Fracture energy assessment of adhesives Part II: Is GIIc an adhesive material property? (A neural network analysis)

Abstract

Different joint configurations were considered to analyze the fracture toughness of adhesives. However, based on the literature, fracture toughness depends on various factors including geometrical parameters, material properties, and testing conditions. In Part I of this study, the tensile fracture toughness (GIc) of adhesives was assessed. The present Part II of this research deals with the influencing factors in shear fracture energy of adhesive materials. The main goal of this study is to find the role of the effective parameters on mode II fracture toughness (GIIc) of adhesives. The rational connection between these parameters and the shear fracture energy is also obtained. The interaction between the effective variables is also investigated. To attain this goal, the artificial neural network (ANN) technique was conducted on over 45 values of GIIc already obtained and reported by different authors using different joint geometries and adhesive materials. The influence of the variables was obtained, and the least important variables were detected and omitted from the model. The effect of different parameters and their interaction on the obtained GIIc were also analyzed. The results indicated that GIIc is remarkably sensitive to the alteration of the adherend Young's modulus and its thickness, the loading rate, and the crack length. It was found that GIIc is less influenced by the adhesive stiffness. In addition, the interaction results indicated that the loading rate has the highest interaction with the other studied variables. This study enables to estimate the mode II fracture toughness of adhesive joints and also to advise a configuration to improve the shear fracture energy of a specific adhesive. Designing a test procedure to minimize the effects of substrate parameters on the obtained GIIc is also possible using the obtained results.