Measurement Uncertainty for Coordinate Measuring Systems

2016 ◽  
pp. 389-404
2016 ◽  
Vol 23 (2) ◽  
pp. 281-294 ◽  
Author(s):  
Rodrigo Coral ◽  
Carlos A. Flesch ◽  
Cesar A. Penz ◽  
Mauro Roisenberg ◽  
Antonio L. S. Pacheco

Abstract When an artificial neural network is used to determine the value of a physical quantity its result is usually presented without an uncertainty. This is due to the difficulty in determining the uncertainties related to the neural model. However, the result of a measurement can be considered valid only with its respective measurement uncertainty. Therefore, this article proposes a method of obtaining reliable results by measuring systems that use artificial neural networks. For this, it considers the Monte Carlo Method (MCM) for propagation of uncertainty distributions during the training and use of the artificial neural networks.


2020 ◽  
pp. 6-10
Author(s):  
A.E. Aslanyan ◽  
E.G. Aslanyan ◽  
S.M. Gavrilkin ◽  
A.S. Doynikov ◽  
A.N. Shchipunov

The article presents the results of studies to improve the National primary standard machine for hardness of metals on the shore D scale GET 161-2001, which were performed in FSUE “VNIIFTRI” from 2016 to 2018 in accordance with the technical task of Rosstandart.The improvement was carried out in order to ensure the uniformity of hardness measurements on the Leeb scales. The created new parts of the primary standard machine, which are settings for reproducing hardness numbers on the Leeb scales, are considered. Metrological characteristics of the upgraded and adopted National primary standard machine (GET 161-2019) were investigated, the budget of measurement uncertainty was calculated for reproducing hardness numbers on the Leeb scales.


2016 ◽  
Vol 2016 (6) ◽  
pp. 69-75
Author(s):  
I.O. Bragynets ◽  
◽  
O.G. Kononenko ◽  
Yu.О. Masjurenko ◽  
◽  
...  

2008 ◽  
Vol 11 (-1) ◽  
pp. 265-276
Author(s):  
Sylwester Kłysz ◽  
Janusz Lisiecki

2008 ◽  
Vol 11 (-1) ◽  
pp. 253-264
Author(s):  
Sylwester Kłysz ◽  
Janusz Lisiecki

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