Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP)

Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It highly depended on interactional effects material properties geometric definitions constituents, which very complicated. The existing studies in identifying fracture modes based either experiments or finite element analysis are often prohibitively time computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) Genetic Programming (GP) understand effect physical attributes single lap joints. A dataset 150 adhesive joint samples generated using LHS, including different combinations adherend adhesive’s thicknesses. ratios calculated Strain Energy Release Rate (SERR) outputs embedded Linear Elastic Fracture Mechanics (LEFM), validated experimental tests. Finally, GP model developed trained provide extracted explicit expression evaluating early-state against design variables.