Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
H Wang, B Li, J Gong, FZ Xuan - Engineering Fracture Mechanics, 2023 - Elsevier
Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …
mechanical structures. Although data-driven approaches have been proven effective in …
[HTML][HTML] Machine learning aided nanoindentation: A review of the current state and future perspectives
The solution of instrumented indentation inverse problems by physically-based models still
represents a complex challenge yet to be solved in metallurgy and materials science. In …
represents a complex challenge yet to be solved in metallurgy and materials science. In …
The potency of defects on fatigue of additively manufactured metals
Given their preponderance and propensity to initiate fatigue cracks, understanding the effect
of processing defects on fatigue life is a significant step towards the wider application of …
of processing defects on fatigue life is a significant step towards the wider application of …
Deep learning-based heterogeneous strategy for customizing responses of lattice structures
Designing lattice structures with tunable mechanical behavior for multi-functional
applications is of great significance. However, the inverse design of lattice structure for the …
applications is of great significance. However, the inverse design of lattice structure for the …
Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods
The current paper presents a machine learning method based on artificial neural network
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
Multi-objective Bayesian optimization accelerated design of TPMS structures
Triply periodic minimal surface (TPMS) is an effective filling architecture in porous ceramic
artificial bone for its great bionic characteristics and self-supporting properties. However …
artificial bone for its great bionic characteristics and self-supporting properties. However …
High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X
L Lei, B Li, H Wang, G Huang, F Xuan - International Journal of Fatigue, 2024 - Elsevier
Uncertainties in fatigue life of laser powder bed fusion (L-PBF) additively manufactured parts
arise from microstructural heterogeneities and randomly dispersed defects generated during …
arise from microstructural heterogeneities and randomly dispersed defects generated during …
Multi-scale and multi-layer perceptron hybrid method for bearings fault diagnosis
S Xie, Y Li, H Tan, R Liu, F Zhang - International Journal of Mechanical …, 2022 - Elsevier
The progressive growth in demand and requirements for bearing problem diagnostics in the
operating segment of trains has resulted from an increase in train speed and the …
operating segment of trains has resulted from an increase in train speed and the …
[HTML][HTML] Machine learning for predicting fatigue properties of additively manufactured materials
Fatigue properties of materials by Additive Manufacturing (AM) depend on many factors
such as AM processing parameter, microstructure, residual stress, surface roughness …
such as AM processing parameter, microstructure, residual stress, surface roughness …
Vacancy dependent mechanical behaviors of high-entropy alloy
An abundance of defects would be inevitably generated during manufacturing and service in
high-entropy alloys (HEAs). However, the mechanical properties of the damaged HEAs with …
high-entropy alloys (HEAs). However, the mechanical properties of the damaged HEAs with …