scholarly journals Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sergey Shevchik ◽  
Tri Le-Quang ◽  
Bastian Meylan ◽  
Farzad Vakili Farahani ◽  
Margie P. Olbinado ◽  
...  
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sergey Shevchik ◽  
Tri Le-Quang ◽  
Bastian Meylan ◽  
Farzad Vakili Farahani ◽  
Margie P. Olbinado ◽  
...  

2017 ◽  
Vol 11 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Toshiyuki Terunuma ◽  
Aoi Tokui ◽  
Takeji Sakae
Keyword(s):  

2017 ◽  
Vol 31 (14) ◽  
pp. 1750154 ◽  
Author(s):  
Xiaohong Zhan ◽  
Xing Bu ◽  
Tao Qin ◽  
Haisong Yu ◽  
Jie Chen ◽  
...  

In order to detect weld defects in laser welding T-joint of Al–Li alloy, a real-time X-ray image system is set up for quality inspection. Experiments on real-time radiography procedure of the weldment are conducted by using this system. Twin fillet welding seam radiographic arrangement is designed according to the structural characteristics of the weldment. The critical parameters including magnification times, focal length, tube current and tube voltage are studied to acquire high quality weld images. Through the theoretical and data analysis, optimum parameters are settled and expected digital images are captured, which is conductive to automatic defect detection.


2021 ◽  
Vol 23 (4) ◽  
pp. 57-62 ◽  
Author(s):  
Amjad Rehman ◽  
Tariq Sadad ◽  
Tanzila Saba ◽  
Ayyaz Hussain ◽  
Usman Tariq

2019 ◽  
Vol 31 (4) ◽  
pp. 799-814 ◽  
Author(s):  
Yanxi Zhang ◽  
Deyong You ◽  
Xiangdong Gao ◽  
Congyi Wang ◽  
Yangjin Li ◽  
...  

2021 ◽  
Vol 35 (5) ◽  
pp. 431-435
Author(s):  
Vijayakumar Ponnusamy ◽  
Diwakar R. Marur ◽  
Deepa Dhanaskodi ◽  
Thangavel Palaniappan

This work proposes deep learning neural network-based X-ray image classification. The X-ray baggage scanning machinery plays an essential role in the safeguard of customs, airports, and other systematically very important landmarks and infrastructures. The technology at present of baggage scanning machines is designed on X-ray attenuation. The detection of threatful objects is built on how different objects attenuate the X-ray beams going through them. In this paper, the deep convolutional neural network of YOLO is utilized in classifying baggage images. Real-time performance of the baggage image classification is an essential one for security scanning. There are many computationally intensive operations in the You Only Look Once (YOLO) architecture. The computational intensive operations are implemented in the Field Programmable Gate Array (FPGA) platform to optimize process delays. The critical issues involved in those implementations include data representation, inner products computation and implementation of activation function and resolving these issues will also be a significant task. The FPGA implementation results show that with less resource occupancy, the YOLO implementation provides maximum accuracy of 98.9% in classifying X-ray baggage images and identifying hazardous materials. This result proves that the proposed implementation is best suited for practical system deployments for real-time Baggage scanning.


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