Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design

This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted G...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mechanics based design of structures and machines Ročník 53; číslo 8; s. 5822 - 5849
Hlavní autoři: Gomes, Guilherme Ferreira, Bendine, Kouider, Pereira, Joao Luiz Junho
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis 03.08.2025
Témata:
artificial intelligence
formula SAE
metamodel
Multi-objective optimization
regression
ISSN:1539-7734, 1539-7742
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted Genetic Algorithm II, Multi-objective Lichtenberg Algorithm, Multi-objective Sunflower Optimization, and Multi-objective Particle Swarm Optimization-aiming to minimize chassis mass and maximize stiffness. The results show that AI-driven metamodeling significantly reduces computational cost, cutting optimization time by over 99%, while maintaining accuracy comparable to direct Finite Element simulations. This work provides a framework for enhanced automotive and structural optimization.
ISSN:1539-7734
1539-7742
DOI:10.1080/15397734.2025.2476041