Testing System for the Mechanical Properties of Small-Scale Specimens Based on 3D Microscopic Digital Image Correlation

The testing of the mechanical properties of materials on a small scale is difficult because of the small specimen size and the difficulty of measuring the full-field strain. To tackle this problem, a testing system for investigating the mechanical properties of small-scale specimens based on the three-dimensional (3D) microscopic digital image correlation (DIC) combined with a micro tensile machine is proposed. Firstly, the testing system is described in detail, including the design of the micro tensile machine and the 3D microscopic DIC method. Then, the effects of different shape functions on the matching accuracy obtained by the inverse compositional Gauss–Newton (IC-GN) algorithm are investigated and the numerical experiment results verify that the error due to under matched shape functions is far larger than that of overmatched shape functions. The reprojection error is shown to be smaller than before when employing the modified iteratively weighted radial alignment constraint method. Both displacement and uniaxial measurements were performed to demonstrate the 3D microscopic DIC method and the testing system built. The experimental results confirm that the testing system built can accurately measure the full-field strain and mechanical properties of small-scale specimens.

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Full-Field Strain Characterizations and Fracture Process of Rock Blasting Using a Small-Scale Double-Hole Bench Model

Advances in Civil Engineering ◽

10.1155/2020/8649258 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jun Yang ◽

Xin Liu ◽

Zhenyang Xu ◽

Hongliang Tang ◽

Qi Yu

Keyword(s):

Digital Image Correlation ◽

Crack Propagation ◽

Digital Image ◽

Fracture Process ◽

Image Correlation ◽

Field Strain ◽

Small Scale ◽

Rock Blasting ◽

Full Field ◽

Bench Model

A small-scale double-hole bench model is designed with granite to study the fracture mechanism of rock blasting. By combining high-speed camera and digital image correlation, the full-field strain characterization and fracture process of the specimen bevel surface are investigated. The preliminary test results show that the strain concentration zone corresponds to the crack propagation location, and digital image correlation can well detect the crack propagation. In addition, through observing the crack propagation pattern on the specimen bevel surface, it can be seen that the fracture of the specimen is caused by the dominant horizontal crack and the dominant vertical crack, and the generation of the dominant horizontal crack takes precedence over that of the dominant vertical. Finally, the measurements of two-dimensional digital image correlation and three-dimensional digital image correlation are discussed.

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Full-Field Sensing: Three-Dimensional Computer Vision and Digital Image Correlation for Noncontacting Shape and Deformation Measurements

Encyclopedia of Structural Health Monitoring ◽

10.1002/9780470061626.shm021 ◽

2008 ◽

Author(s):

Michael A. Sutton

Keyword(s):

Computer Vision ◽

Digital Image Correlation ◽

Digital Image ◽

Three Dimensional ◽

Image Correlation ◽

Full Field ◽

Field Sensing ◽

Deformation Measurements

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Local Elasto-Plastic Identification of the Behaviour of Friction Stir Welds with the Virtual Fields Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.70.135 ◽

2011 ◽

Vol 70 ◽

pp. 135-140 ◽

Cited By ~ 2

Author(s):

G. Le Louëdec ◽

M.A. Sutton ◽

Fabrice Pierron

Keyword(s):

Mechanical Properties ◽

Digital Image Correlation ◽

Digital Image ◽

High Speed ◽

Weld Zone ◽

Spatial Variations ◽

Image Correlation ◽

Virtual Fields Method ◽

Full Field ◽

Friction Stir

Welding is one of the most popular joining technologies in industry. Depending on the materials to be joined, the geometry of the parts and the number of parts to be joined, there is a wide variety of methods that can be used. These joining techniques share a common feature: the material in the weld zone experiences different thermo-mechanical history, resulting in significant variations in material microstructure and spatial heterogeneity in mechanical properties. To optimize the joining process, or to refine the design of welded structures, it is necessary to identify the local mechanical properties within the different regions of the weld. The development of full-field kinematic measurements (digital image correlation, speckle interferometry, etc.) helps to shed a new light on this problem. The large amount of experimental information attained with these methods makes it possible to visualize the spatial distribution of strain on the specimen surface. Full-field kinematic measurements provide more information regarding the spatial variations in material behaviour. As a consequence, it is now possible to quantify the spatial variations in mechanical properties within the weld region through a properly constructed inverse analysis procedure. High speed tensile tests have been performed on FSW aluminium welds. The test was performed on an MTS machine at a cross-head speed of up to 76 mm/s. Displacement fields were measured across the specimen by coupling digital image correlation with a high-speed camera (Phantom V7.1) taking 1000 frames per second. Then, through the use of the virtual fields method it is possible to retrieve the mechanical parameters of the different areas of the weld from the strain field and the loading. The elastic parameters (Young’s modulus and Poisson’s ratio) are supposed to be constant through the weld. Their identification was carried out using the virtual fields method in elasticity using the data of the early stage of the experiment. Assuming that the mechanical properties (elastic and plastic) of the weld are constant through the thickness, the plastic parameters were identified on small sections through the specimen, using a simple linear hardening model. This method leads to a discrete identification of the evolution of the mechanical properties through the weld. It allows the understanding of the slight variations of yield stress and hardening due to the complexity of the welding process.

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Full-field strain measurements at the micro-scale in fiber-reinforced composites using digital image correlation

Composite Structures ◽

10.1016/j.compstruct.2015.12.020 ◽

2016 ◽

Vol 140 ◽

pp. 192-201 ◽

Cited By ~ 60

Author(s):

Mahoor Mehdikhani ◽

Mohammadali Aravand ◽

Baris Sabuncuoglu ◽

Michaël G. Callens ◽

Stepan V. Lomov ◽

...

Keyword(s):

Digital Image Correlation ◽

Digital Image ◽

Image Correlation ◽

Fiber Reinforced Composites ◽

Field Strain ◽

Reinforced Composites ◽

Fiber Reinforced ◽

Full Field ◽

Strain Measurements ◽

Micro Scale

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Dynamic Response Analysis of Mortar Block under Blast Loading Using Digital Image Correlation

Materials Science Forum ◽

10.4028/www.scientific.net/msf.910.161 ◽

2018 ◽

Vol 910 ◽

pp. 161-166 ◽

Cited By ~ 1

Author(s):

Tei Saburi ◽

Toshiaki Takahashi ◽

Shiro Kubota ◽

Yuji Ogata

Keyword(s):

Digital Image Correlation ◽

Digital Image ◽

Strain Gauge ◽

Strain Distribution ◽

Three Dimensional ◽

Image Correlation ◽

Dynamic Strain ◽

Small Scale ◽

Response Analysis ◽

Strain Profile

The dynamic strain distribution behavior of a mortar block blasting was experimentally investigated. A small-scale blasting experiment using a mortar block with well-defined property was conducted and the dynamic strain distribution on the mortal block surface was analyzed using a Digital Image Correlation (DIC) method to establish the effective method for investigating the relationship between blast design and fracture mechanism. The block was blasted by simultaneous detonation of Composition C4 explosive charges with an electric detonator in two boreholes. The behavior of the block surface was observed by two high-speed cameras for three-dimensional DIC analysis and it was also measured by a strain-gauge for comparison. The three-dimensional displacements of the free surface of the block were obtained and dynamic strain distributions were computed. A point strain profile extracted from the analyzed strain distribution data was compared with a directly observed strain profile by the strain gauge.

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Robust Filtering Options for Higher-Order Strain Fields Generated by Digital Image Correlation

Applied Mechanics ◽

10.3390/applmech1040012 ◽

2020 ◽

Vol 1 (4) ◽

pp. 174-192

Author(s):

Nedaa Amraish ◽

Andreas Reisinger ◽

Dieter H. Pahr

Keyword(s):

Digital Image Correlation ◽

Digital Image ◽

Tensile Tests ◽

Image Correlation ◽

Field Strain ◽

Strain Fields ◽

Full Field ◽

Low Pass ◽

Discrete Measurement ◽

Mean Filtering

Digital image correlation (DIC) systems have been used in many engineering fields to obtain surface full-field strain distribution. However, noise affects the accuracy and precision of the measurements due to many factors. The aim of this study was to find out how different filtering options; namely, simple mean filtering, Gaussian mean filtering and Gaussian low-pass filtering (LPF), reduce noise while maintaining the full-field information based on constant, linear and quadratic strain fields. Investigations are done in two steps. First, linear and quadratic strain fields with and without noise are simulated and projected to discrete measurement points which build up strain window sizes consisting of 6×5, 12×11, and 26×17 points. Optimal filter sizes are computed for each filter strategy, strain field type, and strain windows size, with minimal impairment of the signal information. Second, these filter sizes are used to filter full-field strain distributions of steel samples under tensile tests by using an ARAMIS DIC system to show their practical applicability. Results for the first part show that for a typical 12×11 strain window, simple mean filtering achieves an error reduction of 66–69%, Gaussian mean filtering of 72–75%, and Gaussian LPF of 66–69%. If optimized filters are used for DIC measurements on steel samples, the total strain error can be reduced from initial 240−300 μstrain to 100–150 μstrain. In conclusion, the noise-floor of DIC signals is considerable and the preferable filters were a simple mean with s*¯ = 2, a Gaussian mean with σ*¯ = 1.7, and a Gaussian LPF with D0*¯ = 2.5 in the examined cases.

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