Predictability of Different Machine Learning Approaches on the Fatigue Life of Additive-Manufactured Porous Titanium Structure

Abstract

Due to their outstanding mechanical properties and biocompatibility, additively manufactured titanium porous structures are extensively utilized in the domain of medical metal implants. Implants frequently undergo cyclic loading, underscoring the significance of predicting their fatigue performance. Nevertheless, a fatigue life model tailored to additively manufactured titanium porous structures is currently absent. This study employs multiple linear regression, artificial neural networks, support vector machines, and random forests machine learning models to assess the impact of structural and mechanical factors on fatigue life. Four standard maximum likelihood models were trained, and their predictions were compared with fatigue experiments to validate the efficacy of the machine learning models. The findings suggest that the fatigue life is governed by both the fatigue stress and the overall yield stress of the porous structures. Furthermore, it is recommended that the optimal combination of hyperparameters involves setting the first hidden layer of the artificial neural network model to three or four neurons, establishing the gamma value of the support vector machine model at 0.0001 with C set to 30, and configuring the n_estimators of the random forest model to three with max_depth set to seven.