Predictive modelling

Random forest

Vibration fatigue

Engineering

Aerospace engineering

Experimental data

Statistics

Materials science

Fatigue testing

Computer science

Parametric model

Aerospace

Mechanical engineering

Alloy

Mathematics

Work (physics)

Composite material

Parametric statistics

Structural engineering

Titanium alloy

Artificial neural network

Machine learning

International Journal of Fatigue

In the aerospace engineering, many metal parts produced using Additive Manufacturing (AM) technique often bear cyclic loadings, so the fatigue failures of AM alloy parts become very common phenomena. In this work, a new method is proposed to investigate the fatigue damage behavior of AM aerospace alloys, in which the continuum damage mechanics (CDM) theory and machine learning (ML) models are effectively combined. At first, the CDM models with AM effects are theoretically presented, and the fatigue lives are then numerically computed. In total, over 500 sets of data are acquired and employed to train ML models. After that, the two commonly-used ML models including artificial neural network (ANN) and random forest (RF) are implemented to carry out fatigue life prediction. Furthermore, the predicted data are compared with the experimental fatigue life, and the proposed novel method is verified. At last, the parametric studies are discussed to investigate the variation trend of predicted performance and fatigue life with the important parameters of ML models.

A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing