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Type: Theses
Title: Acoustic analysis of rock cutting process for impregnated diamond drilling
Author: Perez Ospina, Santiago
Issue Date: 2016
School/Discipline: School of Civil, Environmental and Mining Engineering
Abstract: The rock cutting industry has experienced important changes with the introduction of diamond-based drilling tools in the last few decades. Impregnated diamond (ID) bits are part of that introduction and their main use is to drill hard and abrasive rock formations. ID core drilling has emerged as the most commonly used technology employed in the advanced stages of mineral exploration. Through this technology, existing resources - mineral and energy - are expanded and greenfield exploration is carried out. As near-surface deposits are depleted, there is a global trend towards targeting deeper for exploration. Currently, in near-surface drilling, bit wear condition is determined by the experience of drilling operators –trial and error–. Although it makes the evaluation very subjective and prone to errors, it is an accepted practice. Conversely, in deep drilling, direct assessment of the bit wear condition is difficult and time consuming. Therefore, alternative techniques must be developed in order to evaluate, in real time, the wear condition of the bit and properties of the drilling medium. In this thesis, Acoustic Emission (AE) along with Measuring While Drilling (MWD) parameters are considered as an alternative technique to remotely monitor the ID bit wear condition (sharp and blunt) and rock properties (abrasivity). A series of rigorous and specialized drilling and abrasivity tests are utilised to generate the acoustic signatures with (topologically variant) and without (topologically invariant) changes in the topology of the tool cutting face. Main findings of this work are as follows: firstly, based on the step test results, linear relationships were developed that make it possible to estimate the depth of cut, weight on the bit (WOB) and torque on the bit (TOB) by simply using the time domain parameters of the AE signals. Wear tests also showed that AE amplitudes start to trend down as wear begins to accelerate. Secondly, acceptable pattern recognition rates are obtained for the majority of tool condition monitoring systems developed for predicting sharpness or bluntness of ID bits. In particular, the system composed by AErms [rms subscript] and TOB excels due to the high classification performance rates and the fewer input variables compared to other tool condition monitoring systems. Lastly, AE parameters, such as total number of events and root mean square of AE, in addition to testing parameters are found to accurately predict rock abrasivity measured via Cerchar Abrasivity Index (CAI). The importance of this index lies on: (i) the fact that ID drilling is commonly used in abrasive rock formations, and (ii) the way CAI has been defined (length of wear flat exerted on a steel pin after being scratched on one centimetre of rock surface), which intrinsically relates it to wear condition of the tool. The insights presented in this thesis open up a new promising field of study, impregnated diamond drilling using AE as an indirect technique to evaluate tool condition.
Advisor: Karakus, Murat
Xu, Chaoshui
Dissertation Note: Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 2016.
Keywords: acoustic emission
diamond drilling
bit wear
rock abrasivity
machine learning
Research by Publication
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
DOI: 10.25909/5b3ed454947f4
Appears in Collections:Research Theses

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