*Article* **Three-Dimensional Point Cloud Task-Specific Uncertainty Assessment Based on ISO 15530-3 and ISO 15530-4 Technical Specifications and Model-Based Definition Strategy**

**Gorka Kortaberria \*, Unai Mutilba, Sergio Gomez and Brahim Ahmed**

Department of Mechanical Engineering, Tekniker Research Centre, 20600 Eibar, Spain **\*** Correspondence: gorka.kortaberria@tekniker.es

**Abstract:** Data-driven manufacturing in Industry 4.0 demands digital metrology not only to drive the in-process quality assurance of manufactured products but also to supply reliable data to constantly adjust the manufacturing process parameters for zero-defect manufacturing processes. Better quality, improved productivity, and increased flexibility of manufacturing processes are obtained by combining intelligent production systems and advanced information technologies where in-process metrology plays a significant role. While traditional coordinate measurement machines offer strengths in performance, accuracy, and precision, they are not the most appropriate in-process measurement solutions when fast, non-contact and fully automated metrology is needed. In this way, non-contact optical 3D metrology tackles these limitations and offers some additional key advantages to deploying fully integrated 3D metrology capability to collect reliable data for their use in intelligent decisionmaking. However, the full adoption of 3D optical metrology in the manufacturing process depends on the establishment of metrological traceability. Thus, this article presents a practical approach to the task-specific uncertainty assessment realisation of a dense point cloud data type of measurement. Finally, it introduces an experimental exercise in which data-driven 3D point cloud automatic data acquisition and evaluation are performed through a model-based definition measurement strategy.

**Keywords:** uncertainty assessment; three-dimensional point clouds; ISO 15530; data-driven metrology; model-based definition; virtual twin
