Multiobjective optimization on adhesive bonding of aluminum‐carbon fiber laminate

Eduardo Valdés; J. D  Mosquera‐Artamonov; Celso Cruz; Jaime Taha-Tijerina

doi:https://doi.org/10.1111/coin.12432

Multiobjective optimization on adhesive bonding of aluminum‐carbon fiber laminate

Eduardo Valdés, J. D Mosquera‐Artamonov, Celso Cruz, Jaime Taha-Tijerina

Departamento de Ingeniería Mecánica y Eléctrica

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

2 Citations (Scopus)

Abstract

This work presents a multi‐objective optimization methodology to find compromise adhesive bonding schemes that possess a great shear load and a low percentage of remaining fiber in the bonding. The joining overlap, adhesive type, and prior surface finishing are considered. The Pareto front of the multi‐objective response surface model is found with an Nondominated Sorting Genetic algorithm. The adhesive bonding factors are the adhesive (MP55420, Betamate 120, and DC‐80), the surface finishing (acetone cleaned and atmospheric plasma), and the overlapping distance of the test coupons.

Original language	English
Pages (from-to)	621-634
Number of pages	14
Journal	Computational Intelligence
Volume	37
Issue number	1
Early online date	7 Jan 2021
DOIs	https://doi.org/10.1111/coin.12432
Publication status	Published - Feb 2021

Bibliographical note

Funding Information:
Authors thank the Consejo Nacional de Ciencia y Tecnologia (CONACYT, Mexico) for many years of support. J. D. Mosquera-Artamonov, E. Valdés thank CONACYT for their respective PhD. granted scholarships. Hugo Gamez-Cuatzin and Saul D. Santillan-Gutierrez for the technical support.

Publisher Copyright:
© 2021 Wiley Periodicals LLC.

All Science Journal Classification (ASJC) codes

Computational Mathematics
Artificial Intelligence

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https://doi.org/10.1111/coin.12432

Cite this

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abstract = "This work presents a multi‐objective optimization methodology to find compromise adhesive bonding schemes that possess a great shear load and a low percentage of remaining fiber in the bonding. The joining overlap, adhesive type, and prior surface finishing are considered. The Pareto front of the multi‐objective response surface model is found with an Nondominated Sorting Genetic algorithm. The adhesive bonding factors are the adhesive (MP55420, Betamate 120, and DC‐80), the surface finishing (acetone cleaned and atmospheric plasma), and the overlapping distance of the test coupons.",

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Multiobjective optimization on adhesive bonding of aluminum‐carbon fiber laminate

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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