scholarly journals Simultaneous tailoring of pulsed thermography experimental and processing parameters for enhanced defect detection and sizing in adhesive bonds in carbon fibre composites

2022 ◽  
Author(s):  
Rachael C Tighe ◽  
Jonathon Hill ◽  
Tom Vosper ◽  
Cody Taylor ◽  
Tairongo Tuhiwai
Keyword(s):  
Edge Detection ◽  
Defect Detection ◽  
Feature Detection ◽  
Signal Reconstruction ◽  
Detection Threshold ◽  
Bond Line ◽  
Minimum Diameter ◽  
Adhesive Bonds ◽  
Detection Depth ◽  
Carbon Fibre Composites

Abstract Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8 % on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1- 2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.

scholarly journals An improved edge detection approach and its application in defect detection

2017 ◽  
Vol 244 ◽  
pp. 012017 ◽  
Author(s):  
Vilas H Gaidhane ◽  
Navdeep ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Edge Detection ◽  
Defect Detection ◽  
Detection Approach

scholarly journals Characteristics of CALIOP attenuated backscatter noise: implication for cloud/aerosol detection

2010 ◽  
Vol 10 (7) ◽  
pp. 17263-17305 ◽  
Author(s):  
D. L. Wu ◽  
J. H. Chae ◽  
A. Lambert ◽  
F. F. Zhang
Keyword(s):  
Feature Detection ◽  
Detection Threshold ◽  
Horizontal Resolution ◽  
Occurrence Frequency ◽  
Orthogonal Polarization ◽  
Color Ratio ◽  
Gridded Dataset ◽  
L1 Data ◽  
Measurement Noises ◽  
532 Nm

Abstract. To study cloud/aerosol features in the upper troposphere and lower stratosphere (UT/LS) with the NASA's A-Train sensors, a research algorithm is developed for a re-gridded CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Level 1 (L1) backscatter dataset. This paper provides a detailed analysis of the measurement noise of this re-gridded dataset in order to compare the lidar measurements with other collocated measurements (e.g., CloudSat, Microwave Limb Sounder). The re-gridded dataset has a manageable data volume for multi-year analysis. It has a fixed (5 km) horizontal resolution, and the measurement error is derived empirically from the background-corrected backscatter profile on a profile-by-profile basis. The 532-nm and 1064-nm measurement noises, determined from the data at altitudes above 19 km, are analyzed and characterized in terms of the mean (μ), standard deviation (σ), and normalized probability density function (PDF). These noises show a larger variance over landmasses and bright surfaces during day, and in regions with enhanced flux of energetic particles during night, where the instrument's ability for feature detection is slightly degraded. An increasing trend in the nighttime 1064-nm σ appears to be significant, which likely causes the increasing differences in cloud occurrence frequency between the 532-nm and 1064-nm channels. Most of the CALIOP backscatter noise distributions exhibit a Gaussian-like behavior but the nighttime 532-nm perpendicular measurements show multi-Gaussian characteristics. We apply σ – based thresholds to detect cloud/aerosol features in the UT/LS from the subset L1 data. The observed morphology is similar to that from the Level 2 (L2) 05km_CLAY+05km_ALAY product, but the occurrence frequency obtained in this study is slightly lower than the L2 product due to differences in spatial averaging and detection threshold. In the case where the measurement noises of two data sets are different, the normalized PDF has proven useful for quantifying the day-night difference of the CALIOP backscatters, showing higher daytime cloud occurrence frequency in the tropical UT/LS. Other cloud/aerosol properties, such as depolarization ratio and color ratio, can be also evaluated with the PDF method.

scholarly journals Measuring and Modeling the Feature Detection Threshold Functions of Colormaps

2019 ◽  
Vol 25 (9) ◽  
pp. 2777-2790 ◽  
Author(s):  
Colin Ware ◽  
Terece L. Turton ◽  
Roxana Bujack ◽  
Francesca Samsel ◽  
Piyush Shrivastava ◽  
...  
Keyword(s):  
Feature Detection ◽  
Detection Threshold ◽  
Threshold Functions

Material characterisation by enhanced resolution in non-stationary thermal wave imaging

2021 ◽  
Vol 63 (12) ◽  
pp. 721-726
Author(s):  
G T Vesala ◽  
V S Ghali ◽  
S Subhani ◽  
Y Naga Prasanthi
Keyword(s):  
Defect Detection ◽  
Thermal Wave ◽  
Signal To Noise Ratio ◽  
Empirical Relationship ◽  
Depth Resolution ◽  
Steel Sample ◽  
Detection Depth ◽  
Wave Imaging ◽  
Domain Phase ◽  
Thermal Wave Imaging

In the recent past, quadratic frequency-modulated thermal wave imaging (QFMTWI) has been advanced with a chirp z-transform (CZT)-based processing approach to facilitate enhanced subsurface anomaly detection, depth quantification and material property estimation with enhanced depth resolution. In the present study, the applicability of CZT-based phase analysis for foreign object defect detection in a structural steel sample using QFMTWI is validated through finite element-based numerical modelling rather than experimental verification due to limited available resources. Furthermore, the enhanced defect detection capability of the CZT phase approach is qualitatively compared with the frequency- and time-domain phase approaches using the defect signal-to-noise ratio (SNR) as a quality metric. Also, an empirical relationship between the observed phases and the thermal reflection coefficient is obtained, which recommends the CZT phase as a prominent approach for foreign material defect detection.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

scholarly journals SIMULATION OF THE DAMAGE PROCESS OF THERMAL GLUE BOND

2021 ◽  
Author(s):  
Oleksandr L. Sokolskyi ◽  
Yuliya Yu. Herasimenko
Keyword(s):  
Tensile Load ◽  
Nonlinear Behavior ◽  
Bond Line ◽  
Ease Of Use ◽  
Test Method ◽  
Correct Selection ◽  
Computer Design ◽  
Adhesive Bonds ◽  
Damage Process ◽  
Bonding Technology

Background. Thermal glue bonding technology is widely used to create paper and paperboard packaging and polymer packaging in the chemical, food, and textile industries. To meet the needs of the consumer, it is necessary to provide a sufficient level of packaging strength and ease of use. When creating strong quality packaging, special attention should be paid to the correct selection of materials and equipment. To create a strong and reliable bond, it is necessary to understand the behavior of adhesive bonds, which depends on the following factors: type of adhesive, curing time, type of bond, the thickness of the bond line, etc. Therefore, for more efficient use of adhesive materials, it is necessary to develop reliable methods for designing and predicting the behavior of adhesive bonds. Objective. Development of an objective test method and a method for predicting the behavior of a thermal glue bond during debonding to analyze its strength. Methods. The article considers an integral method for determining fracture toughness, which can be used regardless of the form of the bond, as well as the linear or nonlinear behavior of the thermal glue. To predict the behavior of an thermal glue bond under load, a computer design model was developed and a numerical calculation of deformations and stresses arising in the thermal glue bond during a tensile load was carried out. Results. The dependences of the stress in the middle of the bond on the displacement and the force at the edges of the sample on the magnitude of the tensile stroke were obtained, which makes it possible to evaluate the quality of the bond and the effect of the stiffness of the thermoplastic adhesive on the damage of the bond. Conclusions. An analysis of the data obtained showed that the forces at the edges of the sample are directly proportional to the magnitude of the stretching stroke, and also that the destruction of the thermal glue bond occurs after reaching the ultimate strength of the adhesive, the magnitude of the stresses first increases to the limit, and then remains constant until complete damage.

A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix

2019 ◽  
Vol 90 (7-8) ◽  
pp. 776-796 ◽  
Author(s):  
Feng Li ◽  
Lina Yuan ◽  
Kun Zhang ◽  
Wenqing Li
Keyword(s):  
Defect Detection ◽  
Detection Threshold ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Detection Scheme ◽  
Second Stage ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Different Characteristics

A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

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