Detection Accuracy on X-Ray Real Time Imaging for Cast Aluminum Parts

2012 ◽  
Vol 452-453 ◽  
pp. 1513-1517
Author(s):  
Ai Guo Wang ◽  
Dong Lin Yang ◽  
Peng Zhao
Keyword(s):  
Real Time ◽  
Influence Factors ◽  
Experimental Result ◽  
Detection Accuracy ◽  
Cast Aluminum ◽  
X Ray ◽  
Imaging Quality ◽  
Real Time Imaging ◽  
Detection Technology ◽  
Imaging Detection

x-ray real time imaging detection technology is a kind of important way for industrial nondestructive test. On the basis of basic theory on X-ray detection, The influence factors on x-ray real time imaging detection precision is analyzed in this article. Through analysis for the focus of X-ray source and the unintelligibility of geometric image, the relation between the optimal amplification multiple and the imaging quality is presented and the electric collimator to solve the influence on imaging quality from the scattered ray. The experimental result shows that the detection resolution ratio is up to 50PL/cm and the sensitivity is up to 1.4 % to solve the on-line real time detection for pore, inclusion and looseness and verify the application feasibility in the detection of cast aluminum parts for x-ray real time imaging detection technology.

Detection Accuracy on X-Ray Real Time Imaging for Cast Aluminum Parts

2012 ◽  
Vol 452-453 ◽  
pp. 1513-1517
Author(s):  
Ai Guo Wang ◽  
Dong Lin Yang ◽  
Peng Zhao
Keyword(s):  
Real Time ◽  
Detection Accuracy ◽  
Cast Aluminum ◽  
X Ray ◽  
Real Time Imaging

Guided Wave Imaging Based on Fully Connected Neural Network for Quantitative Corrosion Assessment

2021 ◽  
Author(s):  
Xiaocen Wang ◽  
Min Lin ◽  
Junkai Tong ◽  
Lin Liang ◽  
Jian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Guided Wave ◽  
Scientific Management ◽  
Imaging Method ◽  
Online Testing ◽  
Real Time Imaging ◽  
Detection Technology ◽  
Fully Connected ◽  
Mean Square Errors

Abstract Corrosion can affect the reliability of materials, which has attracted the attention of the industry. Corrosion detection and quantitative analysis are particularly important for scientific management and decision-making. In this paper, the imaging method based ultrasonic guided wave (UGW) detection technology and fully connected neural network (FCNN) is proposed to realize real-time imaging of corrosion damages. The imaging method contains offline training and online testing. Offline training aims to establish the relationship between detection signals and velocity maps and it is accelerated by adaptive moment estimation (Adam) algorithm. In the process of online testing, the trained model can be called directly to realize real-time imaging, that is, the detection signals are fed into the model and the network will predict the velocity maps. Finally, the velocity maps are converted to thickness maps according to the dispersion curves. Numerical experimental results show that the mean square errors (mses) are respectively 9.08 × 10−4, 2.47 × 10−3 and 2.59 × 10−3 in training, validation and testing. Compared with irregular corrosion damages, the imaging method has better imaging quality for circular corrosion damages.

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