Oxidation behaviors of carbon fiber reinforced multilayer SiC–Si_(3)N_(4) matrix composites

Oxidation behaviors of carbon fiber reinforced SiC matrix composites(C/SiC)are one of the most noteworthy properties.For C/SiC,the oxidation behavior was controlled by matrix microcracks caused by the mismatch of coefficients of thermal expansion(CTEs)and elastic modulus between carbon fiber and SiC matrix.In order to improve the oxidation resistance,multilayer SiC–Si_(3)N_(4) matrices were fabricated by chemical vapor infiltration(CVI)to alleviate the above two kinds of mismatch and change the local stress distribution.For the oxidation of C/SiC with multilayer matrices,matrix microcracks would be deflected at the transition layer between different layers of multilayer SiC–Si_(3)N_(4) matrix to lengthen the oxygen diffusion channels,thereby improving the oxidation resistance of C/SiC,especially at 800 and 1000℃.The strength retention ratio was increased from 61.9%(C/SiC–SiC/SiC)to 75.7%(C/SiC–Si_(3)N_(4)/SiC/SiC)and 67.8%(C/SiC–SiC/Si_(3)N_(4)/SiC)after oxidation at 800℃for 10 h.

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(C N C)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08%mea n relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91%mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.

Thermal conductivity and bending strength of SiC composites reinforced by pitch-based carbon fibers

In this work,pitch-based carbon fibers were utilized to reinforce silicon carbide(SiC)composites via reaction melting infiltration(RMI)method by controlling the reaction temperature and resin carbon content.Thermal conductivities and bending strengths of composites obtained under different preparation conditions were characterized by various analytical methods.Results showed the formation of SiC whiskers(SiC_(w))during RMI process according to vapor–solid(VS)mechanism.SiC_(w) played an important role in toughening the C_(pf)/SiC composites due to crack bridging,crack deflection,and SiC_(w) pull-out.Increase in reaction temperature during RMI process led to an initial increase in thermal conductivity along in-plane and thickness directions of composites,followed by a decline.At reaction temperature of 1600℃,thermal conductivities along the in-plane and thickness directions were estimated to be 203.00 and 39.59 W/(m×K),respectively.Under these conditions,bending strength was recorded as 186.15±3.95 MPa.Increase in resin carbon content before RMI process led to the generation of more SiC matrix.Thermal conductivities along in-plane and thickness directions remained stable with desirable values of 175.79 and 38.86 W/(m×K),respectively.By comparison,optimal bending strength improved to 244.62±3.07 MPa.In sum,these findings look promising for future application of pitch-based carbon fibers for reinforcement of SiC ceramic composites.

Rare earth monosilicates as oxidation resistant interphase for SiCf/SiC CMC:Investigation of SiC_(f)/Yb_(2)SiO_(5)model composites

Model composites consisting of SiC fiber and Yb_(2)SiO_(5)were processed by the spark plasma sintering(SPS)method.The mechanical compatibility and chemical stability between Yb_(2)SiO_(5)and SiC fiber were studied to evaluate the potential application of Yb monosilicate as the interphase of silicon carbide fiber reinforced silicon carbide ceramic matrix composite(SiC_(f)/SiC CMC).Two kinds of interfaces,namely mechanical and chemical bonding interfaces,were achieved by adjusting sintering temperature.SiC_(f)/Yb_(2)SiO_(5)interfaces prepared at 1450 and 1500℃exhibit high interface strength and debond energy,which do not satisfy the crack deflection criteria based on He-Hutchison diagram.Raman spectrum analyzation indicates that the thermal expansion mismatch between Yb_(2)SiO_(5)and SiC contributes to high compressive thermal stress at interface,and leads to high interfacial parameters.Amorphous layer at interface in model composite sintered at 1550℃is related to the diffusion promoted by high temperature and DC electric filed during SPS.It is inspired that the interfacial parameters could be adjusted by introducing Yb_(2)Si_(2)O_(7)-Yb_(2)SiO_(5)interphase with controlled composition to optimize the mechanical fuse mechanism in SiC_(f)/SiC CMC.