Shahrood University of Technology Journal of Mining and Environment 2251-8592 14 4 2023 10 01 Improvement of Drill Bit-Button Performance and Efficiency during Drilling: an application of LSTM Model to Nigeria Southwest Mines 1121 1139 2905 10.22044/jme.2023.13068.2372 EN Babatunde Adebayo Department of Mining Engineering, Faculty of Engineering and Engineering Technology, Federal University of Technology Akure, Nigeria. Blessing Olamide Taiwo Department of Mining Engineering, Faculty of Engineering and Engineering Technology, Federal University of Technology Akure, Nigeria. Mining Engineer, HNF Global Resources Limited, Akoko Edo, Nigeria. BUSUYI THOMAS AFENI Department of Mining Engineering, Faculty of Engineering and Engineering Technology, Federal University of Technology Akure, Nigeria. Aderoju Oluwadolapo Raymond Geology Department, Federal University of Technology Akure, Nigeria. Joshua Oluwaseyi Faluyi Mining Engineer, Dangote Cement Plc Ogun state, Nigeria. Journal Article 2023 05 04 The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations. https://jme.shahroodut.ac.ir/article_2905_599e836a532a097a7ba713237d66e118.pdf