Free vibration analysis and optimal design of adhesively bonded double-strap joints by using artificial neural networks

Guo, Qianqiao; Wang, Suian

doi:10.1590/1679-78255878

Cited by 7 publications

(1 citation statement)

References 36 publications

(34 reference statements)

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“…Another recent interesting approach is to build a surrogate model of an assembled system to predict the behavioral change of the final product as a function of changes in any of its components' properties. In [14], the authors built a surrogate model using artificial neural networks to predict the natural frequency of an adhesive bonded double-strap joint, as well as the loss factor. The surrogate model was later leveraged to optimize the joint mass.…”

Section: Introductionmentioning

confidence: 99%

Functional Parametric Elasto-Dynamics for Efficient Multicomponent Design

Ghnatios

Mordillat

et al. 2022

Computation

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In industrial settings, engineering products are often divided into separate components for detailed conception. They often require iterative corrections between different designers/teams to optimize the final product with all components assembled into a system. This article proposes a surrogate modeling approach with functional descriptions of parts in the model and aims to accelerate the design and optimization phase in real projects. The approach is applied to a vibration problem of a two-component plate structure, where the model estimates the dynamic behavior of the assembled system when only the properties of each individual part are available. A database is built using high-fidelity numerical simulations, and neural-network-based regressions provide reliable predictions on unseen data.

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Section: Introductionmentioning

confidence: 99%