Kinematics & Compliance Validation of a Vehicle Suspension and Steering Kinematics Optimization Using Neural Networks

Creative Commons License

Ağakişi G., ÖZTÜRK F.

Mechanika, cilt.29, sa.3, ss.243-251, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 3
  • Basım Tarihi: 2023
  • Doi Numarası: 10.5755/j02.mech.31983
  • Dergi Adı: Mechanika
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Central & Eastern European Academic Source (CEEAS), Communication Abstracts, Compendex, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.243-251
  • Anahtar Kelimeler: hardpoint optimization, neural networks, steering kinematics
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Physical and virtual K&C analyses are performed to achieve the vehicle dynamics targets by finding the optimum variables such as the position of hardpoints or stiffnesses of bushings. However, finding appropriate design variables that meet all the aims is challenging. This paper evaluates a hardpoint optimization approach to attain suspension K&C characteristic objectives with the design of experiments, neural networks, and genetic algorithm, based on a reference compact-sized prototype vehicle. The MBD model correlation is provided to optimize the hardpoints to improve the vehicle's steering kinematics concerning Ackerman error and camber angle variation that are out of target in baseline suspension. The results showed that NN based optimization strategy to define the hardpoints has significantly improved targeted characteristics compared to conventional response surface methods in the limited design space.