  Composite interfaces are commonly simulated by cohesive zone models with the
key challenge being the calibration of interfacial parameters. A new framework
is presented in this paper to derive the characteristic of any cohesive zone
model. This approach employs the multi-island genetic algorithm to obtain the
interface parameters aligning closely with the experimental observations. The
introduced framework innovatively formulates an objective function, considering
both the congruence of the load-displacement curve and the alignment with the
failure mode of model. The framework combines machine learning and
multi-objective optimization. A method using the interface debonding length to
quantify the failure mode of the model is proposed. To demonstrate the
feasibility of the proposed framework, the newly strength-based cohesive zone
model is taken as an example, and key parameters and damage evolution are
identified accurately. The inverse algorithm is used to identify the interface
parameters of both the double cantilever beam experiment and the four-point
bending test. The robustness and accuracy of the framework are validated
through the double cantilever beam test. The findings indicate that the
numerical results align closely with the experimental data, confirming that the
interface parameters identified by the proposed framework can reproduce the
performance of the adhesive joints.
