Optimizing crash box design for enhanced vehicle safety: A Gaussian process regression approach - Jarwadi, Maulana Hayu; Jusuf, Annisa; Palar, Pramudita Satria; Gunawan, Leonardo (BKS-TM Indonesia, 2023)
Transportation is a fundamental human need that permits mobility, allowing for economic, social, and cultural advancements. The crash box structure, a vital component for crashworthiness, is designed to deform during car collisions to absorb considerable impact energy plastically. This design concept attempts to prevent potential injuries to drivers and passengers. Crash boxes are constantly being developed to optimize their configuration to match needed crashworthiness features. The mean crushing force (Pm), crushing force efficiency (CFE), and specific energy absorption (SEA) are the critical crashworthiness variables that are calculated. With the advancement of data modeling tools, better crash box design can be achieved by revealing recognizable patterns and trends inherent in the data. To that end, this research uses LS-DYNA software to perform a numerical simulation of a hexagonally designed crash box that impacted under axial loading. The simulation includes the variation of thickness and perimeter of the crash box. Following the simulation, the data is modeled using the Gaussian Process Regression (GPR), often known as Kriging. This modeling approach yields surface and contour plots that show the impacts of thickness and perimeter on crashworthiness performance. The results show that the Pm value increases as the structure's thickness and perimeter increase. In comparison, the SEA and CFE values increase when the structure's thickness increases while the perimeter decreases. In summary, the simulation results show that the crash box with a thickness of 3 mm and a perimeter of 400 mm has the highest Pm value. In contrast, the crash box with a thickness of 3 mm and a perimeter of 120 mm achieves the highest CFE and SEA values.