Evaluation of gang saws’ performance in the carbonate rock cutting process using feasibility of intelligent approaches

Abstract Gang saw is widely used in the dimension stone industry and stone cutting factories. One of the important factors in evaluating the efficiency of a machine is the electrical current consumed by the gang saw. Therefore, the evaluation of the electrical current consumed by the gang saw and study of the effective parameters are necessary in the rock cutting process. In the present research, considering the physical and mechanical properties of rock, including the uniaxial compressive strength (UCS), Mohs hardness (Mh), Schimazek’s F-abrasiveness factors (SF-a) and Young’s modulus (YM), it was attempted to study and evaluate the electrical current consumed by the gang saw using soft computing techniques. Thus, the Differential Evolution (DE) algorithm and Self-Organizing Map (SOM) algorithm were used as two intelligent techniques in this study. Results obtained from these studies showed that the DE algorithm could accurately classify 12 carbonate rocks under study into three groups, including travertine rocks sample with the average electrical current of 83.25 (A), crystal rocks sample with the average electrical current of 90 (A) and marble rocks sample with the average electrical current of 94 (A). Due to more details of output and results of the DE algorithm, it can be concluded that this algorithm has superiority over the SOM technique because it provides higher performance capacity in evaluating and classifying carbonate dimension stone samples in terms of the electrical current consumed by the machine and its physical and mechanical properties.