Data Fusion of Non Destructive Testing for Detection of Defects in Welding

2010 ◽  
Author(s):  
R. Farzaneh ◽  
M. S. Safizadeh ◽  
M. Goodarzi ◽  
M. Seyrafi
Keyword(s):  
Image Fusion ◽  
Signal Amplification ◽  
Travel Speed ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Friction Stir ◽  
Length Width ◽  
Pixel Image ◽  
Non Destructive

In this paper the specimens of Aluminum 2024 with 5 millimeter in thickness are joined together by friction stir welding with travel speed of 100 mm/min and tool rotational speeds of 450, 900 and 1800 rpm and a tool were made of hot working steel, H13, firstly. Thus three kinds of welds are produced. Radiography and ultrasonic (UT) non-destructive testing (NDT) procedure were applied to characterize the presence and geometry of possible weld defects prior to mechanical destructive testing. A Echograph Model 1090 digital UT instrumentation and a 4 MHz angle beam probe (refraction angle α = 70°) was used for C-scan of UT contact testing of welded samples (transverse UT velocity 2850 m/s and signal amplification 40 dB). The detection accuracy of defects can be improved by image fusion of ultrasonic and radiography data. For this reason, the data of the two sensors are transformed into a same scale images (length, width and also depth). Pixel by pixel image fusion is used for fusion and analysis. Comparing these results with the destructed part shows that the fusion of two tests improves the results.

scholarly journals Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 13 ◽  
Author(s):  
Yousefi ◽  
Ibarra-Castanedo ◽  
Maldague
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix ◽  
Principal Component ◽  
Computational Time ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive ◽  
Non Negative Matrix Factorization ◽  
Subsurface Defects

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

Data fusion in non destructive testing using fuzzy logic to evaluate friction stir welding

2008 ◽  
Vol 22 (12) ◽  
pp. 826-833 ◽  
Author(s):  
T.G. dos Santos ◽  
B.S. Silva ◽  
P. dos Santos Vilaça ◽  
L. Quintino ◽  
J. M.C. Sousa
Keyword(s):  
Fuzzy Logic ◽  
Friction Stir Welding ◽  
Data Fusion ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Friction Stir ◽  
Non Destructive

Advanced technique for non-destructive testing of friction stir welding of metals

Measurement ◽  
2010 ◽  
Vol 43 (8) ◽  
pp. 1021-1030 ◽  
Author(s):  
Luis S. Rosado ◽  
Telmo G. Santos ◽  
Moisés Piedade ◽  
Pedro M. Ramos ◽  
Pedro Vilaça
Keyword(s):  
Friction Stir Welding ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Advanced Technique ◽  
Friction Stir ◽  
Non Destructive

scholarly journals A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 212
Author(s):  
Mingyu Gao ◽  
Dawei Qi ◽  
Hongbo Mu ◽  
Jianfeng Chen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Feature Extractor ◽  
Testing Technology ◽  
Excessive Consumption ◽  
Non Destructive ◽  
Deep Learning Model

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.

Detection Resolution Analysis of Scanning Acoustic Microscopy Used in Electronic Packaging

2014 ◽  
Vol 536-537 ◽  
pp. 272-275
Author(s):  
Xiang Hui Guo ◽  
Chun Guang Xu ◽  
Liu Yang ◽  
Kai Peng
Keyword(s):  
Electronic Packaging ◽  
Ultrasonic Transducer ◽  
Experimental Results ◽  
Acoustic Microscopy ◽  
Center Frequency ◽  
Detection Accuracy ◽  
Scanning Acoustic Microscopy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

Scanning Acoustic Microscopy (SAM) has been a powerful non-destructive testing tool used in electronic packaging and material characterization. With the development of 3D electronic packaging, internal dimensions of electronic packaging are getting more and more smaller, and the detection accuracy of existing non-destructive testing technology is far behind the requirements of manufacturing technology. In this study, a set of practical SAM system was developed independently by our Lab. And its detection resolution was analyzed using high frequency focused transducers with center frequency ranging from 20 MHz to 100MHz. The experimental results show that the lateral resolution of the ultrasonic transducer with 100MHz central frequency can reach about 40 microns, which is consistent with calculated resolution. Comparing with Sparrow criteria, Rayleigh criteria is more coherent with the experimental results.

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