Multiobjective Optimization On Adhesive Bonding Of Aluminum-Carbon Fiber Laminate

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.