This paper presents the results of a study on the dimensional accuracy analysis of models produced by 3D printing technology—Fused Filament Fabrication (FFF). Geometric measurements were conducted using a dial caliper, a 3D scanner and a coordinate measuring machine. In addition, a statistical analysis of the test results was carried out, considering the division into different numbers of test samples (3, 5, 10, 20, 30). The analysis of the test results made it possible to assess the influence of the measuring tools used and the number of samples tested on the final measurement result, as well as to determine the consequences associated with it.

A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, we jointly address measurement system type classification and regression of key surface parameters—arithmetic mean roughness (Ra), mean peak-to-valley roughness (Rz), and total roundness deviation (RONt)—alongside their reported standard uncertainties. Uncertainty is modelled via quantile and heteroscedastic regression heads, with post hoc conformal calibration used to obtain calibrated prediction intervals. On a held-out test set, high fidelity was achieved by single-target regressors (coefficients of determination: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (standard uncertainty of Ra 0.9899, standard uncertainty of Rz 0.9955); the standard uncertainty of RONt remained more difficult to learn (0.4934). The classifier reached 92.85% accuracy, and probability calibration was essentially unchanged after temperature scaling (expected calibration error 0.00504 → 0.00503 on the test split). Negative transfer was observed for naive multi-output trunks, with single-target models performing better. These results provide calibrated predictions suitable for informing instrument selection and acceptance decisions in metrological workflows.

This paper presents new method for traceability assurance of measurements performed using hybrid measuring systems built using one system that is based on tactile point measurement method (for example: coordinate measuring machine, articulated arm coordinate measuring machine, laser tracker...

The production and assembly of large engineering structures, many requiring tight tolerances, demand accurate long-distance measurements. This poses a major challenge for metrologists across industries such as energy, aviation, automotive, and machinery. Contact measurements provide high accuracy but are slow, as tactile probes must be moved over large distances. Optical methods are much faster, yet their effective range is usually limited to a few meters, and they generally offer lower accuracy. Measurements of large-scale components are further complicated by varying environmental conditions (e.g., temperature gradients) and the accumulation of different error sources, making high-accuracy measurements difficult to achieve. These challenges motivated the authors to develop hybrid measurement systems (HMS) and methods for improving their accuracy. This paper describes the steps taken to build an HMS combining a large-volume, high-accuracy coordinate measuring machine with a structured-light scanner. It also presents a dedicated method for determining measurement uncertainty in HMS, based on a multiple-measurement strategy. A series of tests were performed on material standards with various shapes, dimensions, and geometric features, using both contact and optical systems. The measurement uncertainties were then evaluated using the developed method. Finally, the method was validated through tests conducted on a selected large-scale engineering object.