Abstract
The drilling process is one of the significant stages of rock mechanic and mining engineering. Monitoring this operation can help researchers to have accurate perspective about drilling process, physical and mechanical features of rocks and drill bit characteristics. Drilling operation generates acoustic signals as an unwanted byproduct, which could be helpful for analyzing the nature of this process. Determining the features of rock has an undeniable importance in all downstream steps of designation in mining projects. Definition of rock properties by using direct measurement tests is a time-consuming and costly process and requires high precision. Development a novel frequency based method in this research, for determining physical and rock-mechanical features of rock could be helpful for solving problems of time consumption, cost, and precision. None of the direct and indirect conventional methods is able, to provide an accessible and efficient way (in the viewpoint of cost, time consumption, and precision) for accelerating this process. This study attempts to present mathematical relations between rock mass features and dominant acoustic frequencies emitted during drilling process using Fast Fourier Transform (FFT). A novel rotary drilling machine, with the ability to record acoustic frequencies, is designed and constructed by investigators for providing this goal. All influencing parameters of drilling regime (vertical thrust force, drill bit rotational speed, diameter and material of drill bit, sound recording ability etc.) are manageable using this machine. For drilling tests and determining physical and rock mechanical characteristics, 11 volcanic rock samples are gathered in a wide range of features. After drilling tests and by analyzing acoustic signals, five dominant frequencies are extracted for each sample. Results demonstrate almost all physical–mechanical properties of volcanic rocks (uniaxial compressive strength, tensile strength, S and P-wave velocity, porosity percentage and Schmidt Rebound Number) are predictable using diverse dominant frequencies of acoustic signals. Overall, the results present novel linear models, which are able to predict rock features.
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