Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock

A new classification system is presented to evaluate and ranking the sawability of carbonate rock. The sawability of carbonate rock is classified into five categories: excellent, good, fair, poor and very poor. The sawability is assumed to depend on the uniaxial compressive strength, Young’s modulus...

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Veröffentlicht in:International journal of rock mechanics and mining sciences (Oxford, England : 1997) England : 1997), 2012-02, Vol.50, p.83-93
Hauptverfasser: Ataei, Mohammad, Mikaeil, Reza, Hoseinie, Seyed Hadi, Hosseini, Seyed Mehdi
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Sprache:eng
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Zusammenfassung:A new classification system is presented to evaluate and ranking the sawability of carbonate rock. The sawability of carbonate rock is classified into five categories: excellent, good, fair, poor and very poor. The sawability is assumed to depend on the uniaxial compressive strength, Young’s modulus, Mohs hardness, and a new abrasivity index. The FAHP approach is used to determine the weights of the above-mentioned parameters by decision makers. Moreover, in this paper, a new classification system was developed to modify Schimazek’s F-abrasiveness factor. In this new abrasivity classification, each parameter has a different importance coefficient. The new abrasivity index of carbonate rocks can be obtained from this new abrasivity classification system. The calculated sawability index of developed classification is applied for Iranian carbonate rocks to evaluation the energy consumption in rock sawing process. A variety of two groups of carbonate rocks (seven types) were saw using a fully instrumented laboratory sawing rig at different feed rates, peripheral speeds, and depth of cut. Then, a new statistical model was obtained using multiple regression method based on operating parameters and rock sawability index. ► A new classification system to evaluate sawability of carbonate rock is developed. ► FAHP approach is used in determining of the weights of parameters. ► The application of new classification index was studied. ► A new statistical model was obtained for predicting the energy consumption.
ISSN:1365-1609
1873-4545
1873-4545
DOI:10.1016/j.ijrmms.2011.12.002