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.

中文翻译：

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露天采煤机采石相互作用热行为试验研究及作业参数优化

摘要 露天采矿机因其具有批量生产能力和环保的岩石开挖方式，在矿山中的作业范围显着扩大。操作参数的最佳利用可确保更好的切割效率和更长的机器寿命。在岩石切割过程中产生的刀具温度升高会影响其使用寿命。它尤其影响截齿的磨损率。在本研究中，使用田口方法检查露天采矿机的操作参数。在此过程中，开发了一个适当的参数组合，以促进最佳切割功率的使用、更高的产量和最低的镐消耗。对操作参数的作用，即切削深度（DOC）、切削速度（CS）和滚筒速度（DS）与温度（T）的作用进行了统计分析。还开发了经验模型，用于使用田口方法、多元线性回归 (MLR) 和人工神经网络 (ANN) 研究切削刀具的温度变化。确认测试表明，优化温度达到了 0.05 的显着性水平，田口方法能够以最小的偏差和高度优化露天采矿机的操作参数获得更好的结果。