Abstract
One of the most significant factors in the estimation of dimension stone quarry cost is the production rate of rock cutting machines. Evaluating the production rate of chain-saw machines is a very significant and practical issue. In this research, it has been attempted to evaluate and select the suitable working-face for a quarry by examining the maximum production rate in the Dehbid and Shayan marble quarries. For this purpose, fi eld studies were carried out which included measuring operational characteristics of the chain-saw cutting machine, the production rate and sampling for laboratory tests from seven active case studies. Subsequently, the physical and mechanical properties of rocks including: Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Los Angeles abrasion, quartz content, water absorption percentage, porosity, Schmidt hardness and grain size for all sample measurements were studied after transferring the samples to a rock-mechanics laboratory. Finally, the sawability of the quarried working-faces was evaluated using the PROMETHEE multi-criteria decision-making (MCDM) model according to the physical and mechanical properties. The results of the study indicated that the number 1 and 5 working-faces from the Dehbid and Shayan quarries are the most suitable working-faces in terms of production rate with the maximum recorded production values (4.95 and 3.1 m2 /h), and with net fl ow rates (2.67 and -0.36) respectively.
Highlights
The production rate of stone cutting machines plays a key role in dimension stone quarries when considering the final costs of extracting a stone block
Attempts were done to assess the cutting capability of dimension stones extracted by a chain saw cutting machine in the Dehbid and Shayan quarries
The cutting capability of the seven faces included in this study were graded according to physical and mechanical characteristics including: uniaxial compressive strength of the stone, Brazilian tensile strength, Los Angeles abrasion, quartz percentage, water absorption percentage, porosity, Schmidt hardness and granule size by using a PROMETHEE decision model
Summary
It is certain that in the coming years, the use of chainsaws will be a complete alternative to the diamond wire cutting method. Mohammadi et al (2018) performed their study on modeling and predicting the production rate of chainsaw cutting machines by way of intelligent neural networks To this end, first the laboratory test parameters on calcium carbonate stone were investigated and the operating parameters of the machine, arm angle, saw speed and machine speed were taken into account. Operational characteristics of machines (arm angle, saw speed and machine speed) as well as three important physical and mechanical characteristics (uniaxial compressive strength, Los Angeles abrasion test and Schmidt hammer hardness) were taken into account as input data and the production rate was considered as output data. In the end, according to the input and output currents and the obtained net current, the faces were ranked by cutting-ability with a chainsaw cutting machine
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