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The purpose of this paper is to identify smaller wear particles and improve the calculation speed, identify more abrasive particles and promote industrial applications.

This paper studies a new intelligent recognition method for equipment wear debris based on the YOLO V5S model released in June 2020. Nearly 800 ferrography pictures, 23 types of wear debris, about 5,000 wear debris were used to train and test the model. The new lightweight approach of wear debris recognition can be implemented in rapidly and automatically and also provide for the recognition of wear debris in the field of online wear monitoring.

An intelligent recognition method of wear debris in ferrography image based on the YOLO V5S model was designed. After the training, the GIoU values of the model converged steadily at about 0.02. The overall precision rate and recall rate reached 0.4 and 0.5, respectively. The overall MAP value of each type of wear debris was 40.5, which was close to the official recognition level of YOLO V5S in the MS COCO competition. The practicality of the model was approved. The intelligent recognition method of wear debris based on the YOLO V5S model can effectively reduce the sensitivity of wear debris size. It also has a good recognition effect on wear debris in different sizes and different scales. Compared with YOLOV. YOLOV, Mask R-CNN and other algorithms%2C, the intelligent recognition method based on the YOLO V5S model, have shown their own advantages in terms of the recognition effect of wear debris%2C the operation speed and the size of weight files. It also provides a new function for implementing accurate recognition of wear debris images collected by online and independent ferrography analysis devices.

To the best of the authors’ knowledge, the intelligent identification of wear debris based on the YOLO V5S network is proposed for the first time, and a large number of wear debris images are verified and applied.

The purpose of this paper is to investigate the effect of doping element on the microwave absorption performance of hexagonal nano boron nitride (h-nBN)-reinforced basalt fabric (BF)/epoxy composites. A new type of hybrid composite that will be produced by the use of boron nitride as an additive that leads to increased radar absorption capability will be developed and a new material that can be used in aeronautical radar applications.

This study is focused on the microwave absorption properties of h-nBN doped basalt fabric-reinforced epoxy composites. Basalt fabric (BF)/epoxy composites (pure composites) and the BF/h-nBN (1 Wt.% h-nBN doped composites) hybrid composites were fabricated by vacuum infusion method. Phase identification of the composites were performed using X-ray diffraction (XRD), the 2θ scan range was from 10 to 60 with the scanning speed of 3°/min and surface morphologies of the composites were investigated using scanning electron microscopy (SEM). Microwave properties of samples were investigated through transmission/reflection measurements in Agilent brand 2-Port PNA-L Network Analyzer in the frequency range of 3–18 GHz. The prepared sample is positioned between two horn antennas with and without metal plate.

Experimental results show that h-nBN doped composite was synthesized successfully and the produced hexagonal nano boron nitride-added fiber laminated composite material has good absorption behavior when they are used with metallic sheets and good for isolation applications at many points in the 3–18 GHz band.

This paper will contribute to the literature on the use of basalt fabric, which are new types of fibers, and hexagonal nano boron nitride and the effects of boron nitride on radar absorption properties of composite material. It presents detail characterization of each composite by using XRD and scanning electron microscopy.