WITH NEURAL NETWORKS , 1991 .