Prediction of load on the cutting tools in tunnel boring machines

excavate tunnels with a circular cross-section. TBMs can bore
through a variety of ground conditions. Tunnel boring machines are
used as an alternative to drilling methods. TBMs have the
advantages of limiting the disturbance to the surrounding ground.
Predicting the load on cutting tools in tunnel boring machines is
important for the mining process. The article presents a proposal for
a method of forecasting the load on mining machinery tools. This
paper presents current trends in hard rock tunnelling, including the
directions of research on automated excavation processes. Particular
emphasis is put on the aspects of predicting load variations in the
cutterhead tools, which is of vital importance for machine power
selection and mining process control, among others.
The problem of predicting the load and wear of excavation tools
plays an important role in designing and maintaining cutterheads.
The effective monitoring of the operation of multi-tool cutterhead
knives and their replacement time depend on correct identification
of the type and condition of the excavating tool cutting insert.
A neural network with a multilayer perceptron structure was used as
a prediction tool. The concept of this network type is based on the
arrangement of neurons in successive layers. This neural network
type is treated as an input-output model. Its parameters include
weights and threshold values.