Digital Image Correlation Techniques for Analysing the Deformation Behaviour of Compacted Graphite Cast Irons on a Microstructural Level

2011 ◽  
Vol 70 ◽  
pp. 171-176 ◽  
Author(s):  
Torsten Sjögren ◽  
Fredrik Wilberfors ◽  
Monika Alander

Digital image correlation techniques (DIC) have been used in this study to reveal how different phases (graphite, ferrite and pearlite) of compacted graphite cast irons (CGI) accommodate strains at loading. A DIC-software was used to analyse sets of successively acquired images. The images were acquired with a light optical microscope revealing the microstructure of polished and etched CGI materials at different load levels. Five CGI materials, having percentages of pearlite in the range 35 to 90 area%, were included in the study. Apart from the different matrix constituents (ferrite and pearlite) the nodularity was altered and varied from 5% to 65%. It is concluded from this study that the different phases in CGI are affected by the global strain and load to a greater or less extent. The graphite phase appears to accommodate high strains but, due to fracture of the graphite particles at relatively low strains, the resulting high strain values show the opening up of graphite cavities. The ferrite phase is subjected to a strain concentrating effect of the graphite phase and with a low nodularity, i.e., with graphite particles that are stretched out and interconnected, the effect becomes more pronounced resulting in high strains. The pearlitic phase is the strongest of the constituents within the microstructure and the lowest local strains are observed in this phase. The study shows that DIC strain-field images reveal the microstructural strain level distribution for the CGI materials. The future outlook is to use these sets of strain-field images for verification of micro mechanical finite element analysis on a microstructural scale of CGI materials.

2010 ◽  
Vol 457 ◽  
pp. 470-475 ◽  
Author(s):  
Torsten Sjögren ◽  
Per Eric Persson ◽  
Peter Vomacka

During the last years the use of digital image correlation techniques (DIC) has become wide spread within different areas of research. One area in which these techniques are used is in the analysis of deformation of engineering materials. By the analysis of a set of successive images taken during a tensile test DIC makes it possible to determine how the deformation is localized. The observed local strains are often several times higher than the global strain measured by standard strain gauges. In this study, a set of compacted graphite cast irons (CGI) with different ratios of pearlite to ferrite have been examined by the use of DIC. In contrast to the normal use of DIC, where a pattern is sprayed on the tensile test sample as a reference for the determination of deformation taking place between successive images, the materials natural microstructural pattern has been used in this study. The use of the natural microstructural pattern makes it possible to study how the macroscopic deformation is accommodated within the different phases in the CGI studied. It is shown that the graphite phase accommodates a large portion of the strain and that the soft ferrite is strained more than the stronger, less ductile pearlite. The local strain of the observed area might be up to ten times higher than the global strain measured. The use of DIC improves the understanding of the deformation behaviour of compacted graphite cast irons and will be a useful tool when validating future finite element analyses of the micro-mechanical properties of cast irons.


2010 ◽  
Vol 1 (4) ◽  
pp. 344-357 ◽  
Author(s):  
V. Richter‐Trummer ◽  
P.M.G.P. Moreira ◽  
S.D. Pastrama ◽  
M.A.P. Vaz ◽  
P.M.S.T. de Castro

PurposeThe purpose of this paper is to develop a methodology for in situ stress intensity factor (SIF) determination that can be used for the analysis of cracked structures. The technique is based on digital image correlation (DIC) combined with an overdetermined algorithm.Design/methodology/approachThe linear overdeterministic algorithm for calculating the SIF based on stress values around the crack tip is applied to a strain field obtained by DIC.FindingsAs long as the image quality is sufficiently high, a good accuracy can be obtained for the measured SIF. The crack tip can be automatically detected based on the same strain field. The use of the strain field instead of the displacement field, eliminates problems related to the rigid body motion of the analysed structure.Practical implicationsIn future works, based on the applied techniques, the SIF of complex cracked plane stress structures can be accurately determined in real engineering applications.Originality/valueThe paper demonstrates application of known techniques, refined for other applications, also the use of stress field for SIF overdeterministic calculations.


2017 ◽  
Vol 8 (2) ◽  
pp. 337-347 ◽  
Author(s):  
Jorge Barrios-Muriel ◽  
Francisco Javier Alonso Sánchez ◽  
David Rodríguez Salgado ◽  
Francisco Romero-Sánchez

Abstract. Today there is continuous development of wearable devices in various fields such as sportswear, orthotics and personal gadgets, among others. The design of these devices involves the human body as a support environment. Based on this premise, the development of wearable devices requires an improved understanding of the skin strain field of the body segment during human motion. This paper presents a methodology based on a three dimensional digital image correlation (3D-DIC) system to measure the skin strain field and to estimate anatomical lines with minimum deformation as design criteria for the aforementioned wearable devices. The errors of displacement and strain measurement related to 3-D reconstruction and out-of-plane motion are investigated and the results are acceptable in the case of large deformation. This approach can be an effective tool to improve the design of wearable devices in the clinical orthopaedics and ergonomics fields, where comfort plays a key role in supporting the rehabilitation process.


2017 ◽  
Vol 61 (1) ◽  
pp. 21-36 ◽  
Author(s):  
ZhenXing Hu ◽  
TingGe Xu ◽  
XueMin Wang ◽  
ZhiMing Xie ◽  
HuiYang Luo ◽  
...  

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