# Optimization of crashworthiness of wide crash box under axial quasi-static and dynamic impact for electric vehicle using machine learning
> Djamaluddin F.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105039878876
Jurnal / Konferensi: Mechanics of Advanced Materials and Structures
Tahun terbit: 2026
DOI: https://doi.org/10.1080/15376494.2026.2669358
ISSN: 15376494
Kuartil SJR: Q1
Citations: 0

## Authors
- Djamaluddin F.

## Abstract
This study presents a comprehensive multi-objective optimization of a multi-cell crash box under quasi-static and dynamic axial loading conditions. Finite element analysis is conducted to investigate the crashworthiness indicators: total energy absorption (TEA), specific energy absorption (SEA), and peak crushing force (PCF). Extreme Gradient Boosting (XGBoost) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were developed to maximize SEA and minimize PCF. The results demonstrate that the optimized multi-cell configurations achieve superior energy absorption capacity. The proposed framework provides a computationally efficient approach for the crashworthiness design of crash boxes for electric vehicle applications.

## Keywords
- Crashworthiness
- Electric vehicle
- Automotive engineering
- Computer science
- Crash
- Motor vehicle crash
- Engineering
- Vehicle safety
- Electric machine
- Artificial intelligence
- Machine learning
- Vehicle dynamics
- Structural engineering
- Vehicle engineering
- Work (physics)

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.