نرخ برش

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s F-abrasiveness factor ( ) is one of the most common indices to assess rock abrasivity.  is the function of three rock parameters including the Brazilian tensile strength ( ), median grain size ( ), and equivalent quartz content ( ). By considering its formulation, it has been revealed that the coefficient of each parameter is equal, which is not correct because each parameter plays a different role in the rock abrasion process. This work aims to modify the original form of  by introducing three correction factors. To calculate these correction factors, an integrated method based on a combination of the statistical analysis and probabilistic simulation is applied to a dataset of 15 different andesite rocks. Based on the results obtained, the values of -0.36, 0.3, and -0.89 are suggested as the correction factors of ,  and , respectively. The performance of the modified Schimazek’s F-abrasiveness factor ( ) is checked not only by the wear rate of diamond wire but also by the cutting rate of the wire sawing process of Andesite rocks. The results obtained indicate that the wear rate and cutting rate of andesite rocks can be reliably predicted using . However, it should be noted that this work is a preliminary one on the limited rock types and further studies are required by incorporating different rock types.

One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the maximum energy consumption of the gang saw during the process of soft dimensional rocks with the help of an intelligent optimization model such as random non-linear techniques, i.e. the Hybrid ANFIS-DE and Hybrid ANFIS-PSO algorithms based upon 4 physical and mechanical parameters including uniaxial compressive strength, Mohs hardness, Schimazek’s F-abrasiveness factors, Young modulus, and an operational characteristic of the machine, i.e. production rate. During this research work, 120 samples are tested on 12 carbonate rocks. The maximum energy consumption of the cutting machine during this work is measured and used as a modeling output for evaluating the performance of cutting machine. Also meta-heuristic algorithms including DE and PSO algorithms are used for training the Adaptive Neural Fuzzy Inference System (ANFIS). In addition, the PSO algorithm has a higher ability in terms of model output and performance indices and has a superiority over the differential evolution algorithm. Furthermore, comparison between the measured datasets with the ANFIS-DE and ANFIS-PSO models indicate the accuracy and ability of the ANFIS-PSO model in predicting the performance of gang saw considering the machine’s properties and the cut rock.