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| 1 | Work Hardening Behavior of 1020 Steel During Cold-Beating Simulation显示文摘The present research of cold-beating formation mainly focused on roller design and manufacture, kinematics, constitutive relation, metal flow law, thermo-mechanical coupling, surface micro-topography and microstructure evolution. However, the research on surface quality and performance of workpieces in the process of cold-beating is rare. Cold-beating simulation experiment of 1020 steel is conducted at room temperature and strain rates ranging from 2000 to 4000 s^(-1) base on the law of plastic forming. According to the experimental data, the model of strain hardening of 1020 steel is established,Scanning Electron Microscopy(SEM) is conducted, the mechanism of the work hardening of 1020 steel is clarified by analyzing microstructure variation of 1020 steel. It is found that the strain rate hardening effect of 1020 steel is stronger than the softening effect induced by increasing temperatures, the process of simulation cold-beating cause the grain shape of 1020 steel significant change and microstructure elongate significantly to form a fibrous tissue parallel to the direction of deformation, the higher strain rate, the more obvious grain refinement and the more hardening effect. Additionally, the change law of the work hardening rate is investigated, the relationship between dislocation density and strain, the relationship between work hardening rate and dislocation density is obtained.Results show that the change trend of the work hardening rate of 1020 steel is divided into two stages, the work hardening rate decreases dramatically in the first stage and slowly decreases in the second stage, finally tending toward zero. Dislocation density increases with increasing strain and strain rate, work hardening rate decreases with increasing dislocation density. The research results provide the basis for solving the problem of improving the surface quality and performance of workpieces under cold-beating formation of 1020 steel. | Fengkui CUI Yuanfei LING Jinxue XUE Jia LIU Yuhui LIU Yan LI | 2017 | Chinese Journal of Mechanical Engineering2017，30，2： | 2 |
| 2 | Development of Automatic Fault Tree Synthesis System Using Decision Matrix 显示文摘 | YANG Zongxiao ZHENG Yanyi XUE Jinxue | 2009 | International Journal of Production Economics2009，，121： | 1 |
| 3 | Failure behavior of interfacial domain in SiC-matrix based composites显示文摘In this work,the microstructure,failure behavior and interfacial properties with respective to the interfacial domain in SiCf/BN/SiC and C_(f)/PyC/SiC composites were studied via the fiber push-in test.The differences in the mechanical response of the interfacial domain were observed.During the fiber push-in test for SiCf/BN/SiC,the interface debonding accompanied with interphase fracture occurred,resulting in an obvious sign of the onset of debonding on loading-displacement(P-u)curves.While the good continuity of P-u curves can be observed for Cf/PyC/SiC,which is due to that the failure is in the form of interface debonding along with interphase lateral slipping caused by the extension of buckled carbon fiber,without any interphase fracture.The interfacial properties calculated from the fiber push-in test show that Cf/PyC/SiC possesses a weaker interfacial domain compared with SiC_(f)/BN/SiC.The interfacial shear stress of SiCf/BN/SiC and C_(f)/PyC/SiC composites amounts 94.2 and 48.1 MPa,respectively. | Jinxue Ding Xiaokang Ma Xiaomeng Fan Jimei Xue Fang Ye Laifei Cheng | 2021 | Journal of Materials Science & Technology2021，，29： | 1 |
| 4 | Development of Automatic Fault Tree Synthesis System Using Decision Matrix 显示文摘 | YANG Zongxiao ZHENG Yanyi XUE Jinxue | 2009 | International Journal of Production Economics2009，121，1： | 1 |
| 5 | Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN显示文摘Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not.Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones.Due to a large amount of convolutional computation and the loss of structural information,the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy.To address these issues,we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN)for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features.GNN’s node-centric and small batch is a suitable training way for large CFGs,it can greatly reduce computational overhead.Various NLP basic block embedding models and GNNs are evaluated.Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE)for function representation outperforms the state-of-the-art works.In our framework,we can take only 45%overhead.Improve efficiency significantly with a small performance trade-off. | Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU | 2024 | Chinese Journal of Electronics2024，33，1： | 0 |
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