Improving Joint Strength Prediction by Accounting for Defects in Tensile Testing Specimens

Abstract

Adhesively bonded joints are widely used in various industries, highlighting the importance of understanding their behavior. While fracture toughness tests provide valuable insights, they are complex and time-consuming. As a result, engineers often rely on tensile test data alone, considering its simplicity and practicality. However, the prevalence of defects in tensile specimens can lead to an underestimation of adhesive elongation at break. Tensile specimens distribute loads over a larger area, making them more susceptible to premature failure caused by localized flaws compared to bond line loads. To address this limitation and improve accuracy, this study proposes a novel approach. Tensile tests are conducted to determine the distribution of elongation at break, and inverse modeling using User Material (UMAT) in ABAQUS is employed to acquire the defect distribution in the specimens for different mesh sizes. Single Lap Joint (SLJ) tension tests are performed to compare force-displacement results from experimental data with simulation. The results highlight the significance of considering proper flaw distribution in the adhesive layer. By incorporating the defect distribution obtained from inverse modeling, the prediction of failure displacement can be enhanced compared to using adhesive with uniform properties based solely on average tensile data.