scholarly journals Advanced NDT Methods and Data Processing on Industrial CFRP Components

2019 ◽  
Vol 9 (3) ◽  
pp. 393 ◽  
Author(s):  
Vito Dattoma ◽  
Francesco Panella ◽  
Alessandra Pirinu ◽  
Andrea Saponaro
Keyword(s):  
Data Processing ◽  
Defect Detection ◽  
Temperature Response ◽  
Computation Method ◽  
Impact Simulation ◽  
Thermal Data ◽  
Surface Defect Detection ◽  
Automatic Mapping ◽  
Nondestructive Investigation ◽  
Thermal Pulses

In this work, enhanced thermal data processing is developed with experimental procedures, improving visualization algorithm for sub-surface defect detection on industrial composites. These materials are prone to successful infrared nondestructive investigation analyses, since defects are easily characterized by temperature response under thermal pulses with reliable results. Better defect characterization is achieved analyzing data with refined processing and experimental procedures, providing detailed contrasts maps where defects are better distinguished. Thermal data are analyzed for different CFRP specimens with artificial defects and experimental procedures are verified on real structural aeronautical component with internal anomalies due to impact simulation. A better computation method is found to be useful for simultaneous defect detection by means of automatic mapping of absolute contrast, optimized to identify defect boundaries.

scholarly journals A Brief Review and Advances of Thermographic Image - Processing Methods for IRT Inspection: a Case of Study on GFRP Plate

2020 ◽  
Author(s):  
F.W. Panella ◽  
A. Pirinu ◽  
V. Dattoma
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Defect Detection ◽  
Ratio Method ◽  
Computation Method ◽  
Internal Damage ◽  
Processing Methods ◽  
Mapping Procedure ◽  
Local Boundary ◽  
Thermal Data

AbstractThe present work introduces a different data processing strategy, proposed in order to improve sub-surface defect detection on industrial composites; in addition, a resume of thermal data processing with most common algorithms in literature is presented and applied with new data. A deep comparison between the common absolute contrast, DAC, PCT, TSR and derivative methods and a new proposed contrast mapping procedure is implemented. Thermographic inspection was done in reflection mode on a Glass Fiber Reinforced Plastic plate, with flat bottom hole defects. Thermal data computation method is found to be critical for simultaneous defect detection and automatic mapping, optimized to identify defect boundaries at specific depth, with help of accurate image processing, implemented in a Matlab GUI for a reliable and rapid characterization of internal damage. The new processing approach, the Local Boundary Contrast method, elaborates different contrast maps and facilitates recognition of damage extension. Tanimoto criterion and the signal-to-noise ratio method were applied as a criterion to assess defect detectability of various processing methods.

SDDNet: A Fast and Accurate Network for Surface Defect Detection

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Lisha Cui ◽  
Xiaoheng Jiang ◽  
Mingliang Xu ◽  
Wanqing Li ◽  
Pei Lv ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

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