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Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek’s F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young’s modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties.

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning