Improving the fatigue crack growth prediction by accurate calibration of the constitutive material parameters

This study examines the influence of material parameter selection on fatigue crack growth (FCG) predictions for Grade 2 titanium alloy, 18Ni300 maraging steel and AA7050-T6 aluminium alloy. Material parameters were identified using a genetic algorithm to best fit numerical and experimental monotonic/cyclic curves. FCG simulations employed a node release approach, assuming the cyclic plastic deformation as the main damage mechanism. Results show that under constant amplitude loading, including monotonic tensile data in parameter identification offers minimal benefit. However, for variable amplitude loading, incorporating monotonic behaviour increased the overload affected zone, approaching the experimental results. Key factors influencing delayed crack growth retardation after an overload include plasticity-induced crack closure, crack tip blunting, and residual stresses. The dominant mechanism depends on material strength, with residual stresses being key in high-strength materials, while crack tip blunting and residual plastic deformation are more prominent in softer materials. These findings highlight the importance of selecting proper material parameters for accurate FCG predictions, especially under variable amplitude loading.