Examples of the Use of the ARAMIS 3D Measurement System for the Susceptibility to Deformation Tests for the Selected Mixtures of Coal Mining Wastes
Abstract
:1. Introduction
- deformation measurements by DIC: correlation of a second-order displacement gradient [19];
- In biological tissues [20];
- For strain measurements in open-cell structures such as trabecular bone. A 3D DIC uses high-resolution computed tomography images for displacement measurements in the solid structure [21];
- The investigation of three types of the most commonly used sub-pixel displacement registration algorithms in terms of the registration accuracy and the computational efficiency using computer-simulated speckle images [22];
- Studying crack propagation in brittle materials such as ceramics [23];
- For studying subset size selection in DIC for speckle patterns [24];
- 2D DIC for in-plane displacement and strain measurement [25];
- Monitoring the crack growth process during a cyclic fatigue life [26];
- Analyzing the deformation mechanisms under transverse compression in a fi-ber-reinforced composite [27];
- In experiments including electron microscopes and stereo-microscopes, and large-scale application with a field of view of tens of meters, at low speed and at high- and ultra-high speed [28];
- In local and global approaches to DIC [29];
- For surface deformation measurements [2];
- In masonry wall measurement [30];
- In renewable energy sources’ installations testing [33].
2. Methods of Measuring Deformation of Tested Objects under Uniaxial Stress
- Δh—sample shortening
- Δu—piston displacement
- ΔH—sum of the changes in length of the clamping elements and in clearance.
3. Deformation Measurement Using Digital Image Correlation DIC System with the Example of ARAMIS 3D GOM Measurement System
3.1. Structure of ARAMIS 3D GOM Measurement System
3.2. Operation Principle of ARAMIS 3D Measurement System
3.3. Surface Preparation of Test Specimens
4. Methodology and Research Object
5. Research Findings and Analysis
5.1. Interface Elements and Basic Data Reading Elements
- The lower plate the causes displacements on the specimen is labelled as Lower plate. A point component (obtained by using the measurement points of Figure 4b) was marked on this element, based on which the global deformations forced on the specimen were measured.
- The upper plate that imparts resistance to the test specimen is designated Upper plate. On this element, a point component was applied to measure the amount of backlash, which was reset at each loading of the specimen.
- The surface component was obtained from a gradient scan of the sample surface. This component is a 3D model of the surface of the test specimen (Figure 3c). The surface component is used to inspect the major strains occurring on the specimen surface according to the legend shown on the left side of the interface.
- Analog input 0 is the compression force reading from the testing press to which the ARAMIS 3D system is coupled.
- Analog input 1 is the displacement reading of the lower plate of the testing machine; this is a measurement of a value, which is partially identical to the displacement value read from the Lower plate component.
- Tree of performed measurements and elements marked on the test image (left side of the interface);
- Sample view and 3D model of the sample surface with selected elements (center of interface);
- Timeline, located at the very bottom of the interface. This marks the individual frames of the recorded test;
- The diagram above the sample preview that allows constant control of the measured parameters in each successive frame of the test recording.
- Surface filter—is a filter that compensates for the effects of improperly painted samples on the results. This filter compares measurements of neighboring points of a 3D model of sample surface, and rejects results that differ significantly from each other. The numerical value used for the filter can be used to determine the number of adjacent points when verifying the measurement.
- Temporary filter—this filter verifies the scatter of measurement results between the same measurement points in consecutive frames. The numerical value used for this filter defines the number of neighboring frames to be taken into account while verifying the measurement.
- Average filter—averages the values obtained in adjacent points of the plot or adjacent frames of the recording;
- Median filter—determines the median of the values obtained at adjacent points of the plot or frames of the recording;
- Binomial filter—based on the binomial function, verifies measurements of adjacent points of the surface or frames of the recording;
- Spline smoothing—this is a method for graphically smoothing the results obtained without interfering with their numerical values.
5.2. Measuring Capabilities of ARAMIS 3D
- Measurement of displacements oriented in the X/Y/Z directions of the assumed coordinate system;
- Mises’ balanced strains;
- Thickness reduction;
- Distortion in vertical and horizontal planes, without depth control;
- Major and minor strains.
5.3. Example of ARAMIS 3D Measurement Capabilities
6. Summary
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sutton, M.A.; Orteu, J.-J.; Schreier, H.W. Image Correlation for Shape, Motion and Deformation Measurements; Basic Concepts, Theory and Applications; Springer: Berlin/Heidelberg, Germany, 2009. [Google Scholar]
- Pan, B. Digital image correlation for surface deformation measurement: Historical developments, recent advances and future goals. Meas. Sci. Technol. 2018, 29, 082001. [Google Scholar] [CrossRef]
- Bruck, H.A.; McNeill, S.R.; Sutton, M.A.; Peters, W.H., III. Digital image correlation using Newton-Raphson method of partial differential correction. In Proceedings of the SEM Spring Conference on Experimental Mechanics, Portland, OR, USA, 5–10 June 1989; pp. 261–267. [Google Scholar]
- Mguil-Touchal, S.; Morestin, F.; Brunet, M. Various experimental applications of digital image correlation method. Computer Methods and Experimental Measurements. Trans. Model. Simul. 1997, 16, 45–58. [Google Scholar]
- Schreier, H.; Sutton, M.A. Systematic errors in digital image correlation due to under matched subset shape functions. Exp. Mech. 2002, 42, 303–310. [Google Scholar] [CrossRef]
- Sutton, M.A.; Yan, J.H.; Tiwari, V.; Schreier, H.W.; Orteu, J.J. The effect of out-of-plane motion on 2D and 3D digital image correlation measurements. Opt. Lasers Eng. 2008, 46, 746–757. [Google Scholar] [CrossRef] [Green Version]
- McCormick, N.; Lord, J. Digital image correlation. Mater. Today 2010, 13, 52–54. [Google Scholar] [CrossRef]
- Tang, Z.; Liang, J.; Xiao, Z.; Guo, C. Large deformation measurement scheme for 3D digital image correlation method. Opt. Lasers Eng. 2012, 50, 122–130. [Google Scholar] [CrossRef]
- Bossuyt, S. Chapter 34: Optimized patterns for digital image correlation. In Imaging Methods for Novel Materials and Challenging Applications, Proceedings of the 2012 Annual Conference on Experimental and Applied Mechanics; Series 35; Jin, H., Sciammarella, C., Furlong, C., Yoshida, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; Volume 3, pp. 239–248. [Google Scholar]
- Hoult, N.A.; Take, W.A.; Lee, C.; Dutton, M. Experimental accuracy of two dimensional strain measurements using Digital Image Correlation. Eng. Struct. 2013, 46, 718–726. [Google Scholar] [CrossRef]
- Malesa, M.; Malowany, K.; Tomczak, U.; Siwek, B.; Kujawińska, M.; Siemińska-Lewandowska, A. Application of 3D digital image correlation in maintenance and process control in industry. Comput. Ind. 2013, 64, 1301–1315. [Google Scholar] [CrossRef]
- Blaber, J.; Adair, B.; Antoniou, A. Ncorr: Open-source 2D digital image correlation. Exp. Mech. 2015, 55, 1105–1122. [Google Scholar] [CrossRef]
- Chu, T.C.; Ranson, W.F.; Sutton, M.A.; Peters, W.H. Application of Digital-Image-Correlation techniques to experimental mechanics. Exp. Mech. 1985, 25, 232–244. [Google Scholar] [CrossRef]
- Yoneyama, S.; Murasawa, G. Digital Image Correlation. Experimental Mechanics, Encyclopedia of Life Support Systems (EOLSS); Eolss Publishers: Oxford, UK, 2009. [Google Scholar]
- Stoilov, G.; Kavardzhikov, V.; Pashkouleva, D. A comparative study of random patterns for digital image correlation. J. Theor. Appl. Mech. 2012, 42, 55–66. [Google Scholar] [CrossRef] [Green Version]
- Palanca, M.; Tozzi, G.; Cristofolini, L. The use of digital image correlation in the biomechanical area: A review. Int. Biomech. 2016, 3, 1–21. [Google Scholar] [CrossRef]
- Kosiń, M. The use of an optical system for bending and torsional analyses of open cold-formed profiles. Zesz. Nauk. Politech. Częstochowskiej Czest. Univ. Technol. 2020, 26, 83–88. [Google Scholar]
- Cerbu, C.; Xu, D.; Wang, H.; Roşca, I.C. The use of digital image correlation in determining the mechanical properties of materials. IOP Conf. Ser. Mater. Sci. Eng. 2018, 399, 012007. [Google Scholar] [CrossRef]
- Lu, H.; Cary, P.D. Deformation measurements by digital image correlation: Implementation of a second-order displacement gradient. Exp. Mech. 2000, 40, 393–400. [Google Scholar] [CrossRef]
- Zhang, D.; Arola, D.D. Applications of digital image correlation to biological tissues. J. Biomed. Opt. 2004, 9, 691–699. [Google Scholar] [CrossRef] [PubMed]
- Verhulp, E.; van Rietbergen, B.; Huiskes, R. A three-dimensional digital image correlation technique for strain measurements in microstructures. J. Biomech. 2004, 37, 1313–1320. [Google Scholar] [CrossRef] [PubMed]
- Bing, P.; Xie, H.; Xu, B.; Dai, F. Performance of sub-pixel registration algorithms in digital image correlation. Meas. Sci. Technol. 2006, 17, 1615–1621. [Google Scholar] [CrossRef]
- Roux, S.; Hild, F. Stress intensity factor measurement from digital image correlation: Post-processing and integrated approaches. Int. J. Fract. 2006, 140, 141–157. [Google Scholar] [CrossRef]
- Pan, B.; Xie, H.; Wang, Z.; Qian, K.; Wang, Z. Study on subset size selection in digital image correlation for speckle patters. Opt. Express 2008, 16, 7037–7048. [Google Scholar] [CrossRef]
- Pan, B.; Qian, K.; Xie, H.; Asundi, A. Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review. Meas. Sci. Technol. 2009, 20, 062001. [Google Scholar] [CrossRef]
- Vanlanduit, S.; Vanherzeele, J.; Longo, R.; Guillaume, P. A digital image correlation method for fatigue test experiments. Opt. Lasers Eng. 2009, 47, 371–378. [Google Scholar] [CrossRef]
- Canal, L.P.; Gonzáles, C.; Molina-Aldareguía, J.M.; Segurado, J.; Llorca, J. Application of digital image correlation at the microscale in fiber-reinforced composites. Compos. Part A 2012, 43, 1630–1638. [Google Scholar] [CrossRef] [Green Version]
- Reu, P. The Art and application of DIC. Introduction to digital image correlation: Best practices and applications. Exp. Tech. 2012, 36, 3–4. [Google Scholar]
- Hild, F.; Roux, S. Comparison of local and global approaches to digital image correlation. Exp. Mech. 2012, 52, 1503–1519. [Google Scholar] [CrossRef]
- Ramos, T.; Furtado, A.; Eslami, S.; Alves, S.; Rodrigues, H.; Arêde, A.; Tavares, P.J.; Moreira, P. 2D and 3D Digital Image Correlation in Civil Engineering—Measurements in a Masonry Wall. Procedia Eng. 2015, 114, 215–222. [Google Scholar] [CrossRef] [Green Version]
- Turoń, B.; Ziaja, D.; Buda-Ożóg, L.; Miller, B. DIC in validation of boundary conditions of numerical model of reinforced concrete beams under torsion. Arch. Civ. Eng. 2018, 64, 31–48. [Google Scholar] [CrossRef] [Green Version]
- Zarrinpour, M.R.; Chao, S.-H. Shear strength enhancement mechanisms of steel fiber-reinforced concrete slender beams. ACI Struct. J. 2017, 114, 729–742. [Google Scholar] [CrossRef]
- Poozesh, P.; Baqersad, J.; Niezrecki, C.; Avitabile, P.; Harvey, E.; Yarala, R. Large-area photogrammetry based testing of wind turbine blades. Mech. Syst. Signal Process. 2017, 86, 98–115. [Google Scholar] [CrossRef] [Green Version]
- PKN. PN-S-96012:1997 Roads—Foundation and Improved Substrate Made of Soil Stabilized with Cement; PKN: Warsaw, Poland, 1997. [Google Scholar]
- PKN. PN-EN 206+A1:2006-12 Concrete. Requirements, Properties, Production and Compliance; PKN: Warsaw, Poland, 2017. [Google Scholar]
- Bieniawski, Z.T.; Bernede, M.J. Suggested Methods for Determining the Uniaxial Compressive Strength and Deformability of Rock Materials. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 1979, 16, 138–140, ISSN 0148-9062. [Google Scholar] [CrossRef]
- PKN. PN-EN ISO 6892-1:2020-05 Metals—Tensile Test—Part 1: Room Temperature Test Method; PKN: Warsaw, Poland, 2020. [Google Scholar]
- Nowakowski, A.; Sobczyk, J.; Nurkowski, J.; Gawor, M.; Lizak, Z.; Bujalski, M. Comparison of the results of measurement of sample deformation by means of strain gauges and photogrammetric methods. In Transactions of the Strata Mechanics Research Institute; Strata Mechanics Research Institute: Kraków, Poland, 2013; Volume 15, pp. 95–109. [Google Scholar]
- GOM a ZEISS Company. Available online: Gom.com (accessed on 3 March 2021).
- Hu, Z.; Xie, H.; Lu, J.; Hua, T. Study of the performance of different subpixel image correlation methods in 3D digital image correlation. Appl. Opt. 2010, 49, 4044–4051. [Google Scholar]
- PKN. PN-EN 13286-43:2005 Unbound and Hydraulically Bound Mixtures—Part 43: Method for Determining the Elastic Modulus of Hydraulically Bound Mixtures; PKN: Warsaw, Poland, 2005. [Google Scholar]
- Application Notes—ARAMIS. Available online: GOM.com/en/applications (accessed on 3 March 2021).
Measuring Beam | Measurement Focal Distance (mm) | Measuring Area (mm) |
---|---|---|
150 mm | 350 | 30 × 20 × 10 |
350 | 60 × 50 × 30 | |
350 | 100 × 80 × 50 | |
350 | 150 × 120 × 90 | |
300 mm | 700 | 150 × 120 × 90 |
700 | 215 × 180 × 150 | |
700 | 330 × 270 × 200 | |
700 | 600 × 530 × 400 | |
600 mm | 1400 | 680 × 560 × 560 |
1400 | 1250 × 1100 × 1100 | |
1200 mm | 2700 | 1350 × 1100 × 1100 |
2700 | 2500 × 2150 × 2150 | |
1600 mm | 4500 | 5000 × 4400 × 4400 |
Recipe | Unburnt Coal Mining Slate 0/16 mm (%) | Shredded Rubber Waste 0/2 mm (%) | Silica Fly Ash (%) | CEM I 42.5 R (%) | Water Content * (%) |
---|---|---|---|---|---|
G0 | 90 | 0 | 5 | 5 | 11.2 |
G10 | 80 | 10 | 5 | 5 | 10.1 |
G0 | G10 | |
---|---|---|
F30% (kN) | 3.816 | 2.031 |
Global axial strain in F30% (%) | −0.275 | −0.101 |
Global lateral strain in F30% (%) | 0.068 | 0.028 |
Poisson’s ratio ν (-) | 0.25 | 0.28 |
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Walotek, K.; Bzówka, J.; Ciołczyk, A. Examples of the Use of the ARAMIS 3D Measurement System for the Susceptibility to Deformation Tests for the Selected Mixtures of Coal Mining Wastes. Sensors 2021, 21, 4600. https://doi.org/10.3390/s21134600
Walotek K, Bzówka J, Ciołczyk A. Examples of the Use of the ARAMIS 3D Measurement System for the Susceptibility to Deformation Tests for the Selected Mixtures of Coal Mining Wastes. Sensors. 2021; 21(13):4600. https://doi.org/10.3390/s21134600
Chicago/Turabian StyleWalotek, Konrad, Joanna Bzówka, and Adrian Ciołczyk. 2021. "Examples of the Use of the ARAMIS 3D Measurement System for the Susceptibility to Deformation Tests for the Selected Mixtures of Coal Mining Wastes" Sensors 21, no. 13: 4600. https://doi.org/10.3390/s21134600
APA StyleWalotek, K., Bzówka, J., & Ciołczyk, A. (2021). Examples of the Use of the ARAMIS 3D Measurement System for the Susceptibility to Deformation Tests for the Selected Mixtures of Coal Mining Wastes. Sensors, 21(13), 4600. https://doi.org/10.3390/s21134600