Abstract
Graphene oxide–carbon fiber hybrid reinforced shape memory polymer (GO-CF/SMP) is a composite with both excellent load-bearing capacity and shape memory properties, which is expected to be well suited in the space-expandable structure of aerospace, electronic communication, and other fields application. The preparation process and parameter control directly affect the macroscopic performance of composites. It is important to elucidate the mapping relationship between process parameters and material properties and obtain optimized parameter matching rules for the preparation of composite. In this article, a vacuum impregnation hot-pressing process system (VIHPS) is innovatively adopted to prepare GO-CF/SMP composite. Orthogonal experiment and support vector regression (SVR) analysis are used to determine the weight of the key process parameters in the preparation process on the material properties and construct the process parameter-material performance prediction model. Finally, the optimized process parameter matching law is obtained. When the curing temperature is 80 °C, the curing time is 150 min, the extrusion force is 0.9 MPa, and the vacuum degree is −0.09 MPa, the composite has the best mechanical properties and comprehensive shape memory performance. Its bending strength and comprehensive shape memory performance can reach 465.02 MPa and 96.76%, respectively. The process parameter-material performance prediction model based on SVR has a good degree of fit. The prediction accuracy of the bending strength performance and the comprehensive shape memory performance can reach 99.55% and 94.77%, respectively. The research in this paper provides new ideas for the preparation of GO-CF/SMP cross-scale materials and optimization of process parameters.
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Alok K-S, Vidit G, Chandra S-Y, Aparna S (2019) Flexural strength enhancement in carbon-fiber epoxy composites through graphene nano-platelets coating on fibers. Compos Part B-Eng 179:107539. https://doi.org/10.1016/j.compositesb.2019.107539
Pathak A-K, Borah M, Gupta A, Yokozeki T, Dhakate S-R (2016) Improved mechanical properties of carbon fiber/graphene oxide-epoxy hybrid composites. Compos Sci Technol 135:28–38
Yue C-Y, Tang X-Z, Jiang Z, Yang J, Yu B (2014) Enhanced interphase between epoxy matrix and carbon fiber with carbon nanotube-modified silane coating. Compos Sci Technol 99:131–140
Li F-F, Liu Y-J, Leng J-S (2020) Progress of shape memory polymers and their composites in aerospace applications. J Astronaut 41(6):697–706
Zheng W, Wang Y-L, Zhang F-H, Li C-Y, Liu Y-J, Leng J-S (2018) Development of shape memory polymers micro/nanofiber membranes in biomedical applications. Sci Sin Tech 48:811–826
Wan X, Zhang F-H, Liu Y-J, Leng J-S (2019) CNT-based electro-responsive shape memory functionalized 3D printed nanocomposites for liquid sensors. Carbon 155:77–87
Dong Y-B, Ni Q-Q, Fu Y-Q (2015) Preparation and characterization of water-borne epoxy shape memory composites containing silica. Compos Part A Appl Sci Manuf 72:1–10
Zhang Z-Y, Zhang H, Shou J-Q, Sun Y-Y, Liu Y-Q (2015) Preparation of reduced graphene oxide-reinforced epoxy resin composites and their shape memory properties. New Carbon Mater 30(5):404–411
Xu T, Zhou S, Cui S, Song N, Shi L, Ding P (2019) Three-dimensional carbon fiber-graphene network for improved thermal conductive properties of polyamide-imide composites. Compos Part B-Eng 178:107495. https://doi.org/10.1016/j.compositesb.2019.107495
Shi Y-Y, Yu T, He X-D, Chao K, Zhang X-Y, Zhang J (2015) Mechanism and optimization of process parameters coupling for composite tape winding. Acta Materiae Compositae Sinica 32(3):831–839
Genevieve P, Pascal H, Mohsan H, Larry L (2008) Optimization of RTM processing parameters for Class A surface finish. Compos Part B-Eng 39:1280–1286
Zeng C-J, Liu L-W, Bian W-F, Liu Y-J, Leng J-S (2020) 4D printed electro-induced continuous carbon fiber reinforced shape memory polymer composites with excellent bending resistance. Compos Part B-Eng 194:108034. https://doi.org/10.1016/j.compositesb.2020.108034
Jiang Z-L, Liu Y-Y, Chen H-P, Zhang Y-N, Hu Q-X (2015) Multi-objective optimization of process parameters for biological 3D printing composite forming based on SNR and grey correlation degree. Int J Adv Manuf Technol 80:549–554
Peng A-H, Xiao X-M, Yue R (2014) Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. Int J Adv Manuf Technol 73:87–100
Rajesh K-P, Sohan K-G, Dinesh K-R, Bankim C-R (2017) Reinforcement effect of graphene oxide in glass fibre/epoxy composites at in-situ elevated temperature environments: an emphasis on graphene oxide content. Compos Part A Appl Sci Manuf 95:40–53
Pui-yan H, Kin-tak L, Kun Q, Bronwyn F, Nishar H (2019) Property enhancement of CFRP composites with different graphene oxide employment methods at a cryogenic temperature. Compos Part A Appl Sci Manuf 120:56–63
Ma Y-Q, Zhao Y-T, Xu W, Wang J, Chen Y, Li K-F (2020) Research status and development trend of high thermal conductivity graphene-carbon fiber hybrid reinforced shape memory plastic composite. Acta Materiae Compositae Sinica 37(10):2367–2375
Ma Y-Q, Wang J, Zhao Y-T, Wei X-L, Ju L-Y, Chen Y (2020) A new vacuum pressure infiltration CFRP method and preparation experimental study of composite. Polymers 12(2):419. https://doi.org/10.3390/polym12020419
Ma Y-Q, Ren X-Y, Shi Y, Yan H-T, Liu Y-B, Chen G-M (2020) Method of vacuum impregnation and hot pressing curing for carbon fiber composites. Chinese invention patent CN1090409761
Roy R-K (2001) Design of experiments using the Taguchi approach: 16 steps to product and process improvement. John Wiley and Sons, New York
Ch Sudheer R, Maheswaran BK, Panigrahi SM (2014) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Appl 24:1381–1389
Yang G-C, Chen Z-J, Li Y, Su Z-D (2019) Rapid relocation method for mobile robot based on improved ORB-SLAM2 algorithm. Remote Sens 11(2):149. https://doi.org/10.3390/rs11020149
Lin J-C, Li Y, Yang G-C (2021) FPGAN: Face de-identification method with generative adversarial networks for social robots. Neural Netw 133:132–147
Su Z, Li Y, Yang G (2020) Dietary composition perception algorithm using social robot audition for Mandarin Chinese. IEEE Access 8:8768–8782
Ye J-J, Wang Y-W, Li Z-W, Saafi M, Jia F, Huang B, Ye J-Q (2020) Failure analysis of fiber-reinforced composites subjected to coupled thermo-mechanical loading. Compos Struct 235:111756. https://doi.org/10.1016/j.compstruct.2019.111756
Acknowledgements
The authors are grateful for the financial support from the National Natural Science Foundation of China (No. 51705389, 51805401). This study was funded by the Ministry of education production university cooperation education project of China (Grant Number 201902004016), the New experiment and equipment development project of Xidian University (Grant Number SY1954), and the Fundamental Research Funds for the Central Universities and Innovation Fund of Xidian University (Grant Number 5004-20109205867).
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YM contributed to research thought, results analysis. JW contributed to experimental design, writing the paper. JM contributed to resources. HG contributed to preparation. Fei Li contributed to characterization. YZ contributed to mechanical performance test. YC contributed to shape memory performance test. PW contributed to review and editing. The author’s contribution corresponds their order. All authors read and approved the final manuscript.
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Yuqin Ma and Jie Wang are co-first authors of the article.
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Ma, Y., Wang, J., Ma, J. et al. Investigation on optimization of preparation process parameters of GO-CF/SMP composites prepared by VIHPS. J Mater Sci 57, 4541–4555 (2022). https://doi.org/10.1007/s10853-022-06932-3
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DOI: https://doi.org/10.1007/s10853-022-06932-3