Automation is a key point in many industrial tasks such as calibration and metrology. In this context, machine vision has shown to be a useful tool for automation support, especially when there is no other option available. A system for the calibration of portable measurement devices has been developed. The system uses machine vision to obtain the numerical values shown by displays. A new approach based on human perception of digits, which works in parallel with other more classical classifiers, has been created. The results show the benefits of the system in terms of its usability and robustness, obtaining a success rate higher than 99% in display recognition. The system saves time and effort, and offers the possibility of scheduling calibration tasks without excessive attention by the laboratory technicians.

In many metrological applications the data being measured is associated to the phase difference codified in two fringe patterns. This phase difference can be recovered directly with what are called Differential Phase Shifting Algorithms (DPSAs) by using a combination of irradiance values from both patterns in the arctangent argument. Use of such algorithms requires characterisation mechanisms to inform of their sensitivity to the various random and systematic error sources, which is the same as for well-studied Phase Shifting Algorithms (PSAs). Thus, we present a new analysis of error propagation for DPSAs taking into account the frequency shifting property of the employed arctangent function. The general analysis is verified for significant specific cases associated to large errors that appear during phase difference evaluation using the Monte Carlo method, which provides a characterisation of a DPSA's sensitivity; this is an alternative to spatial and temporal techniques but has wholly coinciding results. Monte Carlo simulation opens up the possibilities for the analysis of other error types for any DPSA.

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This paper describes the uncertainty contribution of nine different phase-shifting algorithms (PSAs) to a gauge block calibration evaluated using a Monte Carlo method. The phase map and its standard deviation, codified in the output distribution obtained with each PSA, are used as input parameters in the exact fractions method for the final calculation of the gauge block length. Results obtained show the behaviour of each PSA versus different types of error sources. Uncertainty evaluation fits well to a Gaussian distribution for all algorithms tested with more than 10 4 trials and the central limit theorem is satisfied. As expected, the Symmetric 5 + 1 PSA shows the best behaviour with the lower uncertainty contribution and appears among PSAs used as the best algorithm for this technical application. As the selected PSAs are representative for the main PSA families, the protocol employed can be used for any other specifically tailored algorithm.

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