Evolutionary artificial neural network approach for predicting properties of Cu- 15Ni-8Sn-0.4Si alloy

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.     

B4C材料泊磨擦特性研究

简要地介绍了Ｂ４Ｃ材料的主要制备方法,摩擦特性和获得超低摩擦因数的机理,提出了获得超低摩擦因数的方法,并指出了现在存在的问题和对未来的展望。     

PLS-BPN法用于7005铝合金力学性能与工艺参数的定量研究

用偏最小二乘法（PLS）结合反向传播人工神经网络（BPN）方法对7005铝合金力学性能与工艺参数之间的关系进行定性分析和计算。结果表明：用PLS法对实验数据作模式识别优化处理的结果与实验很吻合，能够指明该合金工艺参数优化的方向；用BPN定量计算的结果与实验测定值符合也较好：将PLS与BPN法有机地联系起来，有利于克服过拟合，提高BPN预报的准确性。用留一（LOO）交叉验证法分别对3种模型PLS、BPN和PLS—BPN的合金性能预报结果进行验证，其中PLS-BPN模型预测的均方根误差（RMSE）和平均相对误差（MRE）均最低，更适合于7005铝合金性能预报。     

高强高导Cu-Cr-Zr系合金材料的研究进展

分析了Cu-Cr-Zr系合金的国内外发展状况，其性能与添加合金化元素、制备技术和显微组织的相互关系，并指出了需进一步研究的几个问题。