An improved J-integral based adhesive joint fatigue life estimation method for automotive structural durability analysis

ABSTRACT

To estimate the fatigue life of lightweight automotive structures in compliance with top-down design principles, there is a need for an accurate adhesive joint fatigue life prediction method that is compatible with industrial finite element modeling practices. This paper proposes a method to split the analytical J-integral solution based on the fracture mode of joints. The split mode I and II J-integrals correspond to the opening mode and sliding mode of the bonded joint, respectively. By splitting the J-integral and introducing the concept of the mixed mode ratio, an improved approach for estimating adhesive joint fatigue life is presented, considering the influence of loading modes. The proposed fatigue life prediction method is validated through fatigue tests on a sub-component level bonded structure. The results of the validation demonstrate that the split J-integral analytical solution method, considering the mixed mode ratio, provides better predictions compared to its predecessor approach.

KEYWORDS:

• Adhesive joint
• fatigue life prediction
• J-integral
• mixed mode ratio

Nomenclature

$t_{\mathit{i}}$substrate thicknesses

$E_1,E_2$substrate’s Young’s modulus

$E_3$initial adhesive Young’s modulus

$F_i$line force of cross - section i

$M_i$bending moment of cross - section i

$V_i$ shear force of cross - section i

$I_i$second moment of area for section i

$x_i$coordinates axes of the sandwich model

$j_{\mathit{total}}$total J - integral

$J_{\mathit{F}}$J integral of the component on the lien force

$J_{\mathit{M}}$J integral of the component on the bending moment

$J_{\mathit{I}},J_{\mathit{II}}$J integral of the components of mode I and mode II

Acknowledgments

We also highly appreciate the support from Jiangsu Industrial Technology Research Institute and Material Academy, Jitri.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation [52205377]; the Key Basic Research Project of Suzhou [#SJC2022029]; Jiangsu Material Big Data Public Technical Service Platform [BM2021007].