Fatigue dataset of hybrid non-toughened and toughened epoxy adhesives.

In this article, four different structural epoxy adhesives such as SPABOND™ 820HTA (non-toughened), SPABOND™ 840HTA (toughened) adhesives, and their two hybrid combinations are fabricated using a manual mixing method. Quasi-static tensile experiments are conducted at standardized and high strain rates using ASTM D638-22 Type II specimens to investigate the strain rate effects on the tensile properties. Tensile-tensile fatigue experiments are performed using ASTM D638-22 Type I and Type II specimens to evaluate the impact of specimen geometry and toughening on fatigue life. The digital image correlation technique is utilized to obtain full-field strain data in these experiments. Technical data analysis, plotting, smoothing, filtering, and averaging are carried out using Origin ProⓇ and MATLAB R2021bⓇ. The obtained S-N curve data can be used to develop fatigue failure criteria and predict the behavior of wind turbine blade adhesive joints through finite element modeling.

Dataset for the hybrid non-toughened and toughened epoxy adhesive properties.

In this article, the manufacturing and toughening effects on the material properties of epoxy adhesives used in wind turbine rotor blades are presented. Different adhesive materials are developed by combining SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) adhesives with the machine and manual mixing methods. Firstly, the manufacturing quality are compared between the two methods, in terms of void percentage and void volume using micro-computed tomography. Dynamical Mechanical analysis, uniaxial tensile testing, V-notch shear testing and single-edge-notch beam testing are carried out to evaluate the manufacturing and toughening effects. In these experiments, the digital image correlation technique is exploited to obtain the displacement and strain data. Origin ProⓇ and MATLAB R2021bⓇ are utilized for technical data analysis, plotting, smoothing, filtering, and averaging. The obtained data could be used to select the adhesive material based on the strength and stiffness requirements, develop failure criteria, and predict the thick adhesive joint behavior by finite element modeling.