Evolution of the Fatigue Failure Prediction Process from Experiment to Artificial Intelligence: A Review - Samoilă, Cornel; Ursuțiu, Doru; Tudorache (Nistor), Iuliana (MDPI AG, 2025)
An analysis of the time evolution of fatigue break prediction shows increasingly shorter developmental stages. The experimental period was the longest; the combination of more powerful mathematical methods led to a leap in evolution and a shortening of implementation time. All fatigue rupture prediction methods have proven to have limitations due to the multitude of influencing factors and the insufficient number of practical factors considered. Recently, attempts have been made to increase prediction accuracy by combining methods based on the physical mechanisms of the fatigue failure process with data-driven methods assisted by artificial intelligence. We attempt to present this evolution herein. There are several methods of review suitable for analyzing this subject: systematic, semi-systematic, and integrative. From these, a combination of semi-systematic and integrative was chosen precisely because the two methods complement each other.
Keywords: failure; prediction; S-N curves; machine learning; deep learning; neural networks; hybrid fatigue models