scholarly journals Strain Acquisition Framework and Novel Bending Evaluation Formulation for Compression-Loaded Composites Using Digital Image Correlation

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 5931
Author(s):  
Jonas J. A. D’haen ◽  
Michael May ◽  
Octavian Knoll ◽  
Stefan Kerscher ◽  
Stefan Hiermaier
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Material Characterization ◽  
State Of The Art ◽  
Fiber Composites ◽  
Image Correlation ◽  
Carbon Fiber Composites ◽  
Failure Initiation ◽  
Bending Mode ◽  
Strain Data

Consistent and reproducible data are key for material characterization. This work presents digital image correlation (DIC) strain acquisition guidelines for compression-loaded carbon fiber composites. Additionally, a novel bending criterion is formulated which builds up on the DIC strain data so that it is able to completely replace state-of-the-art tactile strain devices. These guidelines are derived from a custom test setup that simultaneously investigates the front and side view of the specimen. They reflect both an observation and post-processing standpoint. It is found that the DIC-based strain progress matches closely with state-of-the-art strain gauges up to failure initiation. The new bending evaluation criterion allows the bending state—and therefore, the validity of the compression test—to be monitored analogously to the methodology defined in the standards. Furthermore, the new bending criterion eliminates a specific bending mode, caused by an offset of clamps, which cannot be detected by the traditional strain gauge-based monitoring approach.

scholarly journals A 117 Line 2D Digital Image Correlation Code Written in MATLAB

2020 ◽  
Vol 12 (18) ◽  
pp. 2906
Author(s):  
Devan Atkinson ◽  
Thorsten Becker
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
State Of The Art ◽  
Image Correlation ◽  
Practical Implementation ◽  
Educational Resource ◽  
2D Digital Image Correlation ◽  
Matlab Code ◽  
Starting Point ◽  
Art Techniques

Digital Image Correlation (DIC) has become a popular tool in many fields to determine the displacements and deformations experienced by an object from images captured of the object. Although there are several publications which explain DIC in its entirety while still catering to newcomers to the concept, these publications neglect to discuss how the theory presented is implemented in practice. This gap in literature, which this paper aims to address, makes it difficult to gain a working knowledge of DIC, which is necessary in order to contribute towards its development. The paper attempts to address this by presenting the theory of a 2D, subset-based DIC framework that is predominantly consistent with state-of-the-art techniques, and discussing its implementation as a modular MATLAB code. The correlation aspect of this code is validated, showing that it performs on par with well-established DIC algorithms and thus is sufficiently reliable for practical use. This paper, therefore, serves as an educational resource to bridge the gap between the theory of DIC and its practical implementation. Furthermore, although the code is designed as an educational resource, its validation combined with its modularity makes it attractive as a starting point to develop the capabilities of DIC.

scholarly journals Stereo Digital Image Correlation in MATLAB

2021 ◽  
Vol 11 (11) ◽  
pp. 4904
Author(s):  
Devan Atkinson ◽  
Thorsten Hermann Becker
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
State Of The Art ◽  
Deep Understanding ◽  
Image Correlation ◽  
Educational Resource ◽  
Full Field ◽  
Current State ◽  
Out Of Plane ◽  
Novel Applications

Digital Image Correlation (DIC) has found widespread use in measuring full-field displacements and deformations experienced by a body from images captured of it. Stereo-DIC has received significantly more attention than two-dimensional (2D) DIC since it can account for out-of-plane displacements. Although many aspects of Stereo-DIC that are shared in common with 2D DIC are well documented, there is a lack of resources that cover the theory of Stereo-DIC. Furthermore, publications which do detail aspects of the theory do not detail its implementation in practice. This literature gap makes it difficult for newcomers to the field of DIC to gain a deep understanding of the Stereo-DIC process, although this knowledge is necessary to contribute to the development of the field by either furthering its capabilities or adapting it for novel applications. This gap in literature acts as a barrier thereby limiting the development rate of Stereo-DIC. This paper attempts to address this by presenting the theory of a subset-based Stereo-DIC framework that is predominantly consistent with the current state-of-the-art. The framework is implemented in practice as a 202 line MATLAB code. Validation of the framework shows that it performs on par with well-established Stereo-DIC algorithms, indicating it is sufficiently reliable for practical use. Although the framework is designed to serve as an educational resource, its modularity and validation make it attractive as a means to further the capabilities of DIC.

Material characterization and damage assessment of an AA5352 aluminium alloy using digital image correlation

2019 ◽  
Vol 55 (1-2) ◽  
pp. 3-19 ◽  
Author(s):  
Behzad V Farahani ◽  
Rui Amaral ◽  
Paulo J Tavares ◽  
Pedro MGP Moreira ◽  
Abel dos Santos
Keyword(s):  
Mechanical Properties ◽  
Digital Image Correlation ◽  
Aluminium Alloy ◽  
Digital Image ◽  
Material Characterization ◽  
High Stress ◽  
Stress Triaxiality ◽  
Image Correlation ◽  
Displacement Response ◽  
Force Displacement

The emergence of reliable material characterization techniques in automotive and aeronautical industries, in particular sheet metal forming, promises to underpin a novel advance in materials research. In this regard, 5xxx series aluminium alloys deliver the largest formability range and can be deformed at room temperature. This study aims at determining the mechanical properties of the AA5352 aluminium alloy, using digital image correlation. Thus, tensile sheet specimens manufactured from the corresponding alloy are mechanically tested under a uniaxial condition and deformation fields are monitored. Considering the force/displacement response and stress/strain curves, the material Poisson’s ratio, Young’s modulus and anisotropy coefficient in the transverse direction are characterized by the experimental digital image correlation data. It intends to obtain accurate and reliable mechanical properties to be considered in the future processing analyses. Numerically, adopting the experimentally obtained material properties, the Gurson–Tvergaard–Needleman damage model is implemented using finite element method formulation to forecast the ductile fracture performance of the tested AA5352 sheet. The predicted results are then compared with the experimental digital image correlation solution verifying good agreement with the force/displacement response and the deformation fields. Overall, the acquired numerical results imply that the Gurson–Tvergaard–Needleman damage criterion is capable to render an accurate prediction upon a high stress triaxiality state.

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