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Sliding Friction and Wear Behavior of C/C Composites Against 40 Cr Steel
Yicheng Ge,Maozhong Yi,Huijuan Xu,Ke Peng,Lin Yang 한국탄소학회 2009 Carbon Letters Vol.10 No.2
In this work, effects of carbon matrix on sliding friction and wear behavior of four kinds of C/C have been investigated against 40 Cr steel ring mate. Composite A with rough lamination carbon matrix (RL) shows the highest volume loss and coefficient of friction, while composite D with smooth lamination/resin carbon matrix (SL/RC) shows the lowest volume loss. The worn surface of composite A appears smooth, whereas that of composite C with smooth lamination carbon (SL) appears rough. The worn surface of composite D appears smooth under low load but rough under high load. Atomic force microscope images show that the size of wear particles on the worn surface is also dependent on the carbon matrix.
Shenshun Ying,Yicheng Sun,Chentai Fu,Lvgao Lin,Shunqi Zhang 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
Broaching tool condition monitoring is the basis of intelligent manufacturing of high-end broaching equipment. There are still technical bottlenecks in tool wear recognition accuracy and response speed. Aiming at the characteristics of complex cutter tooth shape and variable spatial distribution of turbine disc fir-tree slot broaching tool, a method of wear state recognition for broaching tool based on maximum relevance and minimum redundancy and gray wolf optimization algorithm is proposed. In the process of broaching, the broach vibration signals are collected in real time. The signal characteristics in time domain, frequency domain and time-frequency domain are extracted by signal processing technology, and the support vector machine (SVM) recognition model of broach wear state is established. The maximum relevance and minimum redundancy (mRMR) method is used to reduce the dimension of data, grey wolf optimization algorithm (GWO) is used to optimize parameters to improve the recognition accuracy of SVM. The experimental results show that the model can accurately recognize the wear state of fir-tree slot broach at different stages. In addition, grey wolf optimizationsupport vector machine (GWO-SVM) model shows higher accu-racy in classification than particle swarm optimization based support vector machine (PSO-SVM) and genetic algorithm based support vector machine (GA-SVM) models. Compared with PSO-SVM and GA-SVM models, the computational time of GWO-SVM is reduced by 54.2 % and 60.5 % respectively.
C<SUB>f</SUB>/C-Cu- New Sliding Electrical Contact Materials
Liping Ran,Maozhong Yi,Ke Peng,Lin Yang,Yicheng Ge 한국탄소학회 2009 Carbon Letters Vol.10 No.2
[ Cf/C-Cu ]composites were fabricated by infiltrating molten Cu into different Cf/C preforms prepared by chemical vapor infiltration, resin impregnation and carbonization. The microstructure and properties of the composites were investigated. The results show that Cu in the composites filled the pores and showed network-like distribution. Compared with homemade J204 brush material and certain grade pantograph slider from abroad, the composites have higher flexural strength and better electrical conductivity. The friction and wear properties of the composites are better than that of J204, and closed to that of the abroad material.