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Tianxiang Sun,Kai Jin,Wei Wang,Wen Li,Tong Wang,Tengxun Yang,Jia Cheng,Zhipeng Zhao,Shougang Chen 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.128 No.-
Here, a long-term anti-corrosive epoxy (EP) coating based on nano-hybrid filler was developed. The surfaceof graphene oxide (GO) was aminated with (3-aminopropyl) triethoxysilane (APTES), and then theporous covalent organic framework (COF) was in-situ grown on amino functionalized graphene oxide(FGO) nanosheets by Schiff-based reaction to prepare FGO-COF nanofillers. Different characterizationmethods proved the successful modification of GO nanosheets with COF particles. The excellent waterresistance of composite coatings was characterized through contact angle, adhesion, and water uptaketests. Transmission electron microscope (TEM) and molecular dynamics (MD) methods showed thatthe grafting of COF enhanced the dispersion and compatibility of GO in the coating. EIS measurements,salt spray analysis and cathodic delamination test indicated the significant improvements in the protectiveproperties of the composite coatings compared with pure EP coating. The low-frequency impedancemodulus of 1% FGO-COF composite coating reached higher than 6.8 109 Xcm2 after immersed in seawater for 60 days. In addition, the 1% FGO-COF composite coating performed only 34% delamination ratioafter 120 h cathodic delamination test.
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Guan Wang,Pei Zhang,Linyuan Kou,Yan Wu,Tianxiang Wen,Xin Shang,Zhiwen Liu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.2
The hot deformation behavior of the Al-Zn-Mg-Cu alloy was studied by isothermal tensile tests in the temperature range of 200-350 °C and the strain rate range of 0.001-0.1 s -1 . A data-driven deep neural network (DNN) constitutive model and a phenomenological Arrhenius constitutive model were developed for the studied alloy model. The parameters of the DNN model were optimized to improve the prediction accuracy of flow stress. The results show that the accuracy of predictions of the DNN model is better than the Arrhenius model for the hot deformation behavior of 7075 aluminum alloy. The average absolute relative error and correlation coefficient of the DNN model is 1.70 % and 0.9996, respectively. The accuracy of the constitutive model of Arrhenius is relatively low for 7075 aluminum alloy in the range 200-350 °C, 0.001-0.1 s -1 . The optimal network depth and the number of neurons per layer for the analytically optimized DNN constitutive model are 6 and 28, respectively. In addition, the developed DNN model can be effectively applied in intelligent manufacturing, such as short-process highefficiency hot stamping and other plastic-forming technologies.
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