Non-Destructive Determination of the Nuclear Material Content of Spent Fuel Pieces in Canisters

2013 ◽  
Vol 60 (2) ◽  
pp. 1080-1085 ◽  
Author(s):  
Jozsef Zsigrai ◽  
Cong Tam Nguyen ◽  
Istvan Almasi ◽  
Laszlo Lakosi
Keyword(s):  
Nuclear Material ◽  
Spent Fuel ◽  
Material Content ◽  
Non Destructive

Determination of the Plutonium Mass and Curium Ratio of Spent Fuel Assemblies for Input Nuclear Material Accountancy of Pyroprocessing, and Analysis of Their Errors

2012 ◽  
Vol 179 (2) ◽  
pp. 196-204 ◽  
Author(s):  
Tae-Hoon Lee ◽  
Young-Soo Kim ◽  
Tae-Je Kwon ◽  
Hee-Sung Shin ◽  
Ho-Dong Kim
Keyword(s):  
Nuclear Material ◽  
Spent Fuel ◽  
Fuel Assemblies

Stoichiometric Determination of Graphite Intercalation Compounds Using Rutherford Backscatiering Spectrometry

1983 ◽  
Vol 27 ◽  
Author(s):  
L. Salamanca-Riba ◽  
B.S. Elman ◽  
M.S. Dresselhaus ◽  
T. Venkatesan
Keyword(s):  
Experimental Error ◽  
Rutherford Backscattering Spectrometry ◽  
Rutherford Backscattering ◽  
Intercalation Compounds ◽  
Graphite Intercalation Compounds ◽  
X Ray ◽  
Donor And Acceptor ◽  
Graphite Intercalation ◽  
Non Destructive

ABSTRACTRutherford backscattering spectrometry (RBS) is used to characterize the stoichiometry of graphite intercalation compounds (GIC). Specific application is made to several stages of different donor and acceptor compounds and to commensurate and incommensurate intercalants. A deviation from the theoretical stoichiometry is measured for most of the compounds using this non-destructive method. Within experimental error, the RBS results agree with those obtained from analysis of the (00ℓ) x-ray diffractograms and weight uptake measurements on the same samples.

Application of EDS Technique for OSP Film Thickness Measurement

2006 ◽  
Author(s):  
Prong Kongsubto ◽  
Sirarat Kongwudthiti
Keyword(s):  
Thin Film ◽  
Quantitative Analysis ◽  
Joint Strength ◽  
Critical Factor ◽  
Thickness Measurement ◽  
Ic Manufacturing ◽  
And Control ◽  
Film Thickness Measurement ◽  
Non Destructive

Abstract Organic solderability preservatives (OSPs) pad is one of the pad finishing technologies where Cu pad is coated with a thin film of an organic material to protect Cu from oxidation during storage and many processes in IC manufacturing. Thickness of OSP film is a critical factor that we have to consider and control in order to achieve desirable joint strength. Until now, no non-destructive technique has been proposed to measure OSP thickness on substrate. This paper reports about the development of EDS technique for estimating OSP thickness, starting with determination of the EDS parameter followed by establishing the correlation between C/Cu ratio and OSP thickness and, finally, evaluating the accuracy of the EDS technique for OSP thickness measurement. EDS quantitative analysis was proved that it can be utilized for OSP thickness estimation.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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