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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… Click to show full 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.
Journal Title: Computational Intelligence
Year Published: 2021
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