Micro-structural Deformation Field Analysis of Aluminum Foam using Finite Element Method and Digital Image Correlation

Document Type : Original Article

Authors

1 Department of Industrial Engineering, IAU Tehran North Branch, Tehran, Iran

2 Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

3 Department of Civil Engineering, Surveying Group, Shahid Rajaee Teacher Training University, Tehran, Iran

Abstract

Porous materials especially closed-cell metallic foams play important roles among novel materials because of their good characteristics e.g. high strength to weight ratio and crashworthiness. On the other hand, mechanical behavior determination and detailed characterization are essential in efficient manipulation and material tailoring. In the present research especial hybrid experimental-numerical approach is used for aluminum foam behavior determination as to the main goal, i.e. continuous deformation field measurement using digital image correlation (DIC) and finite element analysis (FEA) on porous specimen’s surface.  To overcome the 3D modelling problem of closed-cell foams structure, we present the method based on CT-scan and digital optic microscope imaging combination. In the experimental part of the study, aluminum foams and proper specimens are manufactured, and then high-resolution digital imaging and illumination setup are employed. Finally, the deformation field is obtained using DIC. On the other hand, measurement verification and DIC parameters optimization processes are conducted using ABAQUS 2019 with comprehensive mesh independency study and response surface methodology (RSM) respectively as major research achievement. Finally, correlation equations based on high regression models are obtained. Using detailed geometrical micro-model and optimal DIC parameters yields to good numerical-experimental accordance. The novel approach of combined CT and digital microscope imaging instead of industrial micro-CT lowered imaging costs while yielded to accurate numerical results.

Keywords

References

1.     Skozrit, I., Frančeski, J., Tonković, Z., Surjak, M., Krstulović-Opara, L., Vesenjak, M., Kodvanj, J., Gunjević, B. and Lončarić, D., "Validation of numerical model by means of digital image correlation and thermography", Procedia Engineering,  Vol. 101, (2015), 450-458. DOI: 10.1016/j.proeng.2015.02.054
2.     Begonia, M.T., Dallas, M., Vizcarra, B., Liu, Y., Johnson, M.L. and Thiagarajan, G., "Non-contact strain measurement in the mouse forearm loading model using digital image correlation (dic)", Bone,  Vol. 81, (2015), 593-601. DOI: 10.1016/j.bone.2015.09.007
3.     Bernachy-Barbe, F., Gélébart, L., Bornert, M., Crépin, J. and Sauder, C., "Characterization of sic/sic composites damage mechanisms using digital image correlation at the tow scale", Composites Part A: Applied Science and Manufacturing,  Vol. 68, (2015), 101-109. DOI: 10.1016/j.compositesa.2014.09.021
4.     Mehdikhani, M., Aravand, M., Sabuncuoglu, B., Callens, M.G., Lomov, S.V. and Gorbatikh, L., "Full-field strain measurements at the micro-scale in fiber-reinforced composites using digital image correlation", Composite Structures,  Vol. 140, (2016), 192-201. DOI: 10.1016/j.compstruct.2015.12.020
5.     Whitlow, T., Jones, E. and Przybyla, C., "In-situ damage monitoring of a sic/sic ceramic matrix composite using acoustic emission and digital image correlation", Composite Structures,  Vol. 158, (2016), 245-251. DOI: 10.1016/j.compstruct.2016.09.040
6.     Gerbig, D., Bower, A., Savic, V. and Hector Jr, L.G., "Coupling digital image correlation and finite element analysis to determine constitutive parameters in necking tensile specimens", International Journal of Solids and Structures,  Vol. 97, (2016), 496-509. DOI: 10.1016/j.ijsolstr.2016.06.038
7.     Engqvist, J., Wallin, M., Ristinmaa, M. and Hall, S.A., "Modelling and experiments of glassy polymers using biaxial loading and digital image correlation", International Journal of Solids and Structures,  Vol. 102, (2016), 100-111. DOI: 10.1016/j.ijsolstr.2016.10.013
8.     Krstulović-Opara, L., Surjak, M., Vesenjak, M., Tonković, Z., Kodvanj, J. and Domazet, Ž., "Comparison of infrared and 3d digital image correlation techniques applied for mechanical testing of materials", Infrared physics & technology,  Vol. 73, (2015), 166-174. DOI: 10.1016/j.infrared.2015.09.014
9.     Jiang, Y., Akkus, A., Roperto, R., Akkus, O., Li, B., Lang, L. and Teich, S., "Measurement of j-integral in cad/cam dental ceramics and composite resin by digital image correlation", Journal of the Mechanical Behavior of Biomedical Materials,  Vol. 62, (2016), 240-246. DOI: 10.1016/j.jmbbm.2016.05.012
10.   Nguyen, M.-T., Allain, J.-M., Gharbi, H., Desceliers, C. and Soize, C., "Experimental multiscale measurements for the mechanical identification of a cortical bone by digital image correlation", Journal of the Mechanical Behavior of Biomedical Materials,  Vol. 63, (2016), 125-133. . DOI: 10.1016/j.jmbbm.2016.06.011
11.   Aparna, M.L., Chaitanya, G., Srinivas, K. and Rao, J.A., "Fatigue testing of continuous gfrp composites using digital image correlation (DIC) technique a review", Materials Today: Proceedings,  Vol. 2, No. 4-5, (2015), 3125-3131. DOI: 10.1016/j.matpr.2015.07.275
12.   Feng, X., Tufail, R.F. and Zahid, M., "Experimental investigation and statistical modeling of frp confined ruc using response surface methodology", Civil Engineering Journal,  Vol. 5, No. 2, (2019), 268-283. DOI: 10.28991/cej-2019-03091243
13.   Obianyo, J.I. and Agunwamba, J., "Efficiencies of horizontal and vertical baffle mixers", Emerging Science Journal,  Vol. 3, No. 3, (2019), 130-145. DOI: 10.28991/esj-2019-01176
14.   Sharif, S.H., Archin, S. and Asadpour, G., "Optimization of process parameters by response surface methodology for methylene blue removal using cellulose dusts", Civil Engineering Journal,  Vol. 4, No. 3, (2018), 620-634. DOI: 10.28991/cej-0309121
15.   Yang, K., Zou, J. and Shen, J., "Vibration and noise reduction optimization design of mine chute with foam aluminum laminated structure", International Journal of Engineering, Transactions B: Applications, Vol. 33, No. 8, (2020), 1668-1676. DOI:  10.5829/ije.2020.33.08b.26
16.   Hosseini Ravandi, S., "Assessing of fabric appearance changes using image processing techniques", International Journal of Engineering,  Vol. 12, No. 2, (1999), 99-106.
17.   Fattahzadeh, M. and Saghaei, A., "A statistical method for sequential images–based process monitoring", International Journal of Engineering,  Vol. 33, No. 7, (2020), 1285-1292. DOI: 10.5829/ije.2020.33.07a.15
18.   Poissant, J. and Barthelat, F., "A novel “subset splitting” procedure for digital image correlation on discontinuous displacement fields", Experimental Mechanics,  Vol. 50, No. 3, (2010), 353-364.
19.   Li, X., Fang, G., Zhao, J., Zhang, Z. and Wu, X., "Local hermite (lh) method: An accurate and robust smooth technique for high-gradient strain reconstruction in digital image correlation", Optics and Lasers in Engineering,  Vol. 112, No., (2019), 26-38. DOI: 10.1016/j.optlaseng.2018.08.022
20.   Gonzalez, R.C., Woods, R.E. and Eddins, S.L., "Digital image processing using matlab, Pearson Education India,  (2004).
21.   Pan, B., Dafang, W. and Yong, X., "Incremental calculation for large deformation measurement using reliability-guided digital image correlation", Optics and Lasers in Engineering,  Vol. 50, No. 4, (2012), 586-592. DOI:  10.1016/j.optlaseng.2011.05.005
22.   Peretzki, E., Stockmann, M., Lehmann, T. and Ihlemann, J., "A new surface preparation method for microscopic digital image correlation applications", Materials Today: Proceedings,  Vol. 12, (2019), 377-382. DOI: 10.1016/j.matpr.2019.03.138
23.   Romans, L., "Computed tomography for technologists: A comprehensive text, Lippincott Williams & Wilkins,  (2018).
24.   Hsieh, J., "Computed tomography: Principles, design, artifacts, and recent advances, SPIE press,  Vol. 114,  (2003).
25.   Kak, A.C. and Slaney, M., Principles of computerized tomographic imaging. New york: The institute of electrical and electronics engineers. 1988, Inc.
26.   Wendt, R., The mathematics of medical imaging: A beginner's guide. 2010, Soc Nuclear Med.
27.   Akar, A., Baltaş, H., Çevik, U., Korkmaz, F. and Okumuşoğlu, N., "Measurement of attenuation coefficients for bone, muscle, fat and water at 140, 364 and 662 kev γ-ray energies", Journal of Quantitative Spectroscopy and Radiative Transfer,  Vol. 102, No. 2, (2006), 203-211. DOI:  10.1016/j.jqsrt.2006.02.007
28.   Talebi, S., Sadighi, M. and Aghdam, M., "The effect of impact energy parameters on the closed-cell aluminum foam crushing behavior using x-ray tomography method", AUT Journal of Mechanical Engineering,  Vol. 2, No. 1, (2018), 107-116. DOI: 10.22060/mej.2017.13385.5613
29.   Talebi, S., Sadighi, M., Aghdam, M. and Mirbagheri, S., "Micro–macro analysis of closed-cell aluminum foam with crushing behavior subjected to dynamic loadings", Materials Today Communications,  Vol. 13, (2017), 170-177. DOI: 10.1016/j.mtcomm.2017.10. 004
30.   Systemes, D., Abaqus 6.9 documentation, dassault systemes simulia corp, providence, ri, USA. 2009.
31.   Jamshidi, Y.T., Anaraki, A.P., Sadighi, M., Kadkhodapour, J., Mirbagheri, S.M.H. and Akhavan, B., "Micro-structure analysis of quasi-static crushing and low-velocity impact behavior of graded composite metallic foam filled tube", Metals and Materials International,  (2019), 1-14. DOI: 10.1007/s12540-019-00502-0
International Journal of Engineering
Volume 33, Issue 10
TRANSACTIONS A: Basics
October 2020
Pages 2065-2078

Files

Cited by

Share

How to cite

Statistics