MUKOM - Cost-efficient determination of the measurement uncertainty of complex measurement processes

• Applicant: Professor Dr.-Ing. Robert Heinrich Schmitt
• Subject Area: Production Automation and Assembly Technology ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics ; Measurement Systems
• Term: from 2017 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 388678238

The result of every measurement is impaired with an uncertainty. Because of this measurement uncertainty, the inspection of conformity can result in a faulty test decision with two consequences. Either good parts are scrapped or malfunctioning parts are delivered to the customers. In order to make sure that the test decision is correct, it is necessary to know the uncertainty of the measurement process. Right now and with the currently available procedures, it is already possible to determine the measurement uncertainty for a large amount of measurement processes. However, this remains a challenge for measurement processes, for which there are no standardized metrological models applicable. These can be summarized under the term complex measurement processes. Examples are hardness testing, inspections with large amounts of influencing quantities or inspections with inhomogeneities in the material. Nevertheless, there is a verifiable need in research and the industry for such a procedure. The goal of this research project is therefor to develop a procedure for the estimation of the measurement uncertainty for complex inspection processes. The procedure qualifies the user to identify relevant influence quantities, model complex inspection processes, include knowledge from experts into the modeling. Compared to the conventional procedures, the newly developed procedure will use an innovative approach, which combines the analytical modeling in accordance to the scientifically accepted GUM (Guide to the Expression of Uncertainty in Measurement) and the deduction of meta models from experimental data. Due to this, one can benefit from the advantages of the exact system modeling based on previous knowledge and the experimental modeling using systematic tests.

• DFG Programme: Research Grants