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Journal of Mining Sciences

2019 year, number 6

Performance Prediction of Circular Diamond Saws by Artificial Neural Networks and Regression Method Based on Surface Hardness Values of Mugla Marbles, Turkey

A. Guney
Mugla SД±tkД± Kocman University, Department of Mining Engineering, Mugla, Turkey
Keywords: твердость по Шору, твердость по Шмидту, производительность камнерезной машины, метод статической регрессии, искусственная нейронная сеть, Shore hardness (SH), Schmidt hardness (SCH), hourly areal slab productions (HASP), artificial neural network (ANN), regression method (RM)


Sawing of natural stones with diamond-impregnated circular saws is extensively implemented in stone processing plants in variety of applications that include sawing, cutting, splitting and trimming. Hence, thecost of diamond saws and energy have become important input in terms of estimating the hourly areal slab productions (HASPs) from the standpoint of effective cost analyses,feasible and sustainable designing of stone processing plantsprior to reaching a decision forthe investment. This study aimed at estimating the HASPs of the machines with circular diamond saws during the dimensioning of marble blocks quarried in Mugla (Turkey) Region. Thus, the models were generated to estimate the HASPs by artificial neural networks (ANN) and regression method (RM), based on Shore and Schmidt hardness values of rocks. Also, HASPs were acquired through in-plant measurements in order to justify the HASPs estimated by ANN and RM models. The analyses of the models generated using ANN proved to yield very strong consistencies with HASPs measured in the plants. Hence, the HASPs canbe estimated reliably by the ANN modelswhich also may be considered as a tool in designing ofnatural stone processing plants based on rock surface hardness.