Introducing a New Model for Prediction of Mean Cutting Forces Acting on Conical Pick Cutters

Details

• Personal Author:
  Moradian O ; Morshedlou A ; Rostami J
• Description:
  Conical pick cutters are by far the most common type of rock cutting tools used on variety of excavation equipment in mining and civil applications. Prediction of the forces acting on pick cutters when cutting rock can be an important input towards cutterhead design and performance estimation for such equipment. The cutting forces are a function of the rock strength and pick tip geometry, which is impacted by the bit wear. To develop a prediction model, a database of mean cutting force (MCF) measured in full-scale testing has been compiled and subsequently analyzed using regression methods to find the empirical equations linking MCF to the bit, cutting geometry and rock properties. Full-scale cutting tests are known to offer precise measurement of the cutting forces for a given bit geometry, cutting geometry (spacing between the cuts and depth of penetration), and combined rock mechanic characteristics of the sample; consequently, the information can be used to develop models of prediction of cutting forces. Linear and log-linear regression and tree-based machine learning models (random forest, decision tree, and extreme gradient boost) were used for the analysis of the experimental data. The results demonstrated that while using input parameters including uniaxial compressive strength (UCS), spacing, Brazilian tensile strength (BTS), penetration, and pick cutter's tip radius and tip angle, models can offer a reasonable prediction of the cutting forces. Among the models that have been examined, regression tree models, especially extreme gradient boost shows the highest coefficient of determination (R2), and the lowest mean absolute error (MAE). [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Algorithms Artificial Intelligence Author Keywords: Conical Picks Compressive Strength Cutting Tools Excavation Equipment Linear Regression Machine Learning Mean Cutting Force Prediction Rock Mechanics Tree-based Models

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• ISSN:
  0723-2632
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; OSHA Region 8
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  57
• Issue:
  3
• NIOSHTIC Number:
  nn:20068953
• Citation:
  Rock Mech Rock Eng 2024 Mar; 57(3):1695-1716
• Email:
  amidmorshedlou@mines.edu
• Federal Fiscal Year:
  2024
• 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-512:252c24bb6cf6377b1570fa3a2c34f14cb91554a5be41a7daa5a0403787246811d32a9a60f193e77bb06711cf705cbb52fde734882b39ec99840d7c08cc663486
• Download URL:
  https://stacks.cdc.gov/view/cdc/207510/cdc_207510_DS1.pdf
• File Type:
  [PDF - 6.26 MB ]