Enhancing a twist beam suspension system conceptual design using population-based optimization methods

Albak E. İ., Solmaz E., Öztürk F.

MATERIALS TESTING, vol.62, no.7, pp.672-677, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62 Issue: 7
  • Publication Date: 2020
  • Doi Number: 10.3139/120.111532
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Page Numbers: pp.672-677
  • Keywords: Suspension system, twist beam, optimization, genetic algorithm, Ant-Lion optimizer, Moth-Flame optimization, ANT LION, TORSION, AXLE
  • Bursa Uludag University Affiliated: Yes


Twist beam suspension systems are usually used in middle segment vehicles due to certain advantages. Researchers have presented many studies on both lightweight and functional twist beam design. In this paper, an optimization study is presented for enhancing the conceptual design of the twist beam by defining design variables along the twist beam as subject to vehicle handling conditions.Toe and camber angles are essential parameters that determine vehicle behavior during maneuvering. In this study, opposite wheel travel analysis is performed to represent maneuvering behavior. Therefore, while the optimization study is presented in the form of weight reduction, it is aimed to keep the toe and camber angles at certain intervals. Ant lion optimizer and moth-flame optimization methods, which are population-based optimization methods, are used in the optimization phase to evaluate the performance of the new algorithms as compared with genetic algorithm in terms of robustness and correctness in the case of twist beam design. A two stage approach is introduced for presenting the optimization model and analysis. In the first stage, design space is created via the Latin hypercube method; the mathematical model is obtained via the least squares regression method. Finally, the mathematical model is solved to enhance twist beam conceptual design using recently developed population based optimization algorithms.