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Volume 4, Issue 2 (5-2011)

In this research, it is attempted to develop a new classification system for evaluating the rock sawability with respect to affective and major parameters. In this new classification system, four major characteristics of rock are selected for evaluating the rock sawability. In total, each rock takes a new score from 10 to 100 and classified into five classes: very poor, poor, fair, good and very good by new classification system. The new calculated rock sawability index (RSi) can be use as a useful index for evaluating the rock sawability. In the present paper, the relationship between ampere consumption, RSi and machine parameters are investigated by multiple regression. For this propose, 12 stones are tested by new sawing machine under different machining conditions (different depth of cut and feed rate). The results of this step are used as input data in SPSS software. Finally, two predicted models are presented with respect to machining parameters and RSi. These new models in stone factories can give a good viewpoint of energy consumption

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