A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures

An Improved Creep-Fatigue Life Model Involving the Cyclic Softening/Hardening and Stress Relaxation Effect

(2021) Xiao_Cheng Zhang et al.   JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME

A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing

(2021) Rui Liu et al.   The International Journal of Advanced Manufacturing Technology

The effect of defect population on anisotropic fatigue resistance of selective laser melted AlSi10Mg alloy

(2021) Z.K. Wu et al.   INTERNATIONAL JOURNAL OF FATIGUE

A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

(2021) Xiao-Cheng Zhang et al.   INTERNATIONAL JOURNAL OF FATIGUE

Bayesian-based probabilistic fatigue crack growth evaluation combined with machine-learning-assisted GPR

(2020) Dianyin Hu et al.   ENGINEERING FRACTURE MECHANICS

The effect of manufacturing defects on the fatigue life of selective laser melted Ti-6Al-4V structures

(2020) Y.N. Hu et al.   MATERIALS & DESIGN

Coupling physics in machine learning to predict properties of high-temperatures alloys

(2020) Jian Peng et al.   npj Computational Materials

Predicting strain-induced martensite in austenitic steels by combining physical modelling and machine learning

(2020) Wangzhong Mu et al.   MATERIALS & DESIGN

A machine-learning fatigue life prediction approach of additively manufactured metals

(2020) Hongyixi Bao et al.   ENGINEERING FRACTURE MECHANICS

Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration

(2020) Sen Liu et al.   Applied Materials Today

Notch effect on structural strength of components at elevated temperature under creep, fatigue and creep-fatigue loading conditions: phenomenon and mechanism

(2019) Jian-Guo Gong et al.   JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME

Use of the Wilshire equation to correlate and extrapolate creep rupture data of Incoloy 800 and 304H stainless steel

(2019) Vito Cedro III et al.   MATERIALS AT HIGH TEMPERATURES

Prediction of long-term creep life of 9Cr–1Mo–V–Nb steel using artificial neural network

(2019) Tuo Liang et al.   INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING

Using deep neural network with small dataset to predict material defects

(2018) Shuo Feng et al.   MATERIALS & DESIGN

A modified strain energy density exhaustion model for creep–fatigue life prediction

(2016) Run-Zi Wang et al.   INTERNATIONAL JOURNAL OF FATIGUE

Experimental and numerical creep–fatigue study of Type 316 stainless steel failure under high temperature LCF loading condition with different hold time

(2015) R. Hormozi et al.   ENGINEERING FRACTURE MECHANICS

Creep–fatigue interaction behavior of 316LN austenitic stainless steel with varying nitrogen content

(2015) G.V. Prasad Reddy et al.   MATERIALS & DESIGN

Relations between fatigue strength and other mechanical properties of metallic materials

(2014) J. C. Pang et al.   FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES

Stacking fault energy in austenitic steels determined by usingin situX-ray diffraction during bending

(2014) D. Rafaja et al.   JOURNAL OF APPLIED CRYSTALLOGRAPHY

Larson–Miller Constant of Heat-Resistant Steel

(2013) Manabu Tamura et al.   METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE

The development of microstructural damage during high temperature creep–fatigue of a nickel alloy

(2012) L.J. Carroll et al.   INTERNATIONAL JOURNAL OF FATIGUE

LIBSVM

(2012) Chih-Chung Chang et al.   ACM Transactions on Intelligent Systems and Technology

Recent developments in stainless steels

(2009) K.H. Lo et al.   MATERIALS SCIENCE & ENGINEERING R-REPORTS

Evaluation of the creep–fatigue damage mechanism of Type 316L and Type 316LN stainless steel

(2007) Dae Whan Kim et al.   INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING

Study on creep-fatigue life prediction methods for low-carbon nitrogen-controlled 316 stainless steel (316FR)

(2006) Yukio Takahashi et al.   NUCLEAR ENGINEERING AND DESIGN