In the present work, a rate-dependent cohesive zone model for the fracture of polymeric interfaces is presented. Inverse calibration of parameters for such complex models through trial and error is computationally tedious due to the large number of parameters and the high computational cost associated. The obtained parameter values are often non-unique and the calibration inherits higher uncertainty when the available experimental data is limited. To alleviate these difficulties, a Bayesian calibration approach is used for the proposed rate-dependent cohesive zone model in this work. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced and it is approximated as a Gaussian process. The improvement in the model predictions following the introduction of a discrepancy function is demonstrated justifying the need for a discrepancy term. Finally, the overall uncertainty of the model is quantified in a predictive setting and the results are provided as confidence intervals. A sensitivity analysis is also performed to understand the effect of the variability of the inputs on the nature of the output.

翻译：本文提出了一种用于描述聚合物界面断裂的速率相关内聚力模型。由于此类复杂模型参数众多且计算成本高昂，通过试错法进行参数反标定在计算上极为繁琐。当可用实验数据有限时，所获得的参数值往往不唯一，且标定过程中存在较高的不确定性。为克服这些困难，本文针对所提出的速率相关内聚力模型采用了贝叶斯标定方法。该内聚力模型同时考虑了界面上的可逆弹性滑移变形和不可逆速率相关分离滑移变形。界面不可逆张开导致的黏性耗散通过考虑应变率效应的弹-黏塑性运动学模型进行描述。为量化参数反估计中的不确定性，采用模块化贝叶斯方法对未知模型参数进行标定，同时虑及内聚力模型的参数不确定性。此外，为量化模型不确定性（如错误假设或缺失物理机制），引入了一个误差函数并将其近似为高斯过程。通过展示引入误差函数后模型预测能力的提升，论证了引入误差项的合理性。最终，在预测框架下对模型的整体不确定性进行了量化，并以置信区间的形式呈现结果。同时开展了灵敏度分析，以探究输入变量变化对输出结果特性的影响。