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A New Semi-Theoretical Disc Cutter Performance Prediction Model Considering Curvature Radius

  • Ting Ren,
  • Jin Yu,
  • Hailong Zhang,
  • Longchuan Deng,
  • Seisuke Okubo,
  • Khalid Elbaz

摘要

Abstract

The disc cutter is the core component of a tunnel boring machine (TBM), whose performance is related to the safety and efficiency of tunnel construction. The accurate performance prediction of the disc cutter is crucial. In this paper, a new rolling force and normal force prediction model is proposed. The model considers the effect of the curvature radius and is closer to the real situation. Combined with an empirical formula, the model calculation is simpler, faster and more accurate. The model is based on five parameters: disc diameter D, cutting depth P, curvature radius r, linear force coefficient \(\delta\) δ and rock strength UCS. Data for the study were collected and analyzed from 13 groups of cutting experiments that used four types of cutters and 10 types of rock samples. The experimental results show a positive correlation between the ratio of the disc cutter rolling force FR to normal force FN and the ratio of the square root of the cutting depth P to disc cutter diameter D; the specific cutting force SE and uniaxial compressive strength UCS; FN and \(P \cdot UCS \cdot \sqrt {Dd}\) P · U C S · Dd (d is circle diameter); and the non-dimensional SE'' and cutter spacing S/cutting depth P. A high correlation is found between the linear force coefficient \(\delta\) δ and the mean cutting depth, as well as the disc cutter diameter and the rock strength. Thus, the empirical formula for \(\delta = (0.18172UCS + 7.83979)\frac{P}{D}\) δ = ( 0.18172 U C S + 7.83979 ) P D is established. Subsequently, the model, is used to predict the rock-cutting force for different rock types, disc cutter diameters, and curvature radii. A strength correction factor \(\mu\) μ is introduced into the model to reduce the error caused by the difference in rock strength and further improve its accuracy. Compared with other typical models, as well as a performance evaluation, the proposed model achieves a higher prediction accuracy, proving that it is more reliable.