A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

Tognan, A. (Università degli Studi di Udine)
Patanè, Andrea (Trinity College Dublin)
Laurenti, L. (TU Delft Team Luca Laurenti)
Salvati, Enrico (Università degli Studi di Udine)

2024

Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting advanced Bayesian Physics-guided Neural Network (B-PGNN) approaches. A B-PGNN is thereby developed to predict the fatigue failure probability of a sample containing defects, referred to a given fatigue endurance limit. In this framework, a robustly calibrated El Haddad's curve is exploited as the prior physics reinforcement of the probabilistic model, i.e., prior knowledge. Following, a likelihood function is built and the B-PGNN is trained via Bayesian Inference, thus calculating the posterior of the parameters. The arbitrariness of the choice of the related architecture is circumvented through a Bayesian model selection strategy. A case-study is analysed to prove the robustness of the proposed approach. This methodology proposes an advanced practical approach to help support the probabilistic design against fatigue failure.

Additive manufacturing
Bayesian Physics-guided Neural Networks
Defects
Fatigue strength
Uncertainty quantification

http://resolver.tudelft.nl/uuid:a23a5214-0c2a-4672-85ac-755681604b5d

https://doi.org/10.1016/j.cma.2023.116521

0045-7825

Computer Methods in Applied Mechanics and Engineering, 418

Institutional Repository

journal article

© 2024 A. Tognan, Andrea Patanè, L. Laurenti, Enrico Salvati