Crashworthiness evaluation and optimization of full polypropylene sandwich tubes under low-velocity impact based on machine learning algorithms

Ma W., Almasifar N., Amini R., Ourang A., Mahariq I., Alhoee J.

Structures, cilt.60, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.istruc.2024.105901
  • Dergi Adı: Structures
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Bio-inspired Sandwich Tube, Energy Absorption, Machine Learning, MLP, Optimization, Polypropylene
  • Çukurova Üniversitesi Adresli: Evet

Özet

The performance of traditional single and multi-cell tubular energy absorbers was enhanced through the design of a polypropylene tube inspired by biological structures such as horsetail and tendons. In this study, the energy-absorbing behavior of these tubes was investigated, and the influence of parameters like thickness and height was examined. Using the LS-DYNA finite element code, various specimens with different thicknesses, heights, and core counts were modeled. Crashworthiness predictions for these designs were made using machine learning, specifically the Multi-layer Perceptron (MLP) algorithm. The optimal sandwich tube, in terms of Performance Criteria Factor (PCF) and Specific Energy Absorption (SEA), was identified using the non-dominated sorting genetic algorithm II (NSGA-II). The specimen featuring three core tubes, 1.1 mm thickness, and 82 mm height was determined to have the most efficient performance. Its force-displacement curve was produced using MLP methods and the Bayesian Regularization training algorithm. Lastly, the crashworthiness behavior of this optimized bio-inspired sandwich tube was validated through experimental testing and finite element modeling.