Cu-15Ni-8Sn-0.3Nb alloy rods were prepared by means of powder metallurgy followed by hot extrusion.Element maps obtained by electron probe micro analyzer(EPMA)showed that Nb-rich phases were formed and distributed wit...Cu-15Ni-8Sn-0.3Nb alloy rods were prepared by means of powder metallurgy followed by hot extrusion.Element maps obtained by electron probe micro analyzer(EPMA)showed that Nb-rich phases were formed and distributed within grains and at grain boundaries of the Cu-15Ni-8Sn-0.3Nb alloy.Transmission electron microscope(TEM)results indicated that there was no obvious orientation relationship between these phases and the matrix.Spinodal decomposition and ordering transformation appeared at early stages of aging at400°C and caused significant strengthening.Cu-15Ni-8Sn-0.3Nb alloy exhibited both higher strength(ultimate tensile strength>1030MPa)and higher tensile ductility(elongation>9.1%)than Cu-15Ni-8Sn alloy after aging treatment.The improvement was caused by Nb-rich phases at grain boundaries which led o the refinement of grain size and postponed the growth of discontinuous precipitates during aging.展开更多
A novel data mining approach,based on artificial neural network(ANN) using differential evolution(DE) training algorithm,was proposed to model the non-linear relationship between parameters of aging processes and mech...A novel data mining approach,based on artificial neural network(ANN) using differential evolution(DE) training algorithm,was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy.In order to improve predictive accuracy of ANN model,the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer.The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm.The present calculated results are consistent with the experimental values,which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient.Moreover,the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu-15Ni-8Sn-0.4Si alloy.展开更多
文摘研究了在Cu-9.5Ni-2.3Sn合金中添加质量分数为0.15%的Si后对该合金铸态及时效态微观组织、电导率和硬度的影响.结果表明:添加0.15%的Si后,合金出现发达的树枝状晶体,且有Ni_2Si、Ni_3Si、Ni_3Sn和Ni_4Sn相出现.经400℃×4 h时效处理后,Ni_2Si、Ni_3Si相的析出使得合金得到强化.合金电导率随时效时间的延长和温度的提高而升高,硬度在时效初期随时效温度的提高和时效时间的延长而提高,在430℃时效2 h和在400℃时效8 h得到峰值,较佳时效工艺为400℃×8 h.
基金Project (2016YFB0301400) supported by the National Key Research and Development Program of ChinaProject (9140A12040515QT48167) supported by the Pre-research Fund of the General Armaments Department of ChinaProject (CSU20151024) supported by the Innovation-driven Plan of Central South University,China
文摘Cu-15Ni-8Sn-0.3Nb alloy rods were prepared by means of powder metallurgy followed by hot extrusion.Element maps obtained by electron probe micro analyzer(EPMA)showed that Nb-rich phases were formed and distributed within grains and at grain boundaries of the Cu-15Ni-8Sn-0.3Nb alloy.Transmission electron microscope(TEM)results indicated that there was no obvious orientation relationship between these phases and the matrix.Spinodal decomposition and ordering transformation appeared at early stages of aging at400°C and caused significant strengthening.Cu-15Ni-8Sn-0.3Nb alloy exhibited both higher strength(ultimate tensile strength>1030MPa)and higher tensile ductility(elongation>9.1%)than Cu-15Ni-8Sn alloy after aging treatment.The improvement was caused by Nb-rich phases at grain boundaries which led o the refinement of grain size and postponed the growth of discontinuous precipitates during aging.
基金Project(2002AA302505) supported by the Hi-tech Research and Development Program of China
文摘A novel data mining approach,based on artificial neural network(ANN) using differential evolution(DE) training algorithm,was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy.In order to improve predictive accuracy of ANN model,the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer.The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm.The present calculated results are consistent with the experimental values,which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient.Moreover,the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu-15Ni-8Sn-0.4Si alloy.
