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Analysis of the Triggering Mechanism of the Square Thin-Walled Absorber

Rogala Michał, Gajewski Jakub

Advances in Science and Technology. Research Journal

2023

Vol. 17, no 6

206--216

The aim of the research is the analysis of thin-walled aluminum profiles with embossed crush initiator. Samples with square cross-section loaded dynamically were studied until the complete loss of velocity by the tup. The numerical analyses were based on an elastic-plastic material model. The material properties of AA 6063 aluminum were derived from own tests performed on a tensile machine. The analyses were conducted using the numerical method (Abaqus CAE). Using a dynamic testing machine, the obtained numerical data were verified on the basis of models showing the best improvement in crush efficiency indicators. In the experimental study, high-speed camera images were used to identify the forming plastic hinges. Based on the obtained results of experimental and numerical analysis, crush efficiency indicators were determined and compared. It was determined that the use of a passive energy absorber increases the efficiency of the crushing force by around 50%, in addition, the correct location of the crush initiator allows to gain 15%. The results of the study showed that proper placement of the crush initiator decrease PCF while increasing MCF.

Identification of crashworthiness indicators of column energy absorbers with triggers in the form of cylindrical embossing on the lateral edges using artificial neural networks

Ferdynus Mirosław, Gajewski Jakub

Eksploatacja i Niezawodność

2022

Vol. 24, no. 4

805--821

The paper presents the possibility of neural network application in order to identify the most advantageous design variants of column energy absorbers in terms of the achieved energy absorption indicators. Design variants of the column energy absorber made of standard thin-walled square aluminium profile with triggers in the form of four identical cylindrical embossments on the lateral edges were considered. These variants differ in the diameter of the trigger, its depth and position. The geometrical parameters of the trigger are crucial for the energy absorption performance of the energy absorber. The following indicators are studied: PCF (Peak Crushing Force), MCF (Mean Crushing Force), CLE (Crash Load Efficiency), SE (Stroke Efficiency) and TE (Total Efficiency). On the basis of numerical studies validated by experimentation, a neural network has been created with the aim of predicting the above-mentioned indices with an acceptable error for an energy absorber with the trigger of specified geometrical parameters and position. The paper demonstrates that the use of an effective multilayer perceptron can successfully speed up the design process, saving time on multivariate time-consuming analyses.