scholarly journals The Use of Process Monitoring Data for Nuclear Material Accounting Summary Report

1980 ◽  
Author(s):  
R. J. Brouns ◽  
B. W. Smith ◽  
D. W. Brite ◽  
C. O. Harvey
Keyword(s):  
Process Monitoring ◽  
Nuclear Material ◽  
Monitoring Data ◽  
Summary Report ◽  
Material Accounting

scholarly journals Revisiting Statistical Aspects of Nuclear Material Accounting

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
T. Burr ◽  
M. S. Hamada
Keyword(s):  
Process Monitoring ◽  
Measurement Errors ◽  
Nuclear Material ◽  
Data Driven ◽  
Testing Period ◽  
The Difference ◽  
One Year ◽  
The Impact ◽  
Material Accounting

Nuclear material accounting (NMA) is the only safeguards system whose benefits are routinely quantified. Process monitoring (PM) is another safeguards system that is increasingly used, and one challenge is how to quantify its benefit. This paper considers PM in the role of enabling frequent NMA, which is referred to as near-real-time accounting (NRTA). We quantify NRTA benefits using period-driven and data-driven testing. Period-driven testing makes a decision to alarm or not at fixed periods. Data-driven testing decides as the data arrives whether to alarm or continue testing. The difference between period-driven and datad-riven viewpoints is illustrated by using one-year and two-year periods. For both one-year and two-year periods, period-driven NMA using once-per-year cumulative material unaccounted for (CUMUF) testing is compared to more frequent Shewhart and joint sequential cusum testing using either MUF or standardized, independently transformed MUF (SITMUF) data. We show that the data-driven viewpoint is appropriate for NRTA and that it can be used to compare safeguards effectiveness. In addition to providing period-driven and data-driven viewpoints, new features include assessing the impact of uncertainty in the estimated covariance matrix of the MUF sequence and the impact of both random and systematic measurement errors.

scholarly journals Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials

2021 ◽  
Vol 64 ◽  
pp. 1248-1254
Author(s):  
Darragh S. Egan ◽  
Caitríona M. Ryan ◽  
Andrew C. Parnell ◽  
Denis P. Dowling
Keyword(s):  
Process Monitoring ◽  
Monitoring Data ◽  
Porous Biomaterials
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