Prediction of load on the cutting tools in tunnel boring machines
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
| Status: | |
| Autorzy: | Jonak Józef, Kuric Ivan, Droździel Paweł, Gajewski Jakub, Sága Milan |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2020 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 25 |
| Strony: | 444 - 452 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 5 kwietnia 2021 |
| Abstrakty: | angielski |
| A tunnel boring machine (TBM) is a machine that is used to 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. |
