A deep transfer learning model for inclusion defect detection of aeronautics composite materials

Gong, Yanfeng; Shao, Hongliang; Luo, Jun; Zhi-xue, LI

doi:10.1016/j.compstruct.2020.112681

Cited by 87 publications

(29 citation statements)

References 33 publications

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“…Therefore, the YOLOv4 model was deployed on an Artificial Intelligence (AI) chip with a C++ code compiler, so converting the Caffe model is a necessary task. The preliminary experiment used a CNN model to enhance and improve existing automated NDT diagnostics [12]. Fortunately, aircraft maintenance technical managers inspired by AI technology have improved recent work operation manuals, which apply to aircraft production and maintenance so that AI automatically learns to describe the content of the RT file image defect area marks.…”

Section: Experiments Process Methods Setup and Resultsmentioning

confidence: 99%

YOLOv4 Object Detection Model for Nondestructive Radiographic Testing in Aviation Maintenance Tasks

Chen

Juang

2021

AIAA Journal

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Section: Experiments Process Methods Setup and Resultsmentioning

confidence: 99%

YOLOv4 Object Detection Model for Nondestructive Radiographic Testing in Aviation Maintenance Tasks

Chen

Juang

2021

AIAA Journal

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“…To overcome this issue, transfer learning-based models have been introduced recently for intelligent fault detection. Gong et al [ 19 ] proposed a novel deep transfer learning model for aeronautics composite materials (ACM) that combined deep learning with a sliding window approach. The proposed model used X-ray images of ACM but these samples were scarce.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

Khan

Khalid

Raouf

et al. 2021

Sensors

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Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

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“…Since X-rays can obtain internal information, this technology is widely applied in internal abnormality detection. To ensure the detection speed, a series of internal abnormality detection methods based on a single projection has been widely implemented in different fields, such as the security field [2][3][4][5] and the aerospace field [6][7][8]. ese methods achieve rapid detection via the direct extraction of features from projections.…”

Section: Introductionmentioning

confidence: 99%

3D M‐Net: Object‐Specific 3D Segmentation Network Based on a Single Projection

Wang

Xiao-dong

et al. 2021

Computational Intelligence and Neuroscience

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The internal assembly correctness of industrial products directly affects their performance and service life. Industrial products are usually protected by opaque housing, so most internal detection methods are based on X-rays. Since the dense structural features of industrial products, it is challenging to detect the occluded parts only from projections. Limited by the data acquisition and reconstruction speeds, CT-based detection methods do not achieve real-time detection. To solve the above problems, we design an end-to-end single-projection 3D segmentation network. For a specific product, the network adopts a single projection as input to segment product components and output 3D segmentation results. In this study, the feasibility of the network was verified against data containing several typical assembly errors. The qualitative and quantitative results reveal that the segmentation results can meet industrial assembly real-time detection requirements and exhibit high robustness to noise and component occlusion.

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A deep transfer learning model for inclusion defect detection of aeronautics composite materials

Cited by 87 publications

References 33 publications

YOLOv4 Object Detection Model for Nondestructive Radiographic Testing in Aviation Maintenance Tasks

YOLOv4 Object Detection Model for Nondestructive Radiographic Testing in Aviation Maintenance Tasks

Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

3D M‐Net: Object‐Specific 3D Segmentation Network Based on a Single Projection

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