Intelligent evaluation of mean cutting force of conical pick by boosting trees and Bayesian optimization

Abstract

Conical picks are important tools for rock mechanical excavation. Mean cutting force (MCF) of conical pick determines the suitability of the target rock for mechanical excavation. Accurate evaluation of MCF is important for pick design and rock cutting. This study proposed hybrid methods composed of boosting trees and Bayesian optimization (BO) for accurate evaluation of MCF. 220 datasets including uniaxial compression strength, tensile strength, tip angle (θ), attack angle, and cutting depth, were collected. Four boosting trees were developed based on the database to predict MCF. BO optimized the hyper-parameters of these boosting trees. Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models. The hybrid model composed of BO and categorical boosting (BO-CatBoost) was the best. Its outstanding performance was attributed to its advantages in dealing with categorical features (θ included 6 types of angles and could be considered as categorical features). A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF. Moreover, the influences of the input parameters on the model and their relationship with MCF were analyzed. When θ increased from 80° to 90°, it had a significant contribution to the increase of MCF.

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