Empirical models to predict mean cutting forces on point-attack pick cutters

Abstract

Since conical pick cutting is a complex process of multi-factor coupling effects, theoretical model construction for cutting force prediction is a quite difficult task. In this paper, various novel intelligent models based on chaos-optimized slime mould algorithm (COSMA) and random forest (RF) are proposed for this task. In the proposed COSMA-RF methods, the chaos algorithms with the ergodicity and randomness are introduced to chaotically determine the initial position to form a COSMA, and the SMA and COSMA are used to tune the hyperparameters of RF and mean square error are assigned as a fitness function. Consequently, 205 data samples having seven variables (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) and one output parameter peak cutting force (PCF) are collected from previous literature. Additionally, the performance of optimal COSMA-RF models is comprehensively compared with the existing theoretical formulae and four common machine algorithms, namely RF, extreme gradient boosting, extreme learning machine and back propagation neural network. The results indicate that Logistic map optimized SMA (LSMA), Sine map optimized SMA (SINSMA) and Sinusoidal map optimized SMA (SSMA) have better convergence ability and accuracy compared with original SMA. LSMA-RF, SINSMA-RF and SSMA-RF models yield better PCF prediction performance compared with theoretical formulae and common machine algorithms. Furthermore, sensitive analysis shows σt, σc, d and β are significantly sensitive to PCF.