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.
Valdés, E., Mosquera-Artamonov, J. D., Cruz-Gonzalez, C., & Jaime Taha-Tijerina, J. (2021). Multiobjective optimization on adhesive bonding of aluminum-carbon fiber laminate. Computational Intelligence, 37(1), 621–634. https://doi.org/10.1111/coin.12432