RENUMBER - Differentiating between measurement uncertainty and model error in the description of measurement processes

 

Measurement processes are always subject to uncertainty, so that the statements derived from the measurement data are also uncertain. If the measurement uncertainty in a product inspection is too high and the characteristic under consideration is close to the specification limits, the decision as to whether the characteristic is within or outside the specification is fraught with risk. Thus, correctly produced parts can be mistakenly rejected, or defective parts mistakenly released.

Since the risk of incorrect decisions cannot be determined and thus controlled without knowledge of the measurement uncertainty, measured values without measurement uncertainty information are worthless. To determine the measurement uncertainty, the so-called model of the measurement is needed. With this model, the measurement uncertainty is determined based on the natural fluctuations of the input variables. Systematic deviations within the measurement process are taken into account when determining the measurement uncertainty, provided that they cannot be eliminated. Deviations between reality and model in the sense of a model error, however, are often not taken into account when determining the measurement uncertainty. As a result, the specified measurement uncertainty may be overestimated and costs may be incurred due to the incorrect specification of the manufacturing tolerance range.

In the research project "RENUMBER" a method is developed which addresses the differentiation between measurement uncertainty and model error. The method is based on a Bayesian approach and modeling using machine learning.

In order to make the procedure tangible for practical application, the individual steps are combined into an overall procedure and implemented in a freely accessible programming language. The method is validated using several example measurement processes to ensure applicability in an industrial context.