Experimental investigations on thermal behavior during pick-rock interaction and optimization of operating parameters of surface miner

Abstract Operation of surface miner has significantly expanded in mines because of its mass production ability and eco-friendly way of rock excavation. Optimal utilization of operating parameters ensures better cutting efficiency and longer machine life. Elevated temperature of a pick developed during the rock cutting process affects its life. It especially influences the wear rate of the cutting pick. During the present study, operating parameters of a surface miner were examined using the Taguchi method. In the process, an appropriate parametric combination was developed facilitating the use of optimal cutting power, higher production and minimum pick consumption. The role of operating parameters, i.e., depth of cut (DOC), cutting speed (CS) and drum speed (DS) with temperature (T) was analyzed statistically. Empirical models were also developed for studying temperature variations in cutting picks using Taguchi method, multiple linear regression (MLR) and artificial neural network (ANN). Confirmation test revealed that optimized temperature was achieved at a significance level of 0.05 and the Taguchi method enabled better outcomes with minimum deviation and a high degree of optimization of operating parameters of surface miner.