Deep learning-based inverse design for engineering systems: multidisciplinary design optimization of automotive brakes

The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most representative factors for evaluating braking performance. In particular, as the two performance factors have a conflicting r...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Structural and multidisciplinary optimization Jg. 65; H. 11
Hauptverfasser:	Kim, Seongsin, Jwa, Minyoung, Lee, Soonwook, Park, Sunghoon, Kang, Namwoo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022 Springer Nature B.V
Schlagworte:	Back propagation Brakes Braking Computational efficiency Computational Mathematics and Numerical Analysis Computing costs Deep learning Design optimization Drag Engineering Engineering Design Inverse design Iterative methods Multidisciplinary design optimization Performance evaluation Quadratic programming Research Paper Theoretical and Applied Mechanics Torque Deep learning Brake system Inverse design Multidisciplinary design optimization
ISSN:	1615-147X, 1615-1488
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!