Adaptive data reduction with neighbourhood search approach in coordinate measurement of planar surfaces

The computational tasks required in the coordinate metrology of manufactured surfaces including Point Measurement Planning (PMP), Substitute Geometry Estimation (SGE), and Deviation Zone Evaluation (DZE) are typically conducted sequentially. This paper represents a methodology to integrate PMP, SGE, and DZE in order to reduce the inherent sources of computational uncertainties in coordinate measurement of planar surfaces. The methodology is developed based on a closed-loop of three tasks, where the results of SGE and DZE are used to properly revise the set of sample points. The goal of this study is to find a PMP approach that uses a minimal number of sampled points to represent the inspected surface efficiently. Several different sampling strategies are presented and are employed for the inspection of the manufactured surfaces with various patterns of distribution of geometric deviations. A comprehensive experimental study is conducted and statistically analyzed to detect the most reliable sampling strategy to be used as the PMP engine of the loop. It is shown that the developed methodology effectively finds the minimum deviation zone of the surfaces using a small number of sample points. The adaptive computational coordinate metrology method developed in this work can potentially be utilized in the inspection of other geometric features and freeform surfaces.