Machine learning techniques to classify drill bit wear and rock strength from drilling

<p dir="ltr"><b>Numerical data used for the study</b></p><ol><li>Numerical data was generated using a lumped parameter axial-torsional model by Gupta and Wahi, 2016.</li><li>The files in the Matlab and Maple files folder contain the results of the simulations and the scripts used to generate them, respectively.</li></ol><p dir="ltr"><b>Information about experimental drilling</b></p><ol><li>The measurements from experimental drilling can be found in the files named JB_TEST1_trialx.</li><li>The details of the instrumentation are found here.</li><li>The images of rocks are rocks used for experimental drilling.</li><li>The images of drill bits show the state of the drill bit at the specified time mentioned in the name.</li><li>The drill bit log shows which rocks were used for drilling and the drill bit condition for the operation.</li></ol><p dir="ltr"><b>Script used for processing data</b></p><ol><li>Copy of MEng Main is the Jupyter notebook used to import and pre-process the data and feed the features through the machine learning models.</li></ol><p><br></p>