Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process
Texture coefficient is one of the most influential parameters in rock engineering specifications in various projects including drilling, cutting, permeability of all-section drilling devices, etc. Meanwhile, investigating and forecasting the energy consumption of saw cutters are one of the most important factors in estimating the cutting costs. The present study aims to investigate the relationship between rock texture characteristics and the amount of energy consumption of the gang saw machine in the process of cutting carbonate rocks. To evaluate the effects of texture on the rocks' engineering specifications, 14 carbonate rock samples were studied. A microscopic thin section was made from each rock specimen. Then, five digital images were taken from each section under a microscope and the values of area, environment, the largest diameter and the smallest diameter of all grains in each image were determined. Using these specifications, the coefficient of texture of all rock samples was calculated and the relationship between the texture coefficient and the rate of energy consumption of the gang saw machine was investigated for the studied samples. The study results indicated that there was a significant relation between the texture coefficient and energy consumption rate in the three groups of carbonate rocks.
Howarth D.F., Rowlands J. C. “Quantitative Assessment of Rock Texture and Correlation with Drillability and Strength Properties“. Rock Mechanics and Rock Engineering, (January 1987), Volume 20, Issue 1, pp 57–85, doi: 10.1007/BF01019511.
Ersoy A., Waller M.D., “Textural characterization of rocks“. J. of Engineering Geology, (June 1995), Vol. 39, Issues 3-4, p. 123-136, doi: 10.1016/0013-7952(95)00005-Z.
Tugrul A., Zarif I.H. “Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey“. Engineering Geology 51 (1999), 303±317, doi: 10.1016/S0013-7952(98)00071-4.
Singh V.K., Singh D., Singh T.N. “Prediction of strength properties of some schistose rocks from petrographic properties using artiﬁcial neural networks“. International Journal of Rock Mechanics & Mining Sciences 38 (2001), 269–284, doi: 10.1016/S1365-1609(00)00078-2.
Thuro, K. Plinninger, R.J. “Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. Technology roadmap for rock mechanics“, South African Institute of mining and metallurgy, (2003), ISRM-10CONGRESS-2003-212.
Kekec B., Unal M., Sensogut C. “Effect of the textural properties of rocks on their crushing and grinding features“. Journal of University of Science and Technology Beijing (October 2006), doi: 10.1016/S1005-8850(06)60079-0.
Hoseinie S.H., Aghababaei H., Pourrahimian Y. “Development of a new classiﬁcation system for assessing of rock mass drillability index (RDi) “. International Journal of Rock Mechanics & Mining Sciences 45 (January 2008), 1–10, doi: 10.1016/j.ijrmms.2007.04.001.
Alber M., Kahraman S. “Predicting the uniaxial compressive strength and elastic modulus of a fault breccia from texture coefﬁcient“. Rock Mech Rock Engng (2009), 42: 117–127, doi: 10.1007/s00603-008-0167-x.
Yilmaz N, Goktan R, Kibici Y. “Relations between some quantitative petrographic characteristics and Mechanical strength properties of granitic building stones“. International Journal of Rock Mechanics & Mining Sciences, 48 (2011), 506-513, doi: 10.1016/j.ijrmms.2010.09.003.
Ghaysari N., Ataei M., Sereshki F., Mikaeil R. “Prediction of performance of diamond wire saw with respect to texture characteristics of rock“. Arch. Min. Sci., Vol. 57 (2012), No 4, p. 887–900, doi: 10.2478/v10267-012-0058-6.
Tandon R, Gupta V. “The control of mineral constituents and textural characteristics on the petrophysical & mechanical (PM) properties of different rocks of the Himalaya“. Engineering Geology 153 (2013), 125–143, doi: 10.1016/j.enggeo.2012.11.005.
Ozturk C.A., Nasuf E. “Strength classiﬁcation of rock material based on textural properties“. Tunnelling and Underground Space Technology 37 (2013), 45–54, doi: 10.1016/j.tust.2013.03.005.
Korkanç M., Solak B. “Estimation of engineering properties of selected tuffs by using grain/matrix ratio“. Journal of African Earth Sciences 120 (2016), 160-172, doi: 10.1016/j.jafrearsci.2016.05.008.
Yusof N.Q.A.M., Zabidi H. “Correlation of mineralogical and textural characteristics with engineering properties of granitic rock from Hulu Langat, Selangor“. Procedia Chemistry, 19 (2016), 975 – 980, doi: 10.1016/j.proche.2016.03.144.
Kahraman S., Fener M., Käsling H., Thuro K. “The inﬂuences of textural parameters of grains on the LCPC abrasivity of coarse-grained igneous rocks“. Tunnelling and Underground Space Technology 58 (2016), 216–223, doi:10.1016/j.tust.2016.05.011.
Krmíek L., Horák V., Kuboušková S., Petružálek M. “Behaviour of Multicomponent Geomaterials: Pilot Experimental Study in Rock Mechanics“. Procedia Engineering 191 (2017), 31 – 3, doi: 10.1016/j.proeng.2017.05.150.
Hoseinie S. H., Ataei M., Mikaeil R. “Effects of microfabric on drillability of rocks“. Bulletin of Engineering Geology and the Environment. (2017), doi: 10.1007/s10064-017-1188-z.
Mikaeil, R., Ataei, M., Yousefi, R. “Application of a fuzzy analytical hierarchy process to the prediction of vibration during rock sawing“, Mining Science and Technology (China), (2011), 21, 611–619, doi: 10.1016/j.mstc.2011.03.008.
Ataei, M., Mikaiel, R., Sereshki, F., Ghaysari, N. “Predicting the production rate of diamond wire saw using statistical analysis“. Arabian Journal of Geosciences. (2011), 5, 1289-1295, doi: 10.1007/s12517-010-0278-z.
Ataei, M., Mikaeil, R., Hoseinie, S. H., Hosseini, S. M. “Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock“. International Journal of Rock Mechanics & Mining Sciences, (2012), 50, 83–93, doi: 10.1016/j.ijrmms.2011.12.002.
Ghaysari, N., Ataei, M., Sereshki, F., Mikaiel, R. “Prediction of Performance of diamond wire saw with respect to texture characterestic of rock“, Arch. Min. Sci., (2012), 57, 4, 887–900, doi: 10.2478/v10267-012-0058-6.
Sadegheslam, G., Mikaeil, R., Rooki, R., Ghadernejad, S., Ataei, M. “Predicting the production rate of diamond wire saw using multiple nonlinear regression analysis“, Geosystem engineering, (2013), 275-285, doi: 10.1080/12269328.2013.856276.
Aryafar A. and Mikaeil R., “Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using the Artificial Neural Networks“, Int. J. Min. & Geo-Eng. Vol.50, No.1, (June 2016), pp.121-130, doi: 10.22059/ijmge.2016.57861.
Mikaeil R., Shaffiee Haghshenas S., Shaffiee Haghshenas S., M. Ataei, “Performance Prediction of Circular Saw Machine Using Imperialist Competitive Algorithm and Fuzzy Clustering Technique“. Neural Computing and Applications, (2016), doi: 10.1007/s00521-016-2557-4.
Almasi SN., Bagherpor R., Mikaeil R., Ozcelick Y., “Developing a new rock classification based on the abrasiveness, hardness, and toughness of rocks and PA for the prediction of hard dimension stone sawability in quarrying. Geosystem Engineering“,(2017), 1-16, doi: 10.1080/12269328.2017.1278727.
Akhyani M., Sereshki F., Mikaeil R., Taji M., “Combining fuzzy RES with GA for predicting wear performance of circular diamond saw in hard rock cutting process“, Journal of Mining and Environment, (2017), doi: 10.22044/jme.2017.5770.1388.
Petruk W., Image analysis: “An overview of developments”. CANMET Report 86-4E, (1986), 5 pp.
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