Objective and automated calibration of progressive damage models for finite element simulation of fiber reinforced

The determination of suitable input parameters for the computational analysis of progressive damage in composites is mostly based on trial-and-error attempts leading to subjective models with limited general use. This study explores the application of genetic algorithms for an objective and automated calibration of continuum damage models in finite element simulations. The general applicability and robustness are demonstrated in three case studies containing carbon and glass fiber-reinforced laminates subjected to progressive tensile and compressive fracture tests. The load-displacement curves of these fracture tests build the basis for optimizing the input parameters of the damage models. The validation in independent load cases and a good correlation of damage patterns between experimental observations and simulations show that the optimized parameters can produce accurate and physically meaningful results. The optimization process requires up to 250 finite element simulations which is significantly less than comparable data-driven approaches incorporating machine learning.

Automated summary

This study explores the application of genetic algorithms for the objective and automated calibration of damage models in composite materials. The method is demonstrated to be generally applicable and robust through three case studies involving carbon and glass fiber-reinforced laminates. The load-displacement curves of fracture tests are used to optimize the input parameters of the damage models. The optimized parameters produce accurate and physically meaningful results, as validated in independent load cases and through good correlation with experimental observations.