Michał Nawotka

This article presents the results of comparative studies on the measurement of geometric features of models produced using selected industrial 3D printing technologies. Measurements were performed on a self-designed model, the shape of which was inspired by artifacts compliant with the standard for assessing 3D printer accuracy. Three different structured-light 3D scanners manufactured by SMARTTECH Sp. z o.o. and marketed under the SMARTTECH3D brand were used for the comparative studies, and a coordinate measuring machine (CMM) was used as a reference measurement. The aim of the study was to determine the capabilities and limitations of 3D scanners in performing metrological measurements of geometric models with advanced freeform and inclined pocket shapes, and to determine the impact of the measurement/metrological strategy on selected measurement quality characteristics. The results showed significant differences in the number of measured points and the ability to analyze freeform geometric features, depending on the selected measurement strategy—the angle of the 3D scanner camera and the kinematics of the scanning system. The two scanners, S/T 2401 (with a robotic arm) and S/T 2402 (with a rotational table), showed point clouds of 22–26 million records, and the high-resolution S/T 2502 (with a rotational table) showed a point cloud of 64 to 82 million records, which is 300% more.

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.

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, measurement system type classification is addressed alongside coordinated regression of Ra, Rz, RONt and their uncertainty targets (Ra_uncert, Rz_uncert, RONt_uncert). Uncertainty is modelled via quantile and heteroscedastic heads with post-hoc conformal calibration to yield calibrated intervals. On a held-out set, high fidelity was achieved by single-target regressors (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (Ra_uncert 0.9899, Rz_uncert 0.9955); RONt_uncert remained difficult (R2 0.4934). The classifier reached 92.85% accuracy and probability calibration was essentially unchanged after temperature scaling (ECE 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 to inform instrument selection and acceptance decisions in metrological workflows.

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.

Compliant mechanisms have gained an increasing interest in recent years, especially in relation to the possibility of using 3D printers for their production. These mechanisms typically find applications in precise positioning systems of building robotic devices or in sensing where they can be used to characterize displacement. Three-dimensional printing with PLA materials allows fiber optic-based sensors to be incorporated into the structures of properly designed compliant mechanisms. Therefore, in this paper, an innovative technology is described, of a Fiber Bragg Grating (FBG) sensor embedded in a measuring head which was then inserted into a specially designed mechanical transmission element. The shape of this element is based on clippers that allow to freely modify the amplification of displacement amplitude so that the FBG sensor always works in the most optimal regime without any need to modify its external dimensions. Flexural sensitivity of the replaceable measuring head equal to 1.26 (mε/mm) can be adapted to the needs of the flexure design.