  Fatigue properties of additively manufactured (AM) materials depend on many
factors such as AM processing parameter, microstructure, residual stress,
surface roughness, porosities, post-treatments, etc. Their evaluation
inevitably requires these factors combined as many as possible, thus resulting
in low efficiency and high cost. In recent years, their assessment by
leveraging the power of machine learning (ML) has gained increasing attentions.
Here, we present a comprehensive overview on the state-of-the-art progress of
applying ML strategies to predict fatigue properties of AM materials, as well
as their dependence on AM processing and post-processing parameters such as
laser power, scanning speed, layer height, hatch distance, built direction,
post-heat temperature, etc. A few attempts in employing feedforward neural
network (FNN), convolutional neural network (CNN), adaptive network-based fuzzy
system (ANFS), support vector machine (SVM) and random forest (RF) to predict
fatigue life and RF to predict fatigue crack growth rate are summarized. The ML
models for predicting AM materials' fatigue properties are found intrinsically
similar to the commonly used ones, but are modified to involve AM features.
Finally, an outlook for challenges (i.e., small dataset, multifarious features,
overfitting, low interpretability, unable extension from AM material data to
structure life) and potential solutions for the ML prediction of AM materials'
fatigue properties is provided.

Materials Science