Fatigue strength estimation of adhesively bonded tubular joint using genetic algorithm approach

The bonding strength of adhesives is influenced by many factors such as, the surface roughness, bonding clearances, interference fit, temperature, and material of the joining parts, etc. Since all these factors affect the strength of the adhesively joined parts, the effects of these parameters need to be investigated. The present paper describes the use of stochastic search process that is the basis of Genetic Algorithm (GA), in developing fatigue strength estimation of adhesively bonded cylindrical components. Nonlinear estimation models are developed using GA. Developed models are validated with experimental data. Genetic Algorithm Fatigue Strength Estimation Model (GAFSEM) is developed to estimate the fatigue strength of the adhesively bonded tubular joint using several adherent materials, such as steel, bronze and aluminum materials.