In a world where the gap between the macro and the micro scales is on the increase, measurement and measurement procedures assume significant importance. In an effort to produce high quality products, industries employ state-of-the-art manufacturing techniques. The best manufacturing techniques would be meaningless without being complimented by the best measurement system and procedures in order to get accurate measurement results. The present industrial scenario is such that the measurement process has become increasingly tedious and also subject to a lot of constraints.
Traceability (VIM 6.10), a requirement for quality standard certification, is the property of the result of a measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainties. In order to establish traceability, measurement results must be accompanied by a statement of uncertainty. The theory and computation of measurement uncertainty requires a certain degree of knowledge and expertise and so in most cases, the industry follows a much simpler procedure called gage repeatability and reproducibility (Gage R&R) to represent the total variation in the measurement system. The need for simple tools for estimation of Gage R&R and measurement uncertainty has provided the motivation for us to build an internet based software system for the estimation of Gage R&R and measurement uncertainty.

The evaluation of measurement uncertainty, from the final user’s standpoint, involves several issues which are only partially addressed by the GUM. An evaluation procedure assisted by a computer program can be of great support and provide the user with detailed results useful for further analysis. In this paper a code is presented for the assisted evaluation of measurement uncertainty, based upon the convolution of probability distributions. So the method allows expression of the final result of measurement as a probability distribution, on which it is possible to evaluate useful parameters such as expanded uncertainty, at whatever confidence level. It also permits to evaluate, in probabilistic terms, the risk associated to matching tolerances. Moreover it is possible to manage the uncertainty on dispersion parameters (sometimes called ‘uncertainty of uncertainty’) considering a modified probability distribution. In the paper the code is presented together with some case studies showing the support available to the operator, the GUM compatibility and the application importance of the final probability distribution.

Standard bullets and casings are currently under development to support the National Integrated Ballistics Information Network (NIBIN) in the U.S. Based on a numerically controlled diamond turning technique, 20 RM 8240 standard bullets were fabricated in 2002. Test results show high repeatability and reproducibility for the bullet signatures on these RM bullets. Prototype standard casings were also manufactured using an electro-forming technique, and are currently under test. These RM bullets and casings are intended for measurement traceability and quality control for ballistics laboratories nationwide.

Methods enabling a determination of errors eI and dI for a high transformation ratio difference between a standard and tested instrument current transformers (ICT) are described in this article. A widespread method uses an automatic transformer test set for the measurement of a difference between a standard and tested ICT. A method transforming secondary currents to voltages and an indirect method using error measurement from magnetizing current are also described.

A measurement process represents a controlled learning process in which various aspects on uncertainty analysis are investigated.
A measurement process is performed if information supplied by it is likely to be considerably more accurate, stable and reliable than the pool of information already available.
The substantial amount of information, got with respect to the conditions prior to the result after the measurement process is performed, can be connected to the "Kullback's principle of minimum cross-entropy".
This, as it is known, is a correct method of inductive inference when no sufficient knowledge about the statistical distributions of the involved random variables is available before the measurement process is carried out except for the permitted ranges, the essential model relationships and some constraints, gained in past experience, valuable usually in terms of expectations of given functions or bounds on them.
In this paper the authors pointed out the connection between the evaluation of the uncertainty in a repeated measurements process and the "Kullback's principle of minimum cross-entropy".