Investigation of the ability of low-frequency acoustic energy for polishing of the CK60 steel using a hybrid FE/BE/DEM approach

Beigmoradi, Sajjad

doi:10.1007/s40571-022-00472-y

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The image detection method can be divided into the iron spectrum analysis method and microscopic imaging method by different methods of obtaining abrasive images. After acquiring the image, a series of transformations and segmentation operations are performed on the abrasive particle image to capture the features of the abrasive particle [8,9].…”

Section: Introductionmentioning

confidence: 99%

Research on Abrasive Particle Target Detection and Feature Extraction for Marine Lubricating Oil

Bai,

Ding,

Zhang

et al. 2024

JMSE

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The hydraulic oil of marine equipment contains a large number of abrasive contaminants that reflect the operating condition of the equipment. In order to realize the detection of particulate contaminants, this research first proposes a shape-based classification method for oil abrasive particles, designs an oil abrasive particle collection system, and constructs a new dataset. After that, the research introduces deep learning target detection technology in computer vision, and uses GhostNet to lighten the network structure, the CBAM (Convolutional Block Attention Module) attention mechanism to improve the generalization ability of the model, and the ASPP module to enhance the model sensory wildness, respectively. A lightweight target detection model, WDD, is created for the identification of abrasive particles. In this study, the WDD model is tested against other network models, and the mAP value of WDD reaches 91.2%, which is 4.8% higher than that of YOLOv5s; in addition, the detection speed of the WDD model reaches 55 FPS. Finally, this study uses real ship lubricating oils for validation, and the WDD model still maintains a high level of accuracy. Therefore, the WDD model effectively balances the accuracy and detection speed of marine oil abrasive particle detection, which is superior to other oil abrasive particle detection techniques.

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Section: Introductionmentioning

confidence: 99%