A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Z...A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.展开更多
Age hardening in Cu-3.2Ni-0.75Si (wt pct) and Cu-1.0Ni-0.25Si (wt pct) alloys from 723 to 823 K is studied. After an incubation period strengthening appears which is due to precipitates in the Cu-l.ONi-0.25Si (wt pct)...Age hardening in Cu-3.2Ni-0.75Si (wt pct) and Cu-1.0Ni-0.25Si (wt pct) alloys from 723 to 823 K is studied. After an incubation period strengthening appears which is due to precipitates in the Cu-l.ONi-0.25Si (wt pct) alloy. On other hand an immediate increase of the yield strength characterizes the aging of the alloy. This is followed by the regions of constant yield strength and further by a peak. The microstructure of the alloy was studied by, means of transmission electron microscope (TEM) and X-ray diffraction (XRD). Spinodal decomposition takes place followed by nucleation of the ordering coherent (Cu,Ni)3Si particles, further precipitation annealing coherent δ-Ni2Si nucleated within the (Cu,Ni)3Si particle. Any change of the yield strength can be described by an adequate change of the structure in the sample. The nature of the aging curves with a 'plateau' is discussed. The formulas of Ashby and Labusch can be used to explain the precipitation.展开更多
基金This work was supported by the stae“863 plan”,under Grant No.2002AA331112by the Major Science and Technology Project of Henan Province,China,under Grant No.0122021300.
文摘A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.
基金supported by the National Natural Science Foundation of China under contract No.50071026.
文摘Age hardening in Cu-3.2Ni-0.75Si (wt pct) and Cu-1.0Ni-0.25Si (wt pct) alloys from 723 to 823 K is studied. After an incubation period strengthening appears which is due to precipitates in the Cu-l.ONi-0.25Si (wt pct) alloy. On other hand an immediate increase of the yield strength characterizes the aging of the alloy. This is followed by the regions of constant yield strength and further by a peak. The microstructure of the alloy was studied by, means of transmission electron microscope (TEM) and X-ray diffraction (XRD). Spinodal decomposition takes place followed by nucleation of the ordering coherent (Cu,Ni)3Si particles, further precipitation annealing coherent δ-Ni2Si nucleated within the (Cu,Ni)3Si particle. Any change of the yield strength can be described by an adequate change of the structure in the sample. The nature of the aging curves with a 'plateau' is discussed. The formulas of Ashby and Labusch can be used to explain the precipitation.
