Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm

• DOI: 10.1016/j.rineng.2023.100892
• Publication Date: 2023-01-13T18:29:21Z
• Authors: Jian Zhou; Yong Dai; Ming Tao; Manoj Khandelwal; Mingsheng Zhao; Qiyue Li
• Keywords: Technology; Mean cutting force; T; Machine learning; Salp swarm algorithm; 0211 other engineering and technologies; 02 engineering and technology; Rock cutting; Conical pick; Random forest

Conical picks are widely used as cutting tools in shearers and roadheaders, and the mean cutting force (MCF) is one of the important parameters affecting conical pick performance. As MCF depends on a number of parameters and due to that the existing empirical and theoretical formulas and numerical modelling are not sufficient enough and reliable to predict MCF in a proficient manner. So, in this research, a novel intelligent model based on a random forest algorithm (RF) and a heuristic algorithm called the salp swarm algorithm (SSA) have been applied to determine the optimal hyper-parameters in RF, and root mean square error is used as a fitness function. A total of 188 data samples including 50 rock types and seven parameters (tensile strength of the rock σt, compressive strength of the rock σc, cone angle θ, cutting depth d, attack angle γ, rake angle α and back-clearance angle β) were collected to develop an SSA-RF model for mean cutting force prediction. The prediction results of the SSA-RF model were compared with seven influential formulas and four classical models, such as random forest, extreme learning machine, support vector machine and radial basis function neural network. The mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Pearson correlation coefficient (R2) were employed as evaluation indexes to compare the capability of different predicting models. The MAE (0.509 and 0.996), RMSE (0.882 and 1.165), MAPE (0.146 and 0.402) and R2 (0.975 and 0.910) values between measured and predicted MCF for training and testing phases of the SSA-RF model clearly demonstrate the superiority in prediction compared to the other tools. A sensitivity analysis has also been performed to understand the influence of each input parameter on MCF, which indicates that σc, d and σt are the most important variables for MCF prediction.