Multiobjective optimization on adhesive bonding of aluminum‐carbon fiber laminate. (7th January 2021)

• Record Type: Journal Article
• Authors: Valdés, Eduardo; Mosquera‐Artamonov, J. D; Cruz‐Gonzalez, Celso; Jaime Taha‐Tijerina, Jose
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 621
• Page End: 634
• Publication Date: 2021-01-07
• Subjects: adhesive bonding -- carbon fiber laminate‐6061 T6 Al -- multi‐objective optimization -- surrogate model; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12432
• Languages: English
• ISSNs: 0824-7935

Abstract

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.