Reference Radiographs for Examination of Aluminum Fusion Welds

2020 ◽  
Author(s):  
Keyword(s):  
Author(s):  
Anton Kidess ◽  
Mingming Tong ◽  
Gregory Duggan ◽  
David J. Browne ◽  
Saša Kenjereš ◽  
...  

2021 ◽  
Author(s):  
Erik Lindgren ◽  
Christopher Zach

Abstract Within many quality-critical industries, e.g. the aerospace industry, industrial X-ray inspection is an essential as well as a resource intense part of quality control. Within such industries the X-ray image interpretation is typically still done by humans, therefore, increasing the interpretation automatization would be of great value. We claim, that safe automatic interpretation of industrial X-ray images, requires a robust confidence estimation with respect to out-of-distribution (OOD) data. In this work we have explored if such a confidence estimation can be achieved by comparing input images with a model of the accepted images. For the image model we derived an autoencoder which we trained unsupervised on a public dataset with X-ray images of metal fusion-welds. We achieved a true positive rate at 80–90% at a 4% false positive rate, as well as correctly detected an OOD data example as an anomaly.


2017 ◽  
Vol 48 (4) ◽  
pp. 1727-1743 ◽  
Author(s):  
Daniel H. Bechetti ◽  
John N. Dupont ◽  
Masashi Watanabe ◽  
John J. de Barbadillo

1975 ◽  
Vol 20 (1) ◽  
pp. 83-108 ◽  
Author(s):  
G. J. Davies ◽  
J. G. Garland

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