Machine learning/finite element analysis - A collaborative approach for predicting the axial impact response of adhesively bonded joints with unique sandwich composite adherends

Fatemeh Mottaghian; Farid Taheri

doi:10.1016/j.compscitech.2023.110162

Machine learning/finite element analysis - A collaborative approach for predicting the axial impact response of adhesively bonded joints with unique sandwich composite adherends

Fatemeh Mottaghian, Farid Taheri

Mathematics

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Original language	English
Article number	110162
Journal	Composites Science and Technology
Volume	242
DOIs	https://doi.org/10.1016/j.compscitech.2023.110162
Publication status	Published - Sept 29 2023

ASJC Scopus Subject Areas

Ceramics and Composites
General Engineering

Keywords

Adhesively bonded joints
Axial impact analysis
Deep neural network
Genetic programming and algorithm
Sandwich composites

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10.1016/j.compscitech.2023.110162

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Mottaghian, F., & Taheri, F. (2023). Machine learning/finite element analysis - A collaborative approach for predicting the axial impact response of adhesively bonded joints with unique sandwich composite adherends. Composites Science and Technology, 242, Article 110162. https://doi.org/10.1016/j.compscitech.2023.110162

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keywords = "Adhesively bonded joints, Axial impact analysis, Deep neural network, Genetic programming and algorithm, Sandwich composites",

author = "Fatemeh Mottaghian and Farid Taheri",

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AU - Mottaghian, Fatemeh

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Machine learning/finite element analysis - A collaborative approach for predicting the axial impact response of adhesively bonded joints with unique sandwich composite adherends

ASJC Scopus Subject Areas

Keywords

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