Physics-informed machine learning and its structural integrity applications: state of the art
The development of machine learning (ML) provides a promising solution to guarantee the
structural integrity of critical components during service period. However, considering the …
structural integrity of critical components during service period. However, considering the …
Probabilistic physics-guided machine learning for fatigue data analysis
Abstract A Probabilistic Physics-guided Neural Network (PPgNN) is proposed in this paper
for probabilistic fatigue SN curve estimation. The proposed model overcomes the limitations …
for probabilistic fatigue SN curve estimation. The proposed model overcomes the limitations …
Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning
The probabilistic fatigue properties of additively manufactured (AM) Ti-6Al-4V using
selective laser melted (SLM) process is analyzed considering the effects of process …
selective laser melted (SLM) process is analyzed considering the effects of process …
In-situ SEM investigation and modeling of small crack growth behavior of additively manufactured titanium alloy
X Wang, Y Zhao, L Wang, L Wei, J He… - International Journal of …, 2021 - Elsevier
The fatigue crack growth of wire and arc additive manufactured Ti-6Al-4V alloys is
investigated using in-situ SEM fatigue testing. The small crack initiation and propagation …
investigated using in-situ SEM fatigue testing. The small crack initiation and propagation …
Model averaging and probability of detection estimation under hierarchical uncertainties for Lamb wave detection
Existing quantification models using Lamb waves are generally data-driven models, and the
model choice can have a significant impact on the quantification results and the probability …
model choice can have a significant impact on the quantification results and the probability …
Physics-guided mixture density networks for uncertainty quantification
This paper proposes a Physics-guided Mixture Density Network (PgMDN) model for
uncertainty quantification of regression-type analysis. It integrates a Mixture Density Network …
uncertainty quantification of regression-type analysis. It integrates a Mixture Density Network …
The effect of grain size and anomalous shape on low cycle fatigue of nickel-based superalloy at elevated temperature
The effect of grain size and shape on low cycle fatigue of a nickel-based superalloy at
elevated temperature was investigated. Fatigue testing was performed using GH4742 …
elevated temperature was investigated. Fatigue testing was performed using GH4742 …
Piecewise stochastic rainflow counting for probabilistic linear and nonlinear damage accumulation considering loading and material uncertainties
A new framework is proposed for probabilistic fatigue life prediction considering
randomness from both loadings and material properties. Piecewise stochastic rainflow …
randomness from both loadings and material properties. Piecewise stochastic rainflow …
Using probabilistic neural networks for modeling metal fatigue and random vibration in process pipework
MS Nashed, MS Mohamed, OT Shady… - Fatigue & Fracture of …, 2022 - Wiley Online Library
Many experiments are usually needed to quantify probabilistic fatigue behavior in metals.
Previous attempts used separate artificial neural network (ANN) to calculate different …
Previous attempts used separate artificial neural network (ANN) to calculate different …
Probabilistic information fusion with point, moment and interval data in reliability assessment
This study presents a general framework for probabilistic information fusion with point,
moment, and interval data based on the principle of maximum relative entropy. Two types of …
moment, and interval data based on the principle of maximum relative entropy. Two types of …