A nonlinear cumulative evolution model for corrosion fatigue damage was proposed. Corrosion fatigue damage was considered as a nonlinear cumulative result of stress corrosion damage and fatigue damage. The influences of stress corrosion damage and fatigue damage on corrosion fatigue damage and damage evolution life were studied from a phenomenological point of view. The relevant damage parameters were determined by the experimental results of the LY12CZ aluminum alloy, and the corrosion fatigue life evaluation model based on damage evolution law was established. The corrosion fatigue life predicted by evaluation model agrees well with the experimental result. The damage evolution model in this study can provide a new method for theoretical research and life prediction of corrosion fatigue.

A thermal fatigue life prediction model of microelectronic chips based on thermal fatigue tests and solder/substrate interfacial singularity analysis from finite element method (FEM) analysis is established in this paper. To save the calculation of interfacial singular parameters of new chips for life prediction, and improve the accuracy of prediction results in actual applications, a hybrid genetic algorithm–artificial neural network (GA–ANN) strategy is utilized. The proposed algorithm combines the local searching ability of the gradient-based back propagation (BP) strategy with the global searching ability of a genetic algorithm. A series of combinations of the dimensions and thermal mechanical properties of the solder and the corresponding singularity parameters at the failure interface are used to train the proposed GA-BP network. The results of the network, together with the established life prediction model, are used to predict the thermal fatigue lives of new chips. The comparison between the network results and thermal fatigue lives recorded in experiments shows that the GA-BP strategy is a successful prediction technique.