Using Barkhausen Noise and Digital Image Correlation to Investigate the Influence of Local Residual Stresses on Fatigue-Crack Propagation

2016 ◽  
Vol 5 (3) ◽  
pp. MPC20150059 ◽  
Author(s):  
M. Thielen ◽  
M. Marx ◽  
M. Sheikh-Amiri ◽  
C. Boller ◽  
C. Motz
Keyword(s):  
Fatigue Crack ◽  
Digital Image Correlation ◽  
Residual Stresses ◽  
Crack Propagation ◽  
Fatigue Crack Propagation ◽  
Digital Image ◽  
Image Correlation ◽  
Barkhausen Noise

scholarly journals Identification and Prediction of Mixed-Mode Fatigue Crack Path in High Strength Low Alloy Steel

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 504
Author(s):  
Jie Zhang ◽  
Cedric Kiekens ◽  
Stijn Hertelé ◽  
Wim De Waele
Keyword(s):  
Fatigue Crack ◽  
Digital Image Correlation ◽  
Crack Propagation ◽  
Digital Image ◽  
Mixed Mode ◽  
Standardized Test ◽  
Crack Tip ◽  
Shear Loading ◽  
Image Correlation ◽  
Crack Trajectory

The trajectory of fatigue crack growth is influenced by many parameters and can be irregular due to changes in stress distribution or in material properties as the crack progresses. Images of the surface of a standardized test specimen can be used to visualize the crack trajectory in a non-destructive way. Accurately identifying the location of the crack tip, however, is challenging and requires devoted image postprocessing. In this respect, digital image correlation allows to obtain full field displacement and strain fields by analysing changes of digital images of the same sample at different stages of loading. This information can be used for the purpose of crack tip tracking. This paper presents a combined experimental-numerical study of detection and prediction of fatigue crack propagation path by means of digital image correlation (DIC) and the extended finite element method (X-FEM). Experimental validation and analyses are carried out on a modified C(T) specimen in which a curved crack trajectory is triggered by introducing mixed-mode (tension + shear) loading. The developed tools are used for validating an automated framework for crack propagation prediction.

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