Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications.

The fast and accurate detection of steel surface defects has become an important goal of research in various fields. As one of the most important and effective methods of detecting steel surface defects, the successive generations of YOLO algorithms have been widely used in these areas; however, for the detection of tiny targets, it still encounters difficulties. To solve this problem, the first modified PP-YOLOE algorithm for small targets is proposed. By introducing Coordinate Attention into the Backbone structure, we encode channel relationships and long-range dependencies using accurate positional information. This improves the performance and overall accuracy of small target detection while maintaining the model parameters. Additionally, simplifying the traditional PAN+FPN components into an optimized FPN feature pyramid structure allows the model to skip computationally expensive but less relevant processes for the steel surface defect dataset, effectively reducing the computational complexity of the model. The experimental results show that the overall average accuracy (mAP) of the improved PP-YOLOE algorithm is increased by 4.1%, the detection speed is increased by 2.06 FPS, and the accuracy of smaller targets (with a pixel area less than 322) that are more difficult to detect is significantly improved by 13.3% on average, as compared to the original algorithm. The detection performance is also higher than that of the mainstream target detection algorithms, such as SSD, YOLOv3, YOLOv4, and YOLOv5, and has a high application value in industrial detection.

Mo–Si–B alloys are a crucial focus for the development of the next generation of ultra-high-temperature structural materials. They have garnered significant attention over the past few decades due to their high melting point and superior strength and oxidation resistance compared to other refractory metal alloys. However, their low fracture toughness at room temperature and poor oxidation resistance at medium temperature are significant barriers limiting the processing and application of Mo–Si–B alloys. Therefore, this review was carried out to compare the effectiveness of doped metallic elements and second-phase particles in solving these problems in detail, in order to provide clear approaches to future research work on Mo–Si–B alloys. It was found that metal doping can enhance the properties of the alloys in several ways. However, their impact on oxidation resistance and fracture toughness at room temperature is limited. Apart from B-rich particles, which significantly improve the high-temperature oxidation resistance of the alloy, the doping of second-phase particles primarily enhances the mechanical properties of the alloys. Additionally, the application of additive manufacturing to Mo–Si–B alloys was discussed, with the observation of high crack density in the alloys prepared using this method. As a result, we suggest a future research direction and the preparation process of oscillatory sintering, which is expected to reduce the porosity of Mo–Si–B alloys, thereby addressing the noted issues.

The aircraft engine is a core component of an airplane, and its critical components work in harsh environments, making it susceptible to a variety of surface defects. To achieve efficient and accurate defect detection, this paper establishes a dataset of surface defects on aircraft engine components and proposes an optimized object detection algorithm based on YOLOv5 according to the features of these defects. By adding a dual-path routing attention mechanism in the Biformer model, the detection accuracy is improved; by replacing the C3 module with C3-Faster based on the FasterNet network, robustness is enhanced, accuracy is maintained, and lightweight modeling is achieved. The NWD detection metric is introduced, and the normalized Gaussian Wasserstein distance is used to enhance the detection accuracy of small targets. The lightweight upsampling operator CARAFE is added to expand the model’s receptive field, reorganize local information features, and enhance content awareness performance. The experimental results show that, compared with the original YOLOv5 model, the improved YOLOv5 model’s overall average precision on the aircraft engine component surface defect dataset is improved by 10.6%, the parameter quantity is reduced by 11.7%, and the weight volume is reduced by 11.3%. The detection performance is higher than mainstream object detection algorithms such as SSD, RetinaNet, FCOS, YOLOv3, YOLOv4, and YOLOv7. Moreover, the detection performance on the public dataset (NEU-DET) has also been improved, providing a new method for the rapid defect detection of aircraft engines and having high application value in various practical detection scenarios.

Diffusion aluminum coating is crucial to protect aero-engine turbine blades from high-temperature oxidation. Slurry aluminizing, as a commonly-used coating preparation technology, has variations in the process parameters that directly affect the quality of the coating. Therefore, this paper investigates the effect of slurry thickness on coating quality. Different forms of aluminized coatings were obtained by coating nine DZ22B nickel-based superalloy plates of the same size with different slurry thicknesses while keeping other parameters constant. These aluminized coatings were characterized using a scanning electron microscope (SEM) with an energy dispersive spectrometer (EDS), an X-ray diffractometer (XRD), and a surface gauge. The results show that the AlNi phase dominates the matrix of the aluminized coating, and the outer layer of the coating has white dotted precipitates of Cr. As the slurry thickness increases, the coating thickness increases, and the proportion of the outer layer in the overall coating increases. In contrast, the thickness of the interdiffusion layer does not change significantly. The thicker the slurry, the higher the Al content of the coating surface. A medium-thickness slurry can form a smooth aluminizing coating with a roughness Ra < 4.5 μm surface. The combined results show that a medium-thick slurry can produce a high-quality coating.

The integration of artificial intelligence with steel manufacturing operations holds great potential for enhancing factory efficiency. Object detection algorithms, as a category within the field of artificial intelligence, have been widely adopted for steel defect detection purposes. However, mainstream object detection algorithms often exhibit a low detection accuracy and high false-negative rates when it comes to detecting small and subtle defects in steel materials. In order to enhance the production efficiency of steel factories, one approach could be the development of a novel object detection algorithm to improve the accuracy and speed of defect detection in these facilities. This paper proposes an improved algorithm based on the YOLOv5s-7.0 version, called YOLOv5s-7.0-FCC. YOLOv5s-7.0-FCC integrates the basic operator C3-Faster (C3F) into the C3 module. Its special T-shaped structure reduces the redundant calculation of channel features, increases the attention weight on the central content, and improves the algorithm’s computational speed and feature extraction capability. Furthermore, the spatial pyramid pooling-fast (SPPF) structure is replaced by the Content Augmentation Module (CAM), which enriches the image feature content with different convolution rates to simulate the way humans observe things, resulting in enhanced feature information transfer during the process. Lastly, the upsampling operator Content-Aware ReAssembly of Features (CARAFE) replaces the “nearest” method, transforming the receptive field size based on the difference in feature information. The three modules that act on feature information are distributed reasonably in YOLOv5s-7.0, reducing the loss of feature information during the convolution process. The results show that compared to the original YOLOv5 model, YOLOv5s-7.0-FCC increases the mean average precision (mAP) from 73.1% to 79.5%, achieving a 6.4% improvement. The detection speed also increased from 101.1 f/s to 109.4 f/s, an improvement of 8.3 f/s, further meeting the accuracy requirements for steel defect detection.