Condensing Measurement Data along the Process Chain into a single Geometrical Digital Shadow

Quality assurance of complex parts, produced by elaborate process chains such as in industrialized additive manufacturing, requires the repeated digitization of products before and after each process step in order to detect any deviation from the planned geometry. This is necessary to enable the efficient production of small lot sizes in smart factories by counteracting those deviations through adaptive process control of downstream processes. The digitization of complex parts during production is often only feasible through a combination of different geometrical measurement technologies, e.g. CMM, xCT or optical 3D scanners, resulting in convoluted data sets consisting of incompatible measurement data from different points in time. In order to effectively utilize the generated data to its full potential, it is necessary to link related data while minimizing redundancy. For this purpose, the “Geometrical Digital Shadow” is proposed as a framework, which provides a way to condense all acquired geometrical data related to a physical object into a single source of truth. This work presents a methodology for a reversible fusion of geometrical data in form of meshes from different measurement technologies but also from different production steps along the process chain into a single evolving 3D model. By calculating and only saving the differences between the existing mesh and a newly generated measurement, just the relevant data is taken into account for further processing. The resulting 3D model encapsulates the data and origin of multiple measurements while reducing the overall data footprint and therefore offers the envisioned increase in information density.