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

Abstract

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, the cost 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 plants prior to reaching a decision for the 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 can be estimated reliably by the ANN models which also may be considered as a tool in designing of natural stone processing plants based on rock surface hardness.