  This paper introduces a simple framework for accurately predicting the
fatigue lifetime of notched components by employing various machine learning
algorithms applied to a wide range of materials, loading conditions, notch
geometries, and fatigue lives. Traditional approaches for this task have relied
on empirical relationships involving one of the mechanical properties, such as
stress, strain, or energy. This study goes further by exploring which
mechanical property serves as a better measure. The key idea of the framework
is to use the gradient of the mechanical properties (stress, strain, and
energy) to distinguish between different notch geometries. To demonstrate the
accuracy and broad applicability of the framework, it is initially validated
using isotropic materials, subsequently applied to samples produced through
additive manufacturing techniques, and ultimately tested on carbon fiber
laminated composites. The research demonstrates that the gradient of all three
measures can be effectively employed to estimate fatigue lifetime, with
stress-based predictions exhibiting the highest accuracy. Among the machine
learning algorithms investigated, Gradient Boosting and Random Forest yield the
most successful results. A noteworthy finding is the significant improvement in
prediction accuracy achieved by incorporating new data generated based on the
Basquin equation.
