Machine learning predicts fretting and fatigue key mechanical properties

Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems

(2021) Kwanele Phinzi et al.   Remote Sensing

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

Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resampling

(2020) Christine Wallisch et al.   STATISTICS IN MEDICINE

Prediction of fatigue–crack growth with neural network-based increment learning scheme

(2020) Xinran Ma et al.   ENGINEERING FRACTURE MECHANICS

Curved fatigue crack growth prediction under variable amplitude loading by artificial neural network

(2020) Bowen Wang et al.   INTERNATIONAL JOURNAL OF FATIGUE

Coupling Fractal Model for Fretting Wear on Rough Contact Surfaces

(2020) Yi Wang et al.   JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME

Reliability-Based Low Fatigue Life Analysis of Turbine Blisk with Generalized Regression Extreme Neural Network Method

(2019) Chunyi Zhang et al.   Materials

A machine learning approach to the prediction of fretting fatigue life

(2019) D. Nowell et al.   TRIBOLOGY INTERNATIONAL

Numerical methods for stress intensity factor ΔK calculations of fretting cracked interface

(2018) A. de Pannemaecker et al.   TRIBOLOGY INTERNATIONAL

Modelling the fretting fatigue crack growth: From short crack correction strategies to microstructural approaches

(2018) A. de Pannemaecker et al.   INTERNATIONAL JOURNAL OF FATIGUE

In situ fretting fatigue crack propagation analysis using synchrotron X-ray radiography

(2017) A. de Pannemaecker et al.   INTERNATIONAL JOURNAL OF FATIGUE

Identification of the fatigue stress intensity factor threshold for different load ratios R : From fretting fatigue to C(T) fatigue experiments

(2016) A. de Pannemaecker et al.   INTERNATIONAL JOURNAL OF FATIGUE

Subsampling versus bootstrapping in resampling-based model selection for multivariable regression

(2015) Riccardo De Bin et al.   BIOMETRICS

Reverse identification of short–long crack threshold fatigue stress intensity factors from plain fretting crack arrest analysis

(2015) A. de Pannemaecker et al.   ENGINEERING FRACTURE MECHANICS

From uni- to multi-axial fretting-fatigue crack nucleation: Development of a stress-gradient-dependent critical distance approach

(2013) S. Fouvry et al.   INTERNATIONAL JOURNAL OF FATIGUE

Prediction of fretting wear behavior of surface mechanical attrition treated Ti–6Al–4V using artificial neural network

(2013) S. Anand Kumar et al.   MATERIALS & DESIGN

Introduction of a reverse simulation approach to identify the fatigue stress intensity factor crack arrest threshold from fretting cracking experiments

(2013) Alix de Pannemaecker et al.   TRIBOLOGY INTERNATIONAL

Experimental validation of a fracture-mechanics model for evaluating fretting-fatigue strength by focusing on non-propagating cracks

(2013) Kunio Asai   TRIBOLOGY INTERNATIONAL

Characterization of crack nucleation in TA6V under fretting–fatigue loading using the potential drop technique

(2010) J. Meriaux et al.   INTERNATIONAL JOURNAL OF FATIGUE

Introduction of a new sliding regime criterion to quantify partial, mixed and gross slip fretting regimes: Correlation with wear and cracking processes

(2010) S. Heredia et al.   WEAR

Development of a fretting–fatigue mapping concept: The effect of material properties and surface treatments

(2009) S. Fouvry et al.   WEAR

Introduction of a fretting-fatigue mapping concept: Development of a dual crack nucleation – crack propagation approach to formalize fretting-fatigue damage

(2008) S FOUVRY et al.   INTERNATIONAL JOURNAL OF FATIGUE

Prediction of fretting crack propagation based on a short crack methodology

(2007) S. Fouvry et al.   ENGINEERING FRACTURE MECHANICS