Automatic segmentation of metallographic image is very important for the implementation of an automatic metallographic analysis system. In this paper, a novel instance segmentation framework of a metallographic image was implemented, which can assign each pixel to a physical instance of a microstructure. In this framework, we used the Mask R-CNN as the basic network to complete the learning and recognition of the latent feature of an aluminum alloy microstructure. Meanwhile, we implemented five different loss functions based on this framework and compared the influence of these loss functions on metallographic image segmentation performance. We carried out several experiments to verify the effectiveness of the proposed framework. In these experiments, we compared and analyzed six different evaluation metrics and provided constructive suggestions for the performance evaluation of metallographic image segmentation method. A large number of experimental results have shown that the proposed method can achieve the instance segmentation of an aluminum alloy metallographic image and the segmentation results are satisfactory.

A critical component of smart grid is the integration of multiple communication technologies which can facilitate the development of smart grid. However, most of the traditional communication technologies can not satisfy the critical and complex requirements of smart grid, such as, efficiency, reliability, resilience, sustainability, and security. In this paper, the cognitive radio (CR) technology is leveraged to construct the communication infrastructure of smart grid. First, ISM bands and leased bands are introduced as backup bands to ensure the QoS of data communications in CR based networks, for which a rule that decides when to stop spectrum sensing and access the ISM bands is also provided. Furthermore, a proper communication scheme is proposed for distributed generation system based on the fact that different renewable energy source has different active period during a day. Some licensed bands whose idle time is in line with the active period of renewable energy sources are set aside exclusively for distributed generation system access. The two schemes improve both the efficiency of spectrum utilization and reliability of cognitive communications in smart grid.

In the manufacturing process of aluminum alloy, the size, distribution, and shape of microscopic grains indicate the mechanical characteristics and product quality. However, for metallographic images that can reveal microstructures, the cost of expert labeling at pixel level is high. To solve the problem, we propose a semisupervised learning strategy for grain boundary detection with a few labeled images and abundant unlabeled samples. To expand the helpful information, transfer learning and rule-based region growing are considered. Specifically, a deep network used for extracting multiscale features is designed. With constant training, through a few labeled metallographic images and abundant transferred natural images, pseudo annotations are generated gradually for unlabeled metallographic images iteratively by feature similarity and boundary region growing. The increased unlabeled samples with their pseudo annotations would be involved in the following training process in semisupervised self-training mode to improve the generalization ability of model, together with the domain adaptation block. In experiments, the proposed two methods named semiricher convolutional features-generative adversarial networks (SemiRCF-GAN) and semiricher convolutional features-maximum mean discrepancy (SemiRCF-MMD) can effectively detect grain boundaries with only one labeled metallographic image, and achieve F1 scores of 0.73 and 0.72, respectively, which surpass typical methods.

Fusing prior knowledge with data-driven deep learning for measurement is interesting and challenging. For the detection of metallographic precipitations, the measurements of size and shape of precipitations are roughly predictable in advance through a transmission electron microscope (TEM). In this article, we proposed a novel instance segmentation network named prior mask R-CNN by fusing prior knowledge for automatic precipitation detection. On the basis of the typical mask R-CNN framework, we made the following improvements. First, at the bounding box classification stage, in order to restore area information, we input the size information besides only uniform dimension features after the region of interest align (RoIAlign). Second, at the mask segmentation stage, we proposed a new loss function based on normalized graph cuts. It is category-sensitive by setting different weight strategies for different categories based on their prior shapes. In addition, from the point of view of practicality, we designed an effective measurement extraction module to get specific measurements, such as the length of precipitations, from the final prediction results of our network. In a variety of experiments, our method achieves the highest mean average precision (mAP) of 0.475 and 0.298 among different famous methods for bounding box detection and mask segmentation tasks, respectively, which proves the effectiveness of our method.

Aging behavior of wrought Al-12.7Si-0.7 Mg alloy was investigated during isothermal aging at 180 °C. Two aging peaks were observed at 3 h and 8 h, respectively. To examine precipitate evolution during aging, the alloy’s microstructure in different aging states was investigated by regular and high-resolution transmission electron microscopy (TEM and HRTEM). The results revealed that the variation of mechanical properties is attributed to the combining effect of Si particles, the grain boundary, and the character of precipitates. The predominant precipitates’ type, size, and volume fraction vary as aging time increases.