Fatigue modeling using neural networks: A comprehensive review
Neural network (NN) models have significantly impacted fatigue‐related engineering
communities and are expected to increase rapidly due to the recent advancements in …
communities and are expected to increase rapidly due to the recent advancements in …
Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges
In recent years, machine learning (ML) techniques have been extensively investigated to
strengthen the understanding of the complex process dynamics underlying metal additive …
strengthen the understanding of the complex process dynamics underlying metal additive …
A novel machine learning method for multiaxial fatigue life prediction: Improved adaptive neuro-fuzzy inference system
J Gao, F Heng, Y Yuan, Y Liu - International Journal of Fatigue, 2024 - Elsevier
In this study, a neuro-fuzzy-based machine learning method is developed to predict the
multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and …
multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and …
High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach
Variations in the high cycle fatigue response of laser powder bed fusion materials can be
caused by the choice of processing and post-processing strategies. The numerous …
caused by the choice of processing and post-processing strategies. The numerous …
Prognostic modelling options for remaining useful life estimation by industry
JZ Sikorska, M Hodkiewicz, L Ma - Mechanical systems and signal …, 2011 - Elsevier
Over recent years a significant amount of research has been undertaken to develop
prognostic models that can be used to predict the remaining useful life of engineering …
prognostic models that can be used to predict the remaining useful life of engineering …
On the application of in-situ monitoring systems and machine learning algorithms for developing quality assurance platforms in laser powder bed fusion: A review
Laser powder bed fusion (LPBF) is one class of metal additive manufacturing (AM) used to
fabricate high-quality complex-shape components. This technology has significantly …
fabricate high-quality complex-shape components. This technology has significantly …
The history of fiber-reinforced polymer composite laminate fatigue
AP Vassilopoulos - International Journal of Fatigue, 2020 - Elsevier
Investigations of the fatigue performance of composite materials have accompanied their
introduction in several engineering domains since the 1950s. An abundance of publications …
introduction in several engineering domains since the 1950s. An abundance of publications …
Machine learning‐based genetic feature identification and fatigue life prediction
K Zhou, X Sun, S Shi, K Song… - Fatigue & Fracture of …, 2021 - Wiley Online Library
Considering the nonlinear relationship between variables and fatigue life and the
computational burden, a machine learning method integrating the artificial neural network …
computational burden, a machine learning method integrating the artificial neural network …
Rate-dependent multiaxial life prediction for polyamide-6 considering ratchetting: Semi-empirical and physics-informed machine learning models
The rate-dependent multiaxial fatigue life of PA6 is investigated by conducting a series of
stress-controlled multiaxial fatigue tests involving the ratchetting. Based on the obtained …
stress-controlled multiaxial fatigue tests involving the ratchetting. Based on the obtained …
[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 …