پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 439 |
| تعداد مقالات | 5,666 |
| تعداد مشاهده مقاله | 7,782,210 |
| تعداد دریافت فایل اصل مقاله | 6,378,662 |
Influencing Parameters on Tensile Modulus Characterization of Unidirectional Composite Laminates Using Digital Image Correlation Technique | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 4، دوره 6، شماره 4، اسفند 2022، صفحه 545-560 اصل مقاله (3.89 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2022.20714.6015 | ||
| نویسندگان | ||
| Amirreza Zarei Aziz؛ Roohollah Sarfaraz Khabbaz* | ||
| Department of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, | ||
| چکیده | ||
| Digital image correlation as a well-established strain measurement technique is commonly employed for material characterization purposes. However, several parameters should usually be decided by users to attain accurate results with minimum processing time while there are few definite recommendations for the selection of these parameters. In the present study, the optimum setting of digital image correlation parameters is examined in order to minimize the processing time and sensitivity to the selection of parameters, and practical directions are advised to improve the efficiency of the technique. The performance of a typical analysis for the derivation of strain field and measurement of tensile modulus of a unidirectional carbon/epoxy composite laminate subjected to monotonic loading is experimentally assessed. The influence of setting parameters on the accuracy of measurement and the computational time required for the process is examined. The mutual influence of these parameters is also analyzed and discussed. Comparison of results shows the sensitivity of outputs to the selection of investigating parameters i.e. subset radius, subset spacing, and region of interest. The results show that an efficient gain from the maximum available region of digital images reduces the sensitivity of the analysis to these parameters. Moreover, the error introduced to the results is slightly increased by the increase of subset spacing while this influence can be diminished by enlarging the subset radius. | ||
| کلیدواژهها | ||
| Digital image correlation؛ Subset size؛ Subset radius؛ Region of interest؛ Strain measurement | ||
| مراجع | ||
|
[1] P. Corigliano, V. Crupi, E. Guglielmino, Non linear finite element simulation of explosive welded joints of dissimilar metals for shipbuilding applications, Ocean Engineering, 160 (2018) 346-353.
[2] Y. He, A. Makeev, B. Shonkwiler, Characterization of nonlinear shear properties for composite materials using digital image correlation and finite element analysis, Composites Science and Technology, 73 (2012) 64-71.
[3] S. Bhattacharjee, D. Deb, Automatic detection and classification of damage zone (s) for incorporating in digital image correlation technique, Optics and Lasers in Engineering, 82 (2016) 14-21.
[4] X. Chen, Z. Yu, H. Wang, C. Bil, Strain monitoring on damaged composite laminates using digital image correlation, Procedia Engineering, 99 (2015) 353-360.
[5] A. Makeev, Y. He, P. Carpentier, B. Shonkwiler, A method for measurement of multiple constitutive properties for composite materials, Composites Part A: Applied Science and Manufacturing, 43(12) (2012) 2199-2210.
[6] B. Pan, K. Qian, H. Xie, A. Asundi, Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review, Measurement science and technology, 20(6) (2009) 062001.
[7] A. Makeev, C. Ignatius, Y. He, B. Shonkwiler, A test method for assessment of shear properties of thick composites, Journal of composite materials, 43(25) (2009) 3091-3105.
[8] A.F. Ab Ghani, J. Mahmud, Material Characterization of Hybrid Composite: Experimental Using Strain Gauge/DIC with Finite Element Modelling Macro/Micro Scale, Key Engineering Materials, 740 (2017) 31 - 40.
[9] M.C. Isa, A.R.A. Manaf, M.H. Anuar, A.F. Ab Ghani, J. Mahmud, S. Nazran, N. Muhammad, R.M. Amman, S.D. Malingam, I. Abu-Shah, SCIENCE & TECHNOLOGY RESEARCH INSTITUTE FOR DEFENCE (STRIDE), Development, 240 (2018) 246.
[10] B. Pan, Z. Lu, H. Xie, Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation, Optics and Lasers in Engineering, 48(4) (2010) 469-477.
[11] Y. Wang, M. Sutton, H. Bruck, H. Schreier, Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements, Strain, 45(2) (2009) 160-178.
[12] G. Crammond, S. Boyd, J. Dulieu-Barton, Speckle pattern quality assessment for digital image correlation, Optics and Lasers in Engineering, 51(12) (2013) 1368-1378.
[13] B. Pan, B. Wang, G. Lubineau, Comparison of subset-based local and FE-based global digital image correlation: Theoretical error analysis and validation, Optics and Lasers in Engineering, 82 (2016) 148-158.
[14] T. Hua, H. Xie, S. Wang, Z. Hu, P. Chen, Q. Zhang, Evaluation of the quality of a speckle pattern in the digital image correlation method by mean subset fluctuation, Optics & Laser Technology, 43(1) (2011) 9-13.
[15] B. Pan, H. Xie, Z. Wang, K. Qian, Z. Wang, Study on subset size selection in digital image correlation for speckle patterns, Optics express, 16(10) (2008) 7037-7048.
[16] D. Lecompte, A. Smits, S. Bossuyt, H. Sol, J. Vantomme, D. Van Hemelrijck, A. Habraken, Quality assessment of speckle patterns for digital image correlation, Optics and lasers in Engineering, 44(11) (2006) 1132-1145.
[17] H. Haddadi, S. Belhabib, Use of rigid-body motion for the investigation and estimation of the measurement errors related to digital image correlation technique, Optics and Lasers in Engineering, 46(2) (2008) 185-196.
[18] S. Yaofeng, J.H. Pang, Study of optimal subset size in digital image correlation of speckle pattern images, Optics and lasers in engineering, 45(9) (2007) 967-974.
[19] W. Tong, An evaluation of digital image correlation criteria for strain mapping applications, Strain, 41(4) (2005) 167-175.
[20] B. Pan, A. Asundi, H. Xie, J. Gao, Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements, Optics and Lasers in Engineering, 47(7-8) (2009) 865-874.
[21] H.W. Schreier, M.A. Sutton, Systematic errors in digital image correlation due to undermatched subset shape functions, Experimental Mechanics, 42(3) (2002) 303-310.
[22] Ncorr V1.2, in: http://www.ncorr.com/download/ncorrmanual_v1_2_2.pdf.
[23] ASTM D3039/D3039M-08,Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials, in, ASTM Internation, West Conshohocken,PA, 2014, pp. 13.
[24] B. Pan, J. Yuan, Y. Xia, Strain field denoising for digital image correlation using a regularized cost-function, Optics and Lasers in Engineering, 65 (2015) 9-17.
[25] J. Blaber, B. Adair, A. Antoniou, Ncorr: open-source 2D digital image correlation matlab software, Experimental Mechanics, 55(6) (2015) 1105-1122. | ||
|
آمار تعداد مشاهده مقاله: 569 تعداد دریافت فایل اصل مقاله: 477 |
||