S.C. Li

Effects of aging temperature on microstructure and high cycle fatigue performance of 7075 aluminum alloy

The hardness, the tensile and the high-cycle fatigue (HCF) performances of 7075 aluminum alloy were investigated under temper T651, solution treated at 380 °C for 0.5 h and aged at different temperatures (150, 170, 190 °C) for 10 hours. The optimal microstructures and the fatigue fracture surfaces were observed. The results show that the hardness and the tensile performances are at their optimum at T651, but the fatigue life is the shortest. The hardness and the elongation are the lowest after solution treatment. With the aging temperature increasing (150-190 °C), the HCF is improved. The crack is initiated from the impurity particles on the subsurface. Treated at 170 °C,the area of the quasi-cleavage plane and the width of parallel serrated sections of the crack propagation are the largest. With increasing aging temperature, the dimple size of finally fracture surfaces becomes larger and the depth deeper.

Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

Purpose The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods. Design/methodology/approach The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed. Findings The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly. Originality/value A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.

Trace Element Effects on Microstructure and Mechanical Properties in Al-Cu-Li Alloy after Thermal Exposure

The effects of trace Ce, Ag on the microstructure and mechanical properties of Al-Cu-Li alloy after thermal exposure have been investigated. It’s found that the addition of Ce may lead to a slight increase in mechanical properties after thermal exposure at 107 . The stability of T1 phase is enhanced by independent Ag addition and the combined additions of Ag and Ce,which results in higher strength compared with Ag-free alloy at 150 thermal exposure. However, at 200 exposure, a great number of q¢ precipitates at the expense of T1 may be responsible for higher tensile strength in the Ag-free alloy than that of the independent addition of Ag and combined additions of Ag and Ce alloys.