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.

References

[1] Wang J-M, Xue Y, Xiao J, et al.. Diffusion characteristics of airflow and CO in the dead-end tunnel with different ventilation parameters after tunneling blasting [J]. ACS Omega, 2023, 8(39): 36269-36283

[2] Li X-J, Zhang H-K, Bai Y-F, et al.. Factor analysis and numerical simulation of rock breaking efficiency of TBM deep rock mass based on orthogonal design [J]. Journal of Central South University, 2022, 29(4): 1345-1362

[3] Zhou J, Zhang Y-L, Li C-Q, et al.. Rockburst prediction and prevention in underground space excavation [J]. Underground Space, 2024, 14: 70-98

[4] Pan Y, Wu M-Z, Zhang L-M, et al.. Time series clustering-enabled geological condition perception in tunnel boring machine excavation [J]. Automation in Construction, 2023, 153: 104954

[5] Cheng Y-L, Zhong J, Mei Y-B, et al.. Rock fragmentation under different installation polar angles of TBM disc cutters [J]. Journal of Central South University, 2017, 24(10): 2306-2313

[6] Liu S-Y, Du C-L, Cui X-X, et al.. Characteristics of different rocks cut by helical cutting mechanism [J]. Journal of Central South University of Technology, 2011, 18(5): 1518-1524

[7] Wang S-F, Li X-B, Yao J-R, et al.. Experimental investigation of rock breakage by a conical pick and its application to non-explosive mechanized mining in deep hard rock [J]. International Journal of Rock Mechanics and Mining Sciences, 2019, 122: 104063

[8] Yasar S, Yilmaz A O. Rock cutting tests with a simple-shaped chisel pick to provide some useful data [J]. Rock Mechanics and Rock Engineering, 2017, 50(12): 3261-3269

[9] Yasar S, Yilmaz A O. Drag pick cutting tests: A comparison between experimental and theoretical results [J]. Journal of Rock Mechanics and Geotechnical Engineering, 2018, 10(5): 893-906

[10] Akintunde I B, Lindsay E E, Olakanmi E O, et al.. Performance evaluation and failure analysis of conical picks used in a Botswana (Ngwato) mine [J]. Engineering Failure Analysis, 2023, 143: 106872

[11] Wang X, Su O, Gao K-D. Evaluation of normal forces at varying cutting angles of conical picks on sandstones [J]. Arabian Journal of Geosciences, 2021, 14(7): 539

[12] Liu S-Y, Cui X-X, Du C-L, et al.. Method to determine installing angle of conical point attack pick [J]. Journal of Central South University of Technology, 2011, 18(6): 1994-2000

[13] Cheluszka P, Mikula E S, Mikula J. Theoretical consideration of fatigue strengthening of conical picks for rock cutting [J]. Tunnelling and Underground Space Technology, 2022, 125: 104481

[14] Evans I. A theory of the cutting force for point-attack picks [J]. International Journal of Mining Engineering, 1984, 2(1): 63-71

[15] Roxborough F F, Liu Z C. Theoretical considerations on pick shape in rock and coal cutting [C]. Underground Operators Conference, 1995, Australia, Australasian Institute of Mining and Metallurgy, Carlton, VIC (Australia)

[16] Goktan R M. A suggested improvement on Evans’ cutting theory for conical bits [C]. Proceedings of Fourth Symposium on Mine Mechanization Automation, 1997, Brisbane, Australia, Colorado School of Mines

[17] Bao R H, Zhang L C, Yao Q Y, et al.. Estimating the peak indentation force of the edge chipping of rocks using single point-attack pick [J]. Rock Mechanics and Rock Engineering, 2011, 44(3): 339-347

[18] Gao K-D, Du C-L, Jiang H-X, et al.. A theoretical model for predicting the peak cutting force of conical picks [J]. Frattura Ed Integrità Strutturale, 2013, 8(27): 43-52

[19] Li X, Wang S, Ge S, et al.. A theoretical model for estimating the peak cutting force of conical picks [J]. Experimental Mechanics, 2018, 58(5): 709-720

[20] Goktan R M, Gunes N. A semi-empirical approach to cutting force prediction for point-attach picks [J]. Journal of the Southern African Institute of Mining and Metallurgy, 2005, 105(4): 257-263

[21] Bilgin N, Demircin M A, Copur H, et al.. Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results [J]. International Journal of Rock Mechanics and Mining Sciences, 2006, 43(1): 139-156

[22] Tiryaki B, Boland J N, Li X S. Empirical models to predict mean cutting forces on point-attack pick cutters [J]. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(5): 858-864

[23] WANG Zhi-wen, ZENG Qing-liang, LU Zhen-guo, et al. Numerical simulation of conical pick cutting arc rock plate fracture based on ANSYS/LS-DYNA [J]. Advances in Materials Science and Engineering, 2020: 6563520. DOI: https://doi.org/10.1155/2020/6563520.

[24] Li H S, Liu S Y, Xu P P. Numerical simulation on interaction stress analysis of rock with conical picks [J]. Tunnelling and Underground Space Technology, 2019, 85: 231-242

[25] Palaniappan S K, Pal S K, Chinnasamy M, et al.. Efficiency of rock cutting and wear behavior of coated bits via lab-scale linear rock-cutting machine: Experimental approach [J]. International Journal of Geomechanics, 2023, 23(2): 06022041

[26] Fan Q-X, Chen C-X, Zhang Q-Q, et al.. A dynamic analysis of a conical pick during rock-cutting process based on the smooth finite element method [J]. Wear, 2023, 512–513: 204523

[27] Li D-Y, Liu Z-D, Xiao P, et al.. Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization [J]. Underground Space, 2022, 7(5): 833-846

[28] Li D-Y, Liu Z-D, Armaghani D J, et al.. Novel ensemble intelligence methodologies for rockburst assessment in complex and variable environments [J]. Scientific Reports, 2022, 12(1): 1844

[29] Li D-Y, Liu Z-D, Armaghani D J, et al.. Novel ensemble tree solution for rockburst prediction using deep forest [J]. Mathematics, 2022, 10(5): 787

[30] Liu Z-D, Li D-Y. Intelligent hybrid model to classify failure modes of overstressed rock masses in deep engineering [J]. Journal of Central South University, 2023, 30(1): 156-174

[31] Liu Z-D, Jahed A D, Fakharian P, et al.. Rock strength estimation using several tree-based ML techniques [J]. Computer Modeling in Engineering & Sciences, 2022, 133(3): 799-824

[32] Liu Z-D, Li D-Y, Liu Y-P, et al.. Prediction of uniaxial compressive strength of rock based on lithology using stacking models [J]. Rock Mechanics Bulletin, 2023, 2: 100081

[33] Asl P F, Monjezi M, Hamidi J K, et al.. Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm [J]. Engineering with Computers, 2018, 34(2): 241-251

[34] Fathipour-Azar H. Mean cutting force prediction of conical picks using ensemble learning paradigm [J]. Rock Mechanics and Rock Engineering, 2023, 56(1): 221-236

[35] Zhou J, Dai Y, Tao M, et al.. Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm [J]. Results in Engineering, 2023, 17: 100892

[36] Copur H, Bilgin N, Tunçdemir H, et al.. A set of indices based on indentation tests for assessment of rock cutting performance and rock properties [J]. Journal of the South African Institute of Mining and Metallurgy, 2003, 103: 589-599

[37] Su O, Ali A N. Numerical simulation of rock cutting using the discrete element method [J]. International Journal of Rock Mechanics and Mining Sciences, 2011, 48(3): 434-442

[38] Wang X, Su O, Wang Q-F, et al.. Effect of cutting depth and line spacing on the cuttability behavior of sandstones by conical picks [J]. Arabian Journal of Geosciences, 2017, 10(23): 525

[39] Yasar S. Determination of optimum rock cutting data through single pick cutting tests [J]. Géotechnique Letters, 2019, 9(1): 8-14

[40] Yasar S, Yilmaz A O. Vertical rock cutting rig (VRCR) suggested for performance prediction of roadheaders [J]. International Journal of Mining, Reclamation and Environment, 2019, 33(3): 149-168

[41] Polat C. Roadheader performance prediction using portable linear cutting machine [J], 2015

[42] Wang X, Wang Q-F, Liang Y-P. Effects of cutting parameters affecting on specific cutting energy of conical picks [J]. Journal of China Coal Society, 2018, 43(2): 563-570 (in Chinese)

[43] Liao J-B, Li X-B, Wang S-F, et al.. Effects of cut depth and cut spacing on a conical pick in rock cutting [J]. Rock and Soil Mechanics, 2023, 44(4): 1009-1021 (in Chinese)

[44] Chen P-T, Li F, Wu C-W. Research on intrusion detection method based on Pearson correlation coefficient feature selection algorithm [J]. Journal of Physics: Conference Series, 2021, 1757(1): 012054

[45] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting [J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139

[46] Friedman J H. Greedy function approximation: A gradient boosting machine [J]. The Annals of Statistics, 2001, 29(5): 1189-1232

[47] Ke G-L, Meng Q, Finley T, et al.. LightGBM: A highly efficient gradient boosting decision tree [C]. Proceedings of the 31st International Conference on Neural Information Processing Systems. December 4–9, 2017, 2017, Long Beach, California, USA, ACM 3149-3157

[48] Chen T-Q, Guestrin C. XGBoost: A scalable tree boosting system [C]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, San Francisco California USA, ACM 785-794

[49] Prokhorenkova L, Gusev G, Vorobev A, et al.. CatBoost: Unbiased boosting with categorical features [EB/OL], 2017 arXiv: 1706.09516

[50] Dorogush A V, Ershov V, Gulin A. CatBoost: Gradient boosting with categorical features support [EB/OL], 2018 arXiv: 1810.11363

[51] Zhang W-G, Wu C-Z, Zhong H-Y, et al.. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization [J]. Geoscience Frontiers, 2021, 12(1): 469-477

[52] Zhou J, Qiu Y, Zhu S, et al.. Estimating TBM advance rate in hard rock condition using XGBoost and Bayesian optimization [J]. Underground Space, 2020, 6(5): 506-515

[53] Cui J-X, Yang B. Survey on Bayesian optimization methodology and applications [J]. Journal of Software, 2018, 29(10): 3068-3090

[54] PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: Machine learning in python [J]. The Journal of Machine Learning Research, 2011, 122825–122830.

[55] Taylor K E. Taylor diagram primer [J], 2005 1-4

RIGHTS & PERMISSIONS

Central South University