Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks
نویسندگان
چکیده
Circular sawing with diamond-impregnated tools has been extensively used in stone processing plants and prediction of rock sawability is important in the cost estimation and the planning of the plants. Rock sawability depends on machine characteristics, type and diameter of diamond saw, depth of cut, rate of sawing and tool wear, and rock properties. Some researchers have investigated the relation between sawability and rock properties. Norling [1] correlated sawability with petrographic properties and concluded that grain size was more relevant to sawability than the quartz content. Burgess [2] proposed a regression model for sawability, which was based on mineralogical composition, hardness, grain size and abrasion resistance. Wright and Cassapi [3] tried to correlate the pertographic analysis and physical properties with actual sawing results. The research indicated cutting forces to have the closest correlation. Hausberger [4] concluded, by studying the work by other authors, that the higher the proportion of minerals with well-defined cleavage planes, the easier the stone is to cut. Unver [5] developed predictive equations for the estimation of specific wear and cutting force in rock sawing. Clausen et al. [6] carried out a study of the acoustic emission during single diamond scratching of
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