Automated assessment of measurement quality in optical coordinate metrology of complex freeform parts

The measurement of complex freeform geometries represents a fundamental challenge for quality assurance in the production of high value-added parts, in particular when additive manufacturing technologies are involved. In addition, the increasing advances towards automation and integration in industrial production hint at the possibility of developing intelligent coordinate measuring systems, capable of autonomously planning a measurement process and assessing measurement performance while the inspection task is in progress. In this context, optical measurement technologies appear as ideal candidates, featuring high sampling densities, relatively short measurement times and capablity to access complex surfaces. In this work, the ongoing development of algorithmic solutions dedicated to the automated assessment of measurement quality is discussed. The solutions are designed to be embedded in smart and autonomous coordinate measuring systems, and need only the acquired point clouds and knowledge of the nominal geometry (CAD model) to operate. At the core of the algorithmic solutions, point cloud analysis and spatial statistics are used to assess measurement uncertainty and part coverage, the latter referring to the capability to sample hidden surfaces and hollow features, as typically found in additively manufactured parts. The algorithmic solutions are illustrated and validated through application to a test case of industrial relevance, generated via additive manufacturing.