Abstract：
The accurate prediction of disc cutter wear is essential to prevent excessive wear of disc cutters on the full-face tunnel boring machine and to reduce costs on cutter tool repair and maintenance. Based on the abrasive wear mechanism and the disc cutter force model, this paper derives a mathematical model between the radial wear of the disc cutter and the penetration, installation radius, rotation speed of the cutter head; and it establishes a prediction model for the radial wear of the disc cutters based on the BP neural network, which is optimized by using the genetic algorithm (GA) and particle swarm optimization (PSO), respectively. The prediction model is verified by a TBM tunnelling project case in Nortwest China, and the results show that the radial wear of the disc cutters can be predicted more accurately by using the thrust of the TBM, the installation radius of the disc cutters and the rotation speed of the cutter head as the input nodes of the neural network, with higher prediction accuracy. Finally, it designs the human-computer interface for the GA-BP neural network based prediction model by using a hybrid programming method of MATLAB and C#.
CHEN Yukun1 GUAN Huisheng1 ZHOU Lei2 LIU Cheng1
.Research on the Wear Prediction of Disc Cutters Based on BP Neural Network[J] MODERN TUNNELLING TECHNOLOGY, 2021,V58(5): 78-84
2021, Vol. 58 