Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research
Artykuł w czasopiśmie
MNiSW
5
spoza listy
| Status: | |
| Autorzy: | Gajewski Jakub, Rogala Michał, Vališ David |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | Pt A |
| Wolumen/Tom: | 242 |
| Numer artykułu: | 115817 |
| Strony: | 1 - 18 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| In case of classic thin-walled energy absorber the energy dissipation is not sufficiently controlled during a collision. A significant part of the columns does not participate in plastic deformation sufficiently. This is due to a lack of proper crush initiators for the formation of subsequent joints. The research problem of the article is to presents a method of applying artificial intelligence methods for determining the optimal parameters of crush initiators, in order to make proper use of plastic deformation zones and improve the efficiency of energy absorption. The methodology is based on the artificial neural network models made possible to select the best and optimal design parameters of multilevel crush initiators. The results of numerical tests were verified on the test bench. The values of all crashworthiness indicators improved, the PCF has been reduced up to 30% from for certain geometric parameters of the multilevel crush initiator. |
