Enhancing the performance of a additive manufactured battery holder using a coupled artificial neural network with a hybrid flood algorithm and water wave algorithm

Yildiz B. S.

MATERIALS TESTING, cilt.66, sa.10, ss.1557-1563, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 10
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1515/mt-2024-0217
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Sayfa Sayıları: ss.1557-1563
  • Anahtar Kelimeler: additive manufacturing, artificial neural network, battery holder, electric vehicle, flood algorithm, PLA, shape design optimization, simulated annealing algorithm
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This research is the first attempt in the literature to combine design for additive manufacturing and hybrid flood algorithms for the optimal design of battery holders of an electric vehicle. This article uses a recent metaheuristic to explore the optimization of a battery holder for an electric vehicle. A polylactic acid (PLA) material is preferred during the design of the holder for additive manufacturing. Specifically, both a hybrid flood algorithm (FLA-SA) and a water wave optimizer (WWO) are utilized to generate an optimal design for the holder. The flood algorithm is hybridized with a simulated annealing algorithm. An artificial neural network is employed to acquire a meta-model, enhancing optimization efficiency. The results underscore the robustness of the hybrid flood algorithm in achieving optimal designs for electric car components, suggesting its potential applicability in various product development processes.