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Generating Component Designs for an Improved NVH Performance by Using an Artificial Neural Network as an Optimization Metamodel

Wysocki, Timo von ORCID iD icon; Rieger, Frank; Tsokaktsidis, Dimitrios Ernst; Gauterin, Frank ORCID iD icon


In modern vehicle development, suspension components have to meet many boundary conditions. In noise, vibration, and harshness (NVH) development these are for example eigenfrequencies and frequency response function (FRF) amplitudes. Component geometry parameters, for example kinematic hard points, often affect multiple of these targets in a non intuitive way. In this article, we present a practical approach to find optimized parameters for a component design, which fulfills an FRF target curve. By morphing an initial component finite element model we create training data for an artificial neural network (ANN) which predicts FRFs from geometry parameter input. Then the ANN serves as a metamodel for an evolutionary algorithm optimizer which identifies fitting geometry parameter sets, meeting an FRF target curve. The methodology enables a component design which considers an FRF as a component target. In multiple simulation examples we demonstrate the capability of identifying component designs modifying specific eigenfrequency or amplitude features of the FRFs.

Verlagsausgabe §
DOI: 10.5445/IR/1000133692
Veröffentlicht am 08.06.2021
DOI: 10.3390/designs5020036
Zitationen: 1
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2411-9660
KITopen-ID: 1000133692
Erschienen in Designs
Verlag MDPI
Band 5
Heft 2
Seiten Article: 36
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 03.06.2021
Schlagwörter component design; optimization; artificial neural network; morphing; FEM; vibration; acoustics; NVH; boundary conditions; simulation
Nachgewiesen in Dimensions
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