Determination of key parameters of Al–Li alloy adhesively bonded joints using cohesive zone model

The key parameters of the adhesive layer of a reinforcing patch are of great significance and affect the ability to suppress crack propagation in an Al–Li alloy patch-reinforced structure.This paper proposes a method to determine the key parameters of the adhesive layer of adhesively bonded joints in the Al–Li alloy patch-reinforced structure.A zero-thickness cohesive zone model(CZM)was selected to simulate the adhesive layer’s fracture process,and an orthogonal simulation was designed to compare against the test results.A three-dimensional progressive damage model of an Al–Li alloy patch-reinforced structure with single-lap adhesively bonded joints was developed.The simulation’s results closely agree with the test results,demonstrating that this method of determining the key parameters is likely accurate.The results also verify the correctness of the cohesive strength and fracture energy,the two key parameters of the cohesive zone model.The model can accurately predict the strength and fracture process of adhesively bonded joints,and can be used in research to suppress crack propagation in Al–Li alloy patch-reinforced structures.

Damage Failure Analysis of Z-Pins Reinforced Composite Adhesively Bonded Single-Lap Joint

In order to study themechanical properties of Z-pins reinforced laminated composite single-lap adhesively bonded joint under un-directional static tensile load,damage failure analysis of the joint was carried out bymeans of test and numerical simulation.The failure mode and mechanism of the joint were analyzed by tensile failure experiments.According to the experimental results,the joint exhibits mixed failure,and the ultimate failure is Z-pins pulling out of the adherend.In order to study the failure mechanism of the joint,the finite element method is used to predict the failure strength.The numerical results are in good agreement with the experimental results,and the error is 6.0%,which proves the validity of the numerical model.Through progressive damage failure analysis,it is found that matrix tensile failure of laminate at the edge of Z-pins occurs first,then adhesive layer failure-proceeds at the edge of Z-pins,and finally matrix-fiber shear failure of the laminate takes place.With the increase of load,the matrix-fiber shear failure expands gradually in the X direction,and at the same time,the matrix tensile failure at the hole edge gradually extends in different directions,which is consistent with the experimental results.

Predicting fatigue life of automotive adhesive bonded joints:a data-driven approach using combined experimental and numerical datasets

The majority of vehicle structural failures originate from joint areas.Cyclic loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure design.The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries.Therefore,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry.In this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)results.Different machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII joints.After the feature engineering and tuned process of the ML models,the preferable model using the Gaussian process regression algorithm is established,fed with eight input parameters,namely thicknesses of the substrates,line forces and bending moments of the adhesive bonded joints obtained from FEA.The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation.It demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.