Document Type: Original Research Paper


1 Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

2 School of Mining, College of Engineering, University of Tehran, Tehran, Iran

3 Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran.

4 Faculty of Mining and Metallurgy, Urmia University of Technology, Urmia, Iran.


One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s F-abrasiveness factor ( ) is one of the most common indices to assess rock abrasivity.  is the function of three rock parameters including the Brazilian tensile strength ( ), median grain size ( ), and equivalent quartz content ( ). By considering its formulation, it has been revealed that the coefficient of each parameter is equal, which is not correct because each parameter plays a different role in the rock abrasion process. This work aims to modify the original form of  by introducing three correction factors. To calculate these correction factors, an integrated method based on a combination of the statistical analysis and probabilistic simulation is applied to a dataset of 15 different andesite rocks. Based on the results obtained, the values of -0.36, 0.3, and -0.89 are suggested as the correction factors of ,  and , respectively. The performance of the modified Schimazek’s F-abrasiveness factor ( ) is checked not only by the wear rate of diamond wire but also by the cutting rate of the wire sawing process of Andesite rocks. The results obtained indicate that the wear rate and cutting rate of andesite rocks can be reliably predicted using . However, it should be noted that this work is a preliminary one on the limited rock types and further studies are required by incorporating different rock types.


[1]. Ataei, M., Mikaeil, R., Sereshki, F. and Ghaysari, N. (2012). Predicting the production rate of diamond wire saw using statistical analysis. Arabian Journal of Geosciences 5:1289-1295. s12517-010-0278-z.

[2]. Mikaeil, R., Yousefi., R., Ataei, M. and Farani, R.A. (2011). Development of a new classification system for assessing of carbonate rock sawability. Archives of Mining Sciences 56:59-70.

[3]. Plinninger, R., Käsling, H., Thuro, K. and Spaun, G. (2003). Testing conditions and geomechanical properties influencing the CERCHAR abrasiveness index (CAI) value. International journal of rock mechanics and mining sciences 40:259-263.

[4]. Yaralı, O., Yaşar, E., Bacak, G. and Ranjith, P.G. (2008). A study of rock abrasivity and tool wear in coal measures rocks. International Journal of Coal Geology 74:53-66.

[5]. Zum, G.K.H. (1987). Microstructure and wear of materials (Vol. 10). Elsevier. Amsterdam, 560 pp.

[6]. Stolarski, T.A. (2000). Tribology in machine design. Butterworth-Heinemann, Oxford, 298 pp.

[7]. Hamzaban, M.T., Memarian, H. and Rostami, J. (2013). Analysis of laboratory methods of rocks abrasivity measurement. Iranian Journal of Mining Engineering 8:87-106 (In Persian).

[8]. Buyuksagis, I.S. (2007). Effect of cutting mode on the sawability of granites using segmented circular diamond sawblade. Journal of Materials Processing Technology 183:399-406.

[9]. Rostami, J., Ghasemi, A., Gharahbagh, E.A., Dogruoz, C. and Dahl, F. (2014). Study of dominant factors affecting Cerchar abrasivity index. Rock mechanics and rock engineering.

[10]. Mikaeil, R., Haghshenas, S.S., Ozcelik, Y. and Gharehgheshlagh, H.H. (2018). Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw, Geotechnical and Geological Engineering 36:3779-3791.

[11]. Schimazek, J. and Knatz, H. (1970). The influence of rock composition on cutting velocity and chisel wear of tunnelling machines. Glückauf 106:274-278.

[12]. Ersoy, A. and Waller, M.D. (1995). Textural characterisation of rocks. Engineering geology 39:123-136.

[13]. ISRM. (1978). Suggested methods for determining hardness and abrasiveness of rocks. International Journal of Rock Mechanics, Mining Sciences and Geomechanics 15: 89–97.

[14]. Verhoef, P.N.W., Van Den Bold, H.J. and Vermeer T.h.W.M. (1990). Influence of microscopic structure on the abrasivity of rock as determined by the pin-on-disc test. Proc. 6th Int. Congr. IAEG, Amsterdam. Balkema, Rotterdam

[15]. Verhoef, P.N.W. (1993). Abrasivity of Hawkesbury sandstone (Sydney, Australia) in relation to rock dredging. Quarterly Journal of Engineering Geology and Hydrogeology 26:5-17. /10.1144/GSL.QJEG.1993.026.01.02.

[16]. Hoseinie, S.H., Ataei, M. and Osanloo, M. (2009). A new classification system for evaluating rock penetrability. International Journal of Rock Mechanic and Mining Sciences 46:1329–1340. /10.1016/j.ijrmms.2009.07.002.

[17]. Mikaeil, R., Ozcelik, Y., Ataei, M. and Yousefi R. (2013). Ranking the sawability of dimension stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics & Mining Sciences 58:118-126. j.ijrmms.2012.09.002.

[18]. Mikaeil, R., Kamran, M.A., Sadegheslam, G. and Ataei, M. (2015). Ranking sawability of dimension stone using PROMETHEE method. Journal of Mining & Environment 6:263-271. jme.2015.477.

[19]. Majeed, Y. and Bakar, M.A. (2016). Statistical evaluation of CERCHAR Abrasivity Index (CAI) measurement methods and dependence on petrographic and mechanical properties of selected rocks of Pakistan. Bulletin of Engineering Geology and the Environment 75:1341-1360.

[20]. Akhyani, M., Mikaeil, R., Sereshki, F. and Taji, M. (2017). Combining fuzzy RES with GA for predicting wear performance of circular diamond saw in hard rock cutting process. Journal of Mining and Environment (first online)

[21]. Almasi, S.N., Bagherpour, R., Mikaeil, R., Ozcelik, Y. (2017). Analysis of bead wear in diamond wire sawing considering the rock properties and production rate. Bulletin of Engineering Geology and the Environment 76:1593-1607.

[22]. Dormishi, A., Ataei, M., Khalokakaei, R., and Mikaeil, R. (2018). Energy consumption prediction of gang saws from rock properties in carbonate rocks cutting process. International Journal of Mining and Mineral Engineering 9:216-227. 1504/IJMME.2018.096115

[23]. Haghshenas, S.S., Shirani Faradonbeh, R., Mikaeil, R., Haghshenas, S.S., Taheri, A., Saghatforoush, A. and Dormishi, A. (2019). A new conventional criterion for the performance evaluation of gang saw Machines. Measurements 146:159-170.

[24]. Aryafar, A., Mikaeil, R., Haghshenas, S.S. and Haghshenas, S.S. (2018). Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement, 124, 20-31.

[25]. Dormishi, A.R., Ataei, M., Khaloo Kakaie, R., Mikaeil, R. and Shaffiee Haghshenas, S. (2019). Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. Journal of Mining and Environment, 10 (2): 543-557.

[26]. Rubinstein, R.Y. and Kroese, D.P. (2007). Simulation and the Monte Carlo Method. A John Wiley & Sons, Inc., Publication.

[27]. Mikaeil, R., Ozcelik, Y., Ataei, M. and Haghshenas, S.S. (2019). Application of harmony search algorithm to evaluate performance of diamond wire saw. Journal of Mining and Environment

[28]. Ataei, M., Mikaeil, R., Hoseinie, S.H., Hoseinie, S.M. (2012). Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock. International Journal of Rock Mechanics and Mining Sciences 50:83-93.

[29]. Ataei, M., Hoseinie, S.H., Mikaeil, R. (2017). Modification of Schimazek’s abrasivity index to optimize its applications in rock engineering. Journal of Engineering Geology 11:73-90 (In Persian).