Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Gajewski Jakub, Rogala Michał, Vališ David
Dyscypliny:
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Rok wydania:	2025
Wersja dokumentu:	Drukowana \| Elektroniczna
Język:	angielski
Numer czasopisma:	Pt A
Wolumen/Tom:	242
Numer artykułu:	115817
Strony:	1 - 18
Impact Factor:	5,2
Web of Science® Times Cited:	2
Scopus® Cytowania:	2
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.

Application of neural networks specific forms for estimation of crushing signal parameters of multilevel structural absorbers implemented in passive safety research