WJET  Vol.5 No.4 B , October 2017
Application of a Fuzzy Analytical Hierarchy Process for Predicting the Grindability of Granite
The ranking system of grindability is the key technology for high-efficiency grinding granite. A new classification system is presented to evaluate and ranking the grindability of granite. On account of the complicated relation between the mineral composition and mechanical properties with the grindability of granite, a new method by the combination of Fuzzy Analytic Hierarchy Process (FAHP) method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods is developed to establish the dependence function and fuzzy relationship between SiO2 content, quartz content, Shore hardness, density, compressive strength, flexural strength and abrasion resistance of granite with grinding force. The grindability of ten types of granite was evaluated and classified by this method. With the fuzzy ranking system established and the grindability classification, it is very convenient to evaluate the grindability and select a suitable diamond tools and proper grinding parameters for a new granite type by only the petrographic analysis and mechanical properties testing.
Cite this paper
Zhang, Z. , Wang, J. , Cao, H. , Lu, Q. and Ding, M. (2017) Application of a Fuzzy Analytical Hierarchy Process for Predicting the Grindability of Granite. World Journal of Engineering and Technology, 5, 117-125. doi: 10.4236/wjet.2017.54B013.
[1]   Mikaeil, R., Yousefi, R. and Ataei, M. (2011) Sawability Ranking of Carbonate Rock Using Fuzzy Analytical Hierarchy Process and TOPSIS Approaches. Scientia Iranica B, 18, 1106-1115. https://doi.org/10.1016/j.scient.2011.09.009

[2]   Ataei, M., Mikaeil, R., Hoseinie, S.H. and Hosseini, S.M. (2012) Fuzzy Analytical Hierarchy Process Approach for Ranking the Sawability of Carbonate Rock. Rock Mechanics and Mining Sciences, 50, 83-93. https://doi.org/10.1016/j.ijrmms.2011.12.002

[3]   Reza, M., Mohammad, A. and Reza, Y. (2011) Application of a Fuzzy Analytical Hierarchy Process to the Prediction of Vibration during Rock Sawing. Mining Science and Technology, 21, 611-619.

[4]   Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M. and Hosseini, S.M. (2013) Ranking the Sawability of Ornamental Stone Using Fuzzy Delphi and Multi-Criteria Decision-Making Techniques. International Journal of Rock Mechanics and Mining Sciences, 58, 118-126. https://doi.org/10.1016/j.ijrmms.2012.09.002

[5]   Yagiz, S. and Gokceoglu, C. (2010) Application of Fuzzy Inference System and Nonlinear Regression Models for Predicting Rock Brittleness. Expert Systems with Applications, 37, 2266-2272. https://doi.org/10.1016/j.eswa.2009.07.046

[6]   Tiryaki, B. (2008) Predicting Intact Rock Strength for Mechanical Excavation Using Multivariate Statistics, Artificial Neural Networks, and regression trees. Engineering Geology, 99, 51-60. https://doi.org/10.1016/j.enggeo.2008.02.003

[7]   Tiryaki, B. (2008) Application of Artificial Neural Networks for Predicting the Cuttability of Rocks by Drag Tools. Tunnellinng and Underground Space Technology, 23, 273-280. https://doi.org/10.1016/j.tust.2007.04.008