Abstract
Optical 3D measuring systems serve as indispensable tools for the measurement and quality control of complex objects feeding process chains in industrial information integration. However, the accuracy of 3D measurements is influenced by a multitude of parameters, and the associated measurement uncertainties and influential factors remain insufficiently researched. This study investigates the effects of measurement object properties and software on measurement outcomes. Specifically, we examine seven geometries (diameter, distance, roundness, concentricity, flatness, parallelism and verticality) and four influencing factors (surface roughness, coating, polygonization, and interpolation). Our analysis employs variance analysis and compares the results with those obtained through linear regression using machine learning. In conclusion, the analysis of measurement uncertainty for optical 3D measurement systems in the assessment of seven distinct geometric characteristics provides a framework for determination of process chain suitability of the optical 3D measuring system.
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