Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN

Yixuan, Liu; Dongbo, Wu; Jiawei, Liang; Hui, Wang

doi:10.1155/2023/1992415

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…Meanwhile, Liu et al utilized the Faster R-CNN ResNet50 neural network model to detect a range of defects present on aero-engine blades, including cracks, wrinkles, pockmarks, scratches, polishing traces, localized chromatic aberrations, and coating detachments. They also confirmed that the model achieved remarkable recognition accuracy [7]. The R-CNN family of algorithms offers significant advantages in terms of detecting accuracy; however, its limitation lies in the relatively slower detection speed.…”

Section: Introductionmentioning

confidence: 53%

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

Yang,

Gan,

2024

Processes

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In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model based on the original YOLOv5 network architecture for enhanced defect identification. Specifically, we integrate a small target identification layer into the network to improve the recognition of minute anomalies like keyholes. Additionally, a similarity attention module (SimAM) is introduced to enhance the model’s sensitivity to channel and spatial features, facilitating the identification of dense target regions. Furthermore, the SPD-Conv module is employed to reduce information loss within the network and enhance the model’s identification rate. During the testing phase, a set of sample photos is randomly selected to evaluate the efficacy of the proposed model, utilizing training and test sets derived from a pre-existing defect database. The model’s performance in multi-category recognition is measured using the average accuracy metric. Test results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 89.8%, surpassing the mAP of the original YOLOv5 network by 1.7% and outperforming other identification networks in terms of accuracy. Notably, the improved YOLOv5 model exhibits superior capability in identifying small-sized defects.

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Section: Introductionmentioning

confidence: 53%

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

Yang,

Gan,

2024

Processes

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“…Several studies have delved into the application of deep learning for failure detection across various manufacturing settings, particularly focusing on the outer layers of additive manufacturing (AM) parts, including turbine and compressor blades for jet engines [10][11][12]. However, the majority of these investigations have predominantly utilized traditional convolutional neural networks (CNNs), which are not optimal for spotting subtle and asymmetrical flaws.…”

Section: Introductionmentioning

confidence: 99%

Automatic Defect Detection of Jet Engine Turbine and Compressor Blade Surface Coatings Using a Deep Learning-Based Algorithm

Zubayer,

Zhang,

Liu

et al. 2024

Coatings

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The application of additive manufacturing (AM) in the aerospace industry has led to the production of very complex parts like jet engine components, including turbine and compressor blades, that are difficult to manufacture using any other conventional manufacturing process but can be manufactured using the AM process. However, defects like nicks, surface irregularities, and edge imperfections can arise during the production process, potentivally affecting the operational integrity and safety of jet engines. Aiming at the problems of poor accuracy and below-standard efficiency in existing methodologies, this study introduces a deep learning approach using the You Only Look Once version 8 (YOLOv8) algorithm to detect surface, nick, and edge defects on jet engine turbine and compressor blades. The proposed method achieves high accuracy and speed, making it a practical solution for detecting surface defects in AM turbine and compressor blade specimens, particularly in the context of quality control and surface treatment processes in AM. The experimental findings confirmed that, in comparison to earlier automatic defect recognition procedures, the YOLOv8 model effectively detected nicks, edge defects, and surface defects in the turbine and compressor blade dataset, attaining an elevated level of accuracy in defect detection, reaching up to 99.5% in just 280 s.

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High surface integrity machining of typical aviation difficult-to-machine material blade

Wu,

Liu,

Wang

2023

Int J Adv Manuf Technol

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Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN

Cited by 7 publications

References 21 publications

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

Automatic Defect Detection of Jet Engine Turbine and Compressor Blade Surface Coatings Using a Deep Learning-Based Algorithm

High surface integrity machining of typical aviation difficult-to-machine material blade

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