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Physics-Informed Transfer Learning Model for Fatigue Life Prediction of In718 Alloy

https://doi.org/10.2139/ssrn.4881980

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods

inferredcites →https://doi.org/10.1016/j.eswa.2020.114316openalex-snapshot

Probabilistic physics-guided machine learning for fatigue data analysis

inferredcites →https://doi.org/10.1016/j.ijfatigue.2010.09.003openalex-snapshot

A neural network approach to fatigue life prediction

inferredcites →https://doi.org/10.1016/j.ijfatigue.2022.106748openalex-snapshot

A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures

inferredcites →https://doi.org/10.1016/j.ijfatigue.2022.106851openalex-snapshot

A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying temperature

inferredcites →https://doi.org/10.1016/j.ijfatigue.2022.107086openalex-snapshot

Rate-dependent multiaxial life prediction for polyamide-6 considering ratchetting: Semi-empirical and physics-informed machine learning models

inferredcites →https://doi.org/10.1016/j.ijmecsci.2022.107185openalex-snapshot

The potency of defects on fatigue of additively manufactured metals

inferredcites →https://doi.org/10.1016/j.matdes.2009.04.003openalex-snapshot

Energy-based prediction of low-cycle fatigue life of BS 460B and BS B500B steel bars

inferredcites →https://doi.org/10.1016/j.mtcomm.2023.106697openalex-snapshot

Data-driven machine learning for alloy research: Recent applications and prospects

inferredcites →https://doi.org/10.1016/j.tafmec.2021.103196openalex-snapshot

Prediction of fatigue crack growth rate in aircraft aluminum alloys using optimized neural networks

inferredcites →https://doi.org/10.1038/s41524-022-00727-5openalex-snapshot

Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys

inferredcites →https://doi.org/10.1038/s41524-023-01000-zopenalex-snapshot

Small data machine learning in materials science

inferredcites →https://doi.org/10.1038/s41598-022-13275-1openalex-snapshot

Explainable machine learning for precise fatigue crack tip detection

inferredcites →https://doi.org/10.1108/ijsi-06-2023-0048openalex-snapshot

Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

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Fatigue residual life estimation of jib structure based on improved v‐SVR algorithm obtaining equivalent load spectrum

inferredcites →https://doi.org/10.1111/ffe.13532openalex-snapshot

Machine learning‐based genetic feature identification and fatigue life prediction

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Fatigue damage detection and risk assessment via neural network modeling of ultrasonic signals

inferredcites →https://doi.org/10.1111/ffe.13792openalex-snapshot

Fatigue life prediction in presence of mean stresses using domain knowledge‐integrated ensemble of extreme learning machines

inferredcites →https://doi.org/10.1111/ffe.13874openalex-snapshot

Application of machine learning methods in multiaxial fatigue life prediction

inferredcites →https://doi.org/10.1111/ffe.13895openalex-snapshot

Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

inferredcites →https://doi.org/10.1111/ffe.13977openalex-snapshot

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

inferredcites →https://doi.org/10.3390/ma16010046openalex-snapshot

Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors

inferredcites →https://doi.org/10.3390/ma16196606openalex-snapshot

A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset

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