The materials with grain size gradient variation on the surface, which were prepared with mechanical-induced severe plastic deformation, always show high resistance to high and low cycle fatigue and frictional wear because of their good strength–ductility synergy. The ultrasonic surface rolling treatment (USRT) has the advantages of high processing efficiency, good surface quality, and large residual compressive stress introduced to the surface after treatment. The USRT was used to prepare aluminum alloy (AA7075) samples with a surface gradient structure; meanwhile, the microstructural evolution mechanism of the deformation layers on the gradient structure was studied with XRD, SEM, and TEM. The microstructure with gradient distribution of grain size and dislocation density formed on the surface of AA7075 aluminum alloy after USRT. The surface layer consists of nanocrystals with random orientation distribution, and high-density dislocation cells and subgrains formed in some grains in the subsurface layer, while the center of the material is an undeformed coarse-grained matrix. The results show that the dislocation slip dominates the grain refinement process, following the continuous cutting and refinement of dislocation cells, subgrains, and fragmentation of the second precipitates. This study systematically clarified the mechanism of grain refinement and nanocrystallization on the surface of high-strength aluminum alloys and laid a theoretical foundation for further research on mechanical behavior and surface friction and wear properties of high-strength non-ferrous materials with gradient structure.

In this paper, eight variables of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and flow are used as network input and slump is used as network output to construct a back-propagation (BP) neural network. On this basis, the learning rate, momentum factor, number of hidden nodes and number of iterations are used as hyperparameters to construct 2-layer and 3-layer neural networks respectively. Finally, the response surface method (RSM) is used to optimize the parameters of the network model obtained previously. The results show that the network model with parameters obtained by the response surface method (RSM) has a better coefficient of determination for the test set than the model before optimization, and the optimized model has higher prediction accuracy. At the same time, the model is used to evaluate the influencing factors of each variable on slump. The results show that flow, water, coarse aggregate and fine aggregate are the four main influencing factors, and the maximum influencing factor of flow is 0.875. This also provides a new idea for quickly and effectively adjusting the parameters of the neural network model to improve the prediction accuracy of concrete slump.

Here, the corrosion weight-loss method, surface analysis technology, and electrochemical test methods were used to study the corrosion behavior and electrochemical characteristics of experimental samples of Q345R steel in a sterile solution (pH 2.0) and a solution containing T. ferrooxidans. The growth cycle of T. ferrooxidans was determined to be approximately 8 days. The corrosion weight-loss method showed that the corrosion rate of Q345R carbon steel coupons decreased with time in the T. ferrooxidans system and the sterile system; the corrosion rate was approximately two times higher in the T. ferrooxidans system than in the sterile system. The corrosion morphology results showed that the presence of T. ferrooxidans promotes the corrosion of Q345R steel and increases the local corrosion of the matrix material. The electrochemical test results showed that after 5 days of corrosion, the polarization resistance of the T. ferrooxidans system was approximately 50% of that of the sterile system, and the corrosion current density of the T. ferrooxidans system was approximately twice as high as that of the sterile system. Therefore, T. ferrooxidans can accelerate the corrosion of Q345R steel two-fold.

Topology-regulated load transfer mechanisms and mechanical performance of sheet-based TPMS titanium scaffolds, Fu, L, Li, Yongzhi, Zhang, Qian, Liu, Ming, Bi, Shihao, Yao, Ji, Wang, Fuyou, Ma, Shuai Topology-regulated load transfer mechanisms and mechanical performance of sheet-based TPMS titanium scaffolds, Fu, L, Li, Yongzhi, Zhang, Qian, Liu, Ming, Bi, Shihao, Yao, Ji, Wang, Fuyou, Ma, Shuai