Estimation of Cutting Forces Acting on Conical Cutters Based on Rock Properties and the Contact Area Between Cutter Tip and Rock

Details

• Personal Author:
  Moradian O ; Morshedlou A ; Rostami J
• Description:
  This study aimed to investigate various models for predicting the cutting force in rock-cutting processes by conical tools or pick cutters. For this purpose, a database of rock cutting forces was established by utilizing full-scale cutting tests and analysis of the measured forces as a function of input parameters, such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), spacing, and the contact area between the pick cutter and rock. The study outlined the procedure for calculating the area of contact (AOC) between a conical pick cutter and rock surface, utilizing key parameters, including tip radius, tip cone angle, depth of penetration, and a fixed 45 degrees attack angle. Six categories of regression models were employed, encompassing conventional regression models (linear, log-log linear, and polynomial), regularized models (LASSO, Ridge, and Elastic-Net), tree-based models (decision tree, random forest, and extreme gradient boost), complex models (SVR and ANN), probabilistic (Bayesian linear and Gaussian process), and Ensemble models (stacking and voting). As a result, the stacking technique within the ensemble models exhibited superior performance in predicting cutting forces, showing the highest Coefficient of Determination (R2) score and the lowest Mean Absolute Error (MAE). To enhance the interpretability of the results, particularly from the ensemble methods, Explainable Artificial Intelligence (XAI) techniques, such as individual conditional expectation (ICE), partial dependence plots (PDPs), and SHAP (SHapley Additive exPlanations) analysis, were applied. The research offers reasonable prediction of cutting force based on specified parameters. This empowers engineers to make well-informed decisions regarding cutter selection, machine specifications, and cutting strategies, resulting in more efficient rock-cutting operations. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Area Of Contact Artificial Intelligence Author Keywords: Cutting Force Compressive Strength Conical Picks Cutting Tools Explainable AI Machine Learning Rock Mechanics Tensile Strength
• ISSN:
  0723-2632
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; Denver Region [OSHA]
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  57
• Issue:
  12
• NIOSHTIC Number:
  nn:20070155
• Citation:
  Rock Mech Rock Eng 2024 Dec; 57(12):10307-10328
• Email:
  amidmorshedlou@mines.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  Colorado School of Mines
• Peer Reviewed:
  True
• Start Date:
  20190913
• Source Full Name:
  Rock Mechanics and Rock Engineering
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:cce496efc6c9af3ad561d566a08906b34eb3f7472b27e022d0797bc3b48adb82580501d761b946980c49082647074fcf90f93eadd45d5151e41db12ccb98da49
• Download URL:
  https://stacks.cdc.gov/view/cdc/208431/cdc_208431_DS1.pdf
• File Type:
  [PDF - 2.61 MB ]