مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

مطالعه عددی- تجربی اثر نرخ کرنش بر خواص کششی نانوکامپوزیت‌های گرافن- اپوکسی

نوع مقاله : پژوهشی اصیل

نویسندگان
1 گروه مکانیک، دانشکده مهندسی، دانشگاه بوعلی سینا، همدان، ایران
2 گروه کامپوزیت، پژوهشکده فرآیند پلمیرها، پژوهشگاه پلیمر و پتروشیمی ایران، تهران، ایران
چکیده
در این پژوهش، اثر نرخ کرنش بر رفتار کششی نانوکامپوزیت‌های گرافن- اپوکسی مورد بررسی قرار گرفت. نمونه‌ها برای ۰/۰۵، ۰/۱، ۰/۳ و ۰/۵ درصد وزنی گرافن اکساید آماده‌سازی شدند و تحت آزمون کشش در نرخ‌های کرنش مختلف قرار گرفتند. نتایج تجربی نشان داد که حداکثر بهبود در استحکام کششی و مدول به ترتیب ۹ و ۱۶% و برای نانوکامپوزیت، ۰/۱ درصد وزنی به‌دست آمده بودند. همچنین، نتایج نشان داد که اپوکسی و نانوکامپوزیت‌های آن به نرخ کرنش حساس بودند. حساسیت به نرخ با افزایش درصدهای وزنی گرافن، کاهش یافت. علاوه‌بر آن نشان داده شد که با افزایش نرخ کرنش، استحکام کششی و مدول اپوکسی خالص به ترتیب ۱۵/۸ و ۱۶/۸% بهبود یافتند. در این مطالعه، تناسب و کارآیی مدل ماده جانسون- کوک برای توصیف ارتباط تنش- کرنش نانوکامپوزیت‌ها، به کمک تکنیک ترکیب آزمایشی- عددی- بهینه‌سازی ارزیابی شد. شبیه‌سازی‌های عددی با استفاده از برنامه تجاری آباکوس و بهینه‌سازی، با استفاده از مدل‌سازی جایگزینی انجام گرفت. نتایج نشان داد که در نرخ‌های کرنش خیلی پایین، مدل جانسون- کوک دقیق نیست. با این حال، با افزایش درصد وزنی گرافن یا افزایش نرخ کرنش، دقت مدل بهبود چشمگیری یافت.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Numerical and Experimental Investigation of the Strain Rate Effect on Tensile Properties of Graphene/Epoxy Nanocomposites

نویسندگان English

Sh. Amini Nejad 1
G.H. Majzoobi 1
S.A.R. Sabet 2
1 Mechanical Engineering Department, Engineering Faculty, Bu-Ali Sina University, Hamedan, Iran
2 Composite Department, Polymer Processing Faculty, Iran Polymer & Petrochemical Institute, Tehran, Iran
چکیده English

In this research, the effect of strain rate on the tensile behavior of the graphene/epoxy nanocomposites was investigated. The specimens were prepared for 0.05, 0.1, 0.3 and 0.5 wt.% graphene oxide and were subjected to tensile tests at different strain rates. The experimental results showed that the maximum improvements in the tensile strength, the modulus, and nanocomposite were 9%, 16%, and 0.1 wt.%, respectively. Also, the results indicated that the epoxy and its nanocomposites were sensitive to the strain rate. The rate sensitivity decreased with the increase of the graphene weight percentages. Moreover, it was shown that by increasing the strain rate, the tensile strength and modulus for pure epoxy were improved by 15.8% and 16.8%, respectively. In this study, the appropriateness and applicability of the Johnson-Cook material model for describing the stress-strain relation of the nanocomposites were examined by a combined experimental-numerical-optimization technique. The numerical simulations were carried out using Abaqus commercial program and the optimizations were performed using the Surrogate modeling. The results showed that the Johnson-cook model is not accurate at very low strain rates. However, the accuracy of the model was remarkably improved by increasing the graphene weight percentage or increasing strain rate.

کلیدواژه‌ها English

Epoxy
Graphene oxide
Strain rate
strength
Johnson-Cook Model
Xu B, Yue Sh, Sui Z, Zhang X, Hou Sh, Cao G, et al. What is the choice for supercapacitors: Graphene or graphene oxide?. Energy & Environmental Science. 2011;4(8):2826-2830. [Link] [DOI:10.1039/c1ee01198g]
Zhu Y, Murali Sh, Cai W, Li X, Suk JW, Potts JR, et al. Graphene and graphene oxide: Synthesis, properties, and applications. Advanced Materials. 2010;22(35):3906-3924. [Link] [DOI:10.1002/adma.201001068]
Du J, Cheng HM. The fabrication, properties, and uses of graphene/polymer composites. Macromolecular Chemistry and Physics. 2012;213(10‐11):1060-1077. [Link] [DOI:10.1002/macp.201200029]
Gómez-del Río T, Rodríguez J. Compression yielding of epoxy: Strain rate and temperature effect. Materials & Design. 2012;35:369-373. [Link] [DOI:10.1016/j.matdes.2011.09.034]
Phan HT. High strain rate behavior of graphene reinforced polyurethane composites [Dissertation]. Columbia: University of Missouri-Columbia; 2012. [Link]
Shokrieh MM, Esmkhani M, Shahverdi HR, Vahedi F. Effect of graphene nanosheets (GNS) and graphite nanoplatelets (GNP) on the mechanical properties of epoxy nanocomposites. Science of Advanced Materials. 2013;5(3):260-266. [Link] [DOI:10.1166/sam.2013.1453]
Yao H, Hawkins SA, Sue HJ. Preparation of epoxy nanocomposites containing well-dispersed graphene nanosheets. Composites Science and Technology. 2017;146:161-168. [Link] [DOI:10.1016/j.compscitech.2017.04.026]
Atif R, Shyha I, Inam F. Modeling and experimentation of multi-layered nanostructured graphene-epoxy nanocomposites for enhanced thermal and mechanical properties. Journal of Composite Materials. 2017;51(2):209-220. [Link] [DOI:10.1177/0021998316640060]
Soltannia B, Haji Gholami I, Masajedian S, Mertiny P, Sameoto D, Taheri F. Parametric study of strain rate effects on nanoparticle-reinforced polymer composites. Journal of Nanomaterials. 2016;2016:9841972. [Link] [DOI:10.1155/2016/9841972]
Shadlou Sh, Ahmadi-Moghadam B, Taheri F. The effect of strain-rate on the tensile and compressive behavior of graphene reinforced epoxy/nanocomposites. Materials & Design. 2014;59:439-447. [Link] [DOI:10.1016/j.matdes.2014.03.020]
Chen W, Lu F, Cheng M. Tension and compression tests of two polymers under quasi-static and dynamic loading. Polymer Testing. 2002;21(2):113-121. [Link] [DOI:10.1016/S0142-9418(01)00055-1]
Naik NK, Shankar PJ, Kavala VR, Ravikumar G, Pothnis JR, Arya H. High strain rate mechanical behavior of epoxy under compressive loading: Experimental and modeling studies. Materials Science and Engineering: A. 2011;528(3):846-854. [Link] [DOI:10.1016/j.msea.2010.10.099]
Zarei Darani S, Naghdabadi R, Jokar E, Irajizad A. Experimental study on mechanical properties of graphene oxide/epoxy nonocomposites in different strain rates. Modares Mechanical Engineering. 2017;16(12):61-66. [Persian] [Link]
Shokrieh MM, Joneidi VA. Characterization and simulation of impact behavior of graphene/polypropylene nanocomposites using a novel strain rate-dependent micromechanics model. Journal of Composite Materials. 2015;49(19):2317-2328. [Link] [DOI:10.1177/0021998314545191]
Shokrieh MM, Mosalmani R, Omidi MJ. Strain rate dependent micromechanical modeling of reinforced polymers with carbon nanotubes. Journal of Composite Materials. 2014;48(27):3381-3393. [Link] [DOI:10.1177/0021998313509864]
Shokrieh MM, Shamaei Kashani AR, Mosalmani R. A dynamic constitutive-micromechanical model to predict the strain rate-dependent mechanical behavior of carbon nanofiber/epoxy nanocomposites. Iranian Polymer Journal. 2016;25(6):487-501. [Link] [DOI:10.1007/s13726-016-0441-9]
Ghaleb ZA, Mariatti M, Ariff ZM. Properties of graphene nanopowder and multi-walled carbon nanotube-filled epoxy thin-film nanocomposites for electronic applications: The effect of sonication time and filler loading. Composites Part A: Applied Science and Manufacturing. 2014;58:77-83. [Link] [DOI:10.1016/j.compositesa.2013.12.002]
Majzoobi GH, Amini Nejad SH, Sabet SA. Role of graphene oxide and sonication time on mechanical properties of epoxy nanocomposites at high strain rate. Materials Research Express. 2019;6(6):065063. [Link] [DOI:10.1088/2053-1591/ab0fde]
Johnson GR, Cook WH. A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures. The 7th International Symposium on Ballistics, 1983 April 19-21, The Hague, The Netherlands. Welfengarten: Tib; 1983. pp. 541-547. [Link]
Regis RG. Particle swarm with radial basis function surrogates for expensive black-box optimization. Journal of Computational Science. 2014;5(1):12-23. [Link] [DOI:10.1016/j.jocs.2013.07.004]
Payandehpeyman J, Majzoobi GH, Bagheri R. Determination of the extended Drucker-Prager parameters using the surrogate-based optimization method for polypropylene nanocomposites. The Journal of Strain Analysis for Engineering Design. 2016;51(3):220-232. [Link] [DOI:10.1177/0309324715627564]
Mack Y, Goel T, Shyy W, Haftka R. Surrogate model-based optimization framework: A case study in aerospace design. In: Yang Sh, Ong YS, Jin Y, editors. Evolutionary computation in dynamic and uncertain environments. Heidelberg: Springer; 2007. pp. 323-342. [Link] [DOI:10.1007/978-3-540-49774-5_14]
Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Tucker PK. Surrogate-based analysis and optimization. Progress in Aerospace Sciences. 2005;41(1):1-28. [Link] [DOI:10.1016/j.paerosci.2005.02.001]
MüLler J, Shoemaker CA, Piché R. SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems. Computers & Operations Research. 2013;40(5):1383-1400. [Link] [DOI:10.1016/j.cor.2012.08.022]
Müller J. Surrogate model algorithms for computationally expensive black-box global optimization problems [Dissertation]. Tampere: Tampere University of Technology; 2012. [Link]
Tian Y, Zhang H, Zhao J, Li T, Bie BX, Luo SN, et al. High strain rate compression of epoxy based nanocomposites. Composites Part A: Applied Science and Manufacturing. 2016;90:62-70. [Link] [DOI:10.1016/j.compositesa.2016.06.008]