Experimental and computational investigation of adhesive joints subjected to impact loads

Abstract

In recent years, the strength of the adhesive joints under impact loading has become important because their use expands to the aircraft and automobile industries. The impact strength of adhesively bonded cylindrical components is affected by the various factors such as the type of adherent, surface roughness, adhesive thickness, and operating temperature. In this study, the effect of three surface roughness values on impact strength is experimentally investigated and the results are discussed. Results showed that optimum surface roughness values were found in the range Ra = 1.5 to 2.5 µm. The highest impact strength was obtained in stainless steel adherent while the lowest one was obtained in aluminium specimens. Materials with higher free surface energy had higher impact strength values. The effect of adhesive thickness on impact strength was dependent on the type of adherent. Additionally, an impact strength prediction model was developed using Artificial Neural Network (ANN). The impact strength prediction results showed that developed artificial neural network model was convenient and powerful tool for impact strength estimation of adhesively bonded joints.