The analysis of feature-based measurement error in coordinate metrology

Coordinate measurement systems (CMSs) dominate the dimensional control and diagnostics of various manufacturing processes. However, CMSs have inherent errors caused by the lack of a tracing ability for some of the measured part features. This is important for product inspection and process variation reduction in a number of automated manufacturing systems, such as for example the automotive body assembly process. The lack of a feature tracing ability means that instead of measuring a given feature, the CMS may actually measure the area around the selected feature. In this paper, a principle for the part feature tracing ability and the resultant feature-based measurement error analysis are developed to estimate the aforementioned deficiencies in the CMSs. The impact of feature type and part(s) positional variation on the feature-based measurement error is explored. The proposed approach is applicable to both contact and non-contact CMSs including both mechanical and optical coordinate measuring machines. An analysis of the error for different measurement algorithms is presented. We show that the developed feature-based measurement error can have a significant impact on the measurement accuracy and hence on process control and the diagnostic algorithms currently used in manufacturing. A feature-based error map and error compensation approach are also developed and presented. Simulations, experimental results and two industrial case studies illustrate the proposed method.