Predicting the Building Stone Cutting Rate Based on Rock Properties and Device Pullback Amperage in Quarries Using M5P Model Tree |
| |
| Authors: | S Najmedin Almasi Raheb Bagherpour Reza Mikaeil Yilmaz Ozcelik Hamid Kalhori |
| |
| Institution: | 1.Department of Mining Engineering,Isfahan University of Technology,Isfahan,Iran;2.Department of Mining and Metallurgical Engineering,Urmia University of Technology,Urmia,Iran;3.Department of Mining Engineering,Hacettepe University,Beytepe, Ankara,Turkey |
| |
| Abstract: | One of the key parameters that affect the selection of equipment and the cost estimation of dimension stone quarries is the rock cutting rate or production rate. In this study, the M5P tree algorithm is used to determine the relationship between the hard rock sawability and its factors especially the physical and mechanical characteristics of rock. To achieve the research goal, a variety of eleven types of hard dimension stone were selected and nine major physical and mechanical characteristics of rock including uniaxial compressive strength, Young’s modulus, Brazilian tensile strength, equivalent quarts content, grain size, Mohs hardness, point load test, density and P-wave velocity of these samples were evaluated. The cutting rate of diamond wire for all of the Workpiece was measured at different pullback amperage with a fully instrumented cutting platform in laboratory. All operational parameters of cutting process were entirely controlled. Thus, a database containing 99 datasets was provided and it has been used for analyses. The obtained results from the pruned and unpruned tree models showed a significant relationship between cutting rate and its factors. In the end, the results of M5P tree method were compared with statistical analyses (i.e., linear and nonlinear regression). The coefficient of determination be equal with 0.92, 0.86, 0.77 and 0.63 for unpruned tree, pruned tree, linear and nonlinear regression method respectively. This comparison showed that the both method of M5P tree technique have a better performance in predicting the cutting rate rather than the statistical regression methods. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
