Anand模型采用有限元法模拟WLCSP器件Sn3.8Ag0.7Cu-X(Ce,Fe)无铅焊点在热循环载荷条件下的应力-应变响应,借助蠕变应变疲劳寿命预测模型Sn Ag Cu,Sn Ag Cu Ce,Sn Ag Cu Fe焊点疲劳寿命.结果表明,在服役器件整体器件出现明显的变形现象,...Anand模型采用有限元法模拟WLCSP器件Sn3.8Ag0.7Cu-X(Ce,Fe)无铅焊点在热循环载荷条件下的应力-应变响应,借助蠕变应变疲劳寿命预测模型Sn Ag Cu,Sn Ag Cu Ce,Sn Ag Cu Fe焊点疲劳寿命.结果表明,在服役器件整体器件出现明显的变形现象,电路板翘曲严重.从中心到拐角焊点变形-应力-应变逐渐增加,芯片下拐角焊点成为整个结构潜在的危险区域.通过计算WLCSP器件Sn Ag Cu、Sn Ag Cu Ce和Sn Ag Cu Fe三种焊点的疲劳寿命,证实了Sn Ag Cu Ce和Sn Ag Cu Fe焊点寿命明显高于Sn Ag Cu焊点,证明了在Sn Ag Cu中添加一定量的铈和铁可以显著提高Sn Ag Cu焊点的使用寿命,分析结果为新型无铅钎料的研发提供理论支撑.展开更多
In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the bes...In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network（NN） to predict the stress-strain curve. In order to achieve this, three different NN architectures（feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network）, three different transfer functions（purelin, Log-Sigmoid, and Tan-Sigmoid）, number of hidden layers（1 and 2）, number of neurons in the hidden layer（s）,and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set（a,16 % a, 40 % a, and b stress-strain curves）. After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress-strain curve of near b titanium alloy.展开更多
文摘Anand模型采用有限元法模拟WLCSP器件Sn3.8Ag0.7Cu-X(Ce,Fe)无铅焊点在热循环载荷条件下的应力-应变响应,借助蠕变应变疲劳寿命预测模型Sn Ag Cu,Sn Ag Cu Ce,Sn Ag Cu Fe焊点疲劳寿命.结果表明,在服役器件整体器件出现明显的变形现象,电路板翘曲严重.从中心到拐角焊点变形-应力-应变逐渐增加,芯片下拐角焊点成为整个结构潜在的危险区域.通过计算WLCSP器件Sn Ag Cu、Sn Ag Cu Ce和Sn Ag Cu Fe三种焊点的疲劳寿命,证实了Sn Ag Cu Ce和Sn Ag Cu Fe焊点寿命明显高于Sn Ag Cu焊点,证明了在Sn Ag Cu中添加一定量的铈和铁可以显著提高Sn Ag Cu焊点的使用寿命,分析结果为新型无铅钎料的研发提供理论支撑.
文摘In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network（NN） to predict the stress-strain curve. In order to achieve this, three different NN architectures（feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network）, three different transfer functions（purelin, Log-Sigmoid, and Tan-Sigmoid）, number of hidden layers（1 and 2）, number of neurons in the hidden layer（s）,and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set（a,16 % a, 40 % a, and b stress-strain curves）. After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress-strain curve of near b titanium alloy.
