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

International Journal of Rock Mechanics and Mining Sciences

Volumn 47, Issue 5, 2010, Pages 858-864

  Tiryaki, B ^a   Boland, J N ^a   Li, X S ^a  

^a CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CUTTING; FORCE; MODELING;

EID: 77954031500     PISSN: 13651609     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijrmms.2010.04.012     Document Type: Article

References (21)

1. Evans I. A theory of the cutting force for point-attack picks. Int J Min Eng 1984, 184(2):63-71.
2. Nishimatsu Y. The mechanics of rock cutting. Int J Rock Mech Min Sci 1972, 9:261-270.
3. Bilgin N, Demircin M.A., Copur H, Balci C, Tuncdemir H, Akcin N. Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results. Int J Rock Mech Min Sci 2006, 43:139-156.
4. Gertsch R.E. Mechanical mining: challenges and directions. Min Eng 1994, 46(11):1250-1253.
5. Goktan R.M., Gunes N. A semi-empirical approach to cutting force prediction for point-attack picks. J S Afr Inst Min Metall 2005, 105:257-263.
6. Alehossein H, Hood M. An application of linearised dimensional analysis to rock cutting. Int J Rock Mech Min Sci 1999, 36:701-709.
7. Alehossein H. Data mining for geotechnical and mining engineers. J News Austral Geomech Soc 2001, 36:49-54.
8. Ellis G.W., Yao C, Zhao R. Neural network modeling of the mechanical behavior of sand. Proc 9th Eng Mech Congr ASCE 1992, 421-424. ASCE, New York. L.D. Lutes, J.M. Niedzwecki (Eds.).
9. Ghabousi J, Garret J.H., Wu X Knowledge based modelling of material behaviour with neural networks. J Eng Mech ASCE 1991, 117:132-153.
10. Tiryaki B. Application of artificial neural networks for predicting the cuttability of rocks by drag tools. Tunnelling Underground Space Technol 2008, 23:273-280.
11. Tiryaki B. Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 2008, 99:51-60.
12. Benardos A.G., Kaliampakos D.C. Modelling TBM performance with artificial neural networks. Int J Rock Mech Min Sci 2004, 19:597-605.
13. Alvarez Grima M, Bruines P.A. Verhoef PNW. Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling Underground Space Technol 2000, 15:259-269.
14. Huang Y. Application of artificial neural networks to predictions of aggregate quality parameters. Int J Rock Mech Min Sci 1999, 36:551-561.
15. Balci C, Bilgin N. Correlative study of linear small and full-scale rock cutting tests to select mechanized excavation machines. Int J Rock Mech Min Sci 2007, 44:468-476.
16. Statistics Toolbox for use with MATLAB, User's Guide Version 5. The MathWorks Inc, 2006.
17. Middleton G.V. Data analysis in the earth sciences using MATLAB 2000, Prentice-Hall, New Jersey.
18. Tuncdemir H, Bilgin N, Copur H, Balci C. Control of rock cutting efficiency by muck size. Int J Rock Mech Min Sci 2008, 45:278-288.
19. Hood M.C., Roxborough F.F. Rock breakage: mechanical. Mining engineering handbook 1992, 1:680-721. Soc Min Eng, Littleton, Colo. 2nd ed.
20. Breiman L, Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees. Monterey 1984, Wadsworth & Brooks/Cole, Calif.
21. Demuth H, Beale M, Hagan M. Neural network toolbox for use with MATLAB, User's Guide Version 5. The MathWorks Inc, 2006.