Search

Fatigue properties and cracking behavior of cold-drawn commercially pure aluminum wires (CPAWs) widely used as the overhead transmission conductors were investigated. It was found that the fracture surface of the CPAWs shows an obvious four-stage fracture characteristic, i.e., crack initiation, planar crack propagation, 45°-inclined crack propagation and final rapid fracture. The crack growth mechanisms for the CPAWs were found quite different from those for the conventional coarse-grained materials. The cracks in the CPAWs firstly grow along the grain boundaries (Stage I crack growth), and then grow along the plane of maximum shear stress during the last stage of cycling (Stage II crack growth), leading to the distinctive fracture surfaces, i.e., the granular surface in the planar crack propagation region and the coarse fatigue striations in the 45°-inclined crack propagation region. The grain boundary migration was observed in the fatigued CPAWs. The increase in fatigue load enhances the dislocation recovery, increases the grain boundary migration rate, and thus promotes the occurrence of softening and damage localization up to the final failure.

The purpose of material recognition is to identify the main objects and their material categories in natural material images.Aiming at the problem of low recognition accuracy caused by the lack of data in material image data sets and the difficulty of manually labeling local texture regions,a material recognition method based on attention mechanism and deep convolutional neural network is proposed.The core of the method is material recognition deep convolutional neural network (MaterialNet).MaterialNet uses the deep residual network to extract the features of the image,and introduces the attention mechanism by the proposed cascaded atrous spatial pyramid pooling method,so that the network can adaptively focus on the key areas containing texture features through end-to-end training,so as to effectively identify the local texture features of materials.Based on the FMD material datasets,the experimental results show that the overall identification accuracy of MaterialNet is 82.3%,which is 7.2% and 4.5% higher than the current mainstream B-CNN and CNN+FV material identification methods,respectively.The recognition accuracy of MaterialNet is high for a variety of materials,and it has the advantages of less parameters and less calculation.

Equal channel angular pressing (ECAP) is applied to investigate the microstructural evolution and mechanical properties of Cu-8 wt pct Ag alloy subjected to one to four passes via four different routes (A, BA, BC, and C). It is demonstrated that better mechanical properties, a higher fraction of high-angle boundaries, and a smaller grain size can be obtained most rapidly with route A, whereas the specimen processed by route BC contains relatively inhomogeneous microstructure and has poor mechanical properties. The ultimate tensile stress (UTS) of the Cu-Ag alloy processed by route BC saturates after four passes; in contrast, the UTS of the Cu-Ag alloy processed by route A increases continuously in relation to the number of ECAP passes. Based on the experimental results, the strengthening mechanisms of the Cu-8 wt pct Ag alloy processed by different routes as well as the efficiency of different routes in refining the binary alloy are discussed. [ABSTRACT FROM AUTHOR]