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

Machine Learning Neural-Network Predictions for Grain-Boundary Strain Accumulation in a Polycrystalline Metal

(2021) R. B. Vieira et al.   EXPERIMENTAL MECHANICS

A machine learning approach to fracture mechanics problems

(2020) Xing Liu et al.   ACTA MATERIALIA

Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning

(2020) Yue Liu et al.   ACTA MATERIALIA

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

(2020) Dianyin Hu et al.   ENGINEERING FRACTURE MECHANICS

Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach

(2020) Jingye Yang et al.   INTERNATIONAL JOURNAL OF FATIGUE

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

(2020) Wei Sai et al.   Advanced Theory and Simulations

Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring

(2020) Cemal Basaran   Entropy

A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys

(2020) Waqas Muhammad et al.   INTERNATIONAL JOURNAL OF PLASTICITY

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

(2020) Hongyixi Bao et al.   ENGINEERING FRACTURE MECHANICS

Predicting Twin Nucleation in a Polycrystalline Mg Alloy Using Machine Learning Methods

(2019) Zhounuo Tong et al.   METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE

Identification and characterization of fracture in metals using machine learning based texture recognition algorithms

(2019) Dayakar L. Naik et al.   ENGINEERING FRACTURE MECHANICS

An Entropy-Based Failure Prediction Model for the Creep and Fatigue of Metallic Materials

(2019) Jundong Wang et al.   Entropy

Low Cycle Fatigue Life Prediction Using Unified Mechanics Theory in Ti-6Al-4V Alloys

(2019) Noushad Bin Jamal M et al.   Entropy

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 machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

(2018) Andrea Rovinelli et al.   npj Computational Materials

Data-driven proton exchange membrane fuel cell degradation predication through deep learning method

(2018) Rui Ma et al.   APPLIED ENERGY

Remaining Useful Life Prediction for Lithium-ion Battery: A Deep Learning Approach

(2018) Lei Ren et al.   IEEE Access

Using deep neural network with small dataset to predict material defects

(2018) Shuo Feng et al.   MATERIALS & DESIGN

Creep-fatigue life prediction and interaction diagram in nickel-based GH4169 superalloy at 650°C based on cycle-by-cycle concept

(2017) Run-Zi Wang et al.   INTERNATIONAL JOURNAL OF FATIGUE

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

Review of creep–fatigue endurance and life prediction of 316 stainless steels

(2015) Xiao-Li Yan et al.   INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING

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

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

LIBSVM

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

Low cycle fatigue and creep-fatigue interaction behaviour of 316L(N) stainless steel and its welds

(2010) M. Valsan et al.   TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS

A new methodology for analysis of creep and creep fracture data for 9–12% chromium steels

(2008) B. Wilshire et al.   INTERNATIONAL MATERIALS REVIEWS

The European Creep Collaborative Committee (ECCC) Approach to Creep Data Assessment

(2008) Stuart Holdsworth   JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME

Effect of short-term data on predicted creep rupture life – pivoting effect and optimized censoring

(2008) S. Holmström et al.   MATERIALS AT HIGH TEMPERATURES

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