The session will focus on integrating emerging methods for evaluating measurement uncertainty in coordinate metrology with computational algorithms and mathematical models. It combines practical metrology with advanced mathematical methods, highlighting the interdisciplinary nature of the topics and the importance of sound mathematical foundations in developing modern metrological tools. The presented solutions may also be applicable to other fields of science and engineering where mathematical modelling and measurement uncertainty analysis are important.

Particular attention will be given to approaches based on mathematical modelling, numerical simulations, Monte Carlo methods, artificial intelligence and machine learning, as well as to the development of new mathematical and statistical tools for measurement purposes. Within the area of mathematical methods, particular emphasis will be placed on probabilistic methods, sensitivity analysis, parameter estimation, inverse problems, and multidimensional and nonlinear modelling. Another important area of the session will be the concepts of virtual machines and Digital Twins, supporting the analysis, optimisation and evaluation of measurement processes.

These solutions enable virtual evaluation of measurement strategies, analysis of the influence of system parameters, and prediction of achievable accuracy before measurements are performed. They can also support the optimisation of measurement processes and the selection of suitable measurement systems for specific applications.

The session will bring together the practical expertise of TC14 and the mathematical expertise of TC21, creating opportunities to exchange experience and develop integrated solutions. It will provide TC14 participants with valuable mathematical tools for coordinate metrology, while offering TC21 participants concrete metrological challenges and potential areas for applying and further developing their methods.
Itemized list of topics:
– Methods for evaluating measurement uncertainty in coordinate metrology: modelling, Monte Carlo methods and parametric methods.
– Development of new mathematical and statistical tools for measurement and method validation.
– Probabilistic methods, sensitivity analysis, parameter estimation, inverse problems, and multidimensional and nonlinear modelling in measurement.
– Applications of artificial intelligence and machine learning to measurement uncertainty evaluation.
– Virtual Machines and Digital Twins in coordinate metrology.
– Virtual evaluation of measurement strategies, accuracy prediction, and optimisation of measurement processes.