In accordance with the recent version of ISO 376:2011, the classification of the force transducers is based on the relative errors calculated from the calibration results. This classification approach doesn't take the uncertainty of measurement into consideration. It becomes one of the most important factors that must be utilized when making a classification decision based on of ISO/IEC 17025:2017. In this study a proposed approach for force proving instrument classification was developed. This approach is based on taking into account the calibration results uncertainty of the instruments as a decision rule for classifications. Since the expanded budget uncertainty is a combination of different parameters that may affect the classifications decisions so it is more realistic and more accurate for decision making. The results of this paper demonstrate a recommendation for ISO 376:2011 to modify its classification criteria for the force proving instruments in the upcoming version of this standard.

Abstract. Monte Carlo Simulation (MCS) and Expression of Uncertainty in Measurement (GUM) are the most common approaches for uncertainty estimation. In this work MCS and GUM were used to estimate the uncertainty of hardness measurements. It was observed that the resultant uncertainties obtained with the GUM and MCS without correlated inputs for Brinell hardness (HB) were ±0.69 HB, ±0.67 HB and for Vickers hardness (HV) were ±6.7 HV, ±6.5 HV, respectively. The estimated uncertainties with correlated inputs by GUM and MCS were ±0.6 HB, ±0.59 HB and ±6 HV, ±5.8 HV, respectively. GUM overestimate a little bit the MCS estimated uncertainty. This difference is due to the approximation used by the GUM in estimating the uncertainty of the calibration curve obtained by least squares regression. Also the correlations between inputs have significant effects on the estimated uncertainties. Thus the correlation between inputs decreases the contribution of these inputs in the budget uncertainty and hence decreases the resultant uncertainty by about 10%. It was observed that MCS has features to avoid the limitations of GUM. The result analysis showed that MCS has advantages over the traditional method (GUM) in the uncertainty estimation, especially that of complex systems of measurement. MCS is relatively simple to be implemented.

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The proficiency test (PT) is a powerful tool to help a laboratory to demonstrate its competence. The statistical analysis used plays very important role in the PT results. This paper demonstrates two PT numerical examples for tensile test using different statistical methods in their analysis. The study shows that zeta score and the normalized error value (E n ) give representative impression about the consistency of the results with regarding the claimed laboratories uncertainties. The robust z-score value gives opportunity to avoid the effect of the outlier values regardless of the claimed laboratory uncertainty.