Shear strength estimation of adhesively bonded cylindrical components under static loading using the genetic algorithm approach

The bonding strength of adhesives is influenced by the interference fit, bonding clearances, surface roughness, adherent and temperature. Since all these factors affect the strength of the adhesively joined parts, the effect of these parameters was investigated experimentally by the researchers. The present paper describes the use of stochastic search process that is the basis of the genetic algorithm (GA), in developing shear strength estimation of adhesively bonded cylindrical components. Non-linear estimation models are developed using GA. Developed models are validated with experimental data. Genetic algorithm shear strength estimation model (GASSEM) is used to estimate the shear strength of the adhesively bonded tubular joint for the surface roughness, bonding clearance, interference fit, temperature and adherent, such as steel, bronze and aluminum material.