The paper presents the automated on-line system for wood logs 3D geometry scanning. The system consists of 6 laser triangulation scanners and is able to scan full wood logs which can have the diameter ranging from 250 mm to 500 mm and the length up to 4000 mm. The system was developed as a part of the BIOSTRATEG project aiming to optimize the cutting of logs in the process of wood planks manufacturing by intelligent positioning in sawmill operation. This paper illustrates the detailed description of scanner construction, full measurement process, system calibration and data processing schemes. The full 3D surface geometry of products and their applied portion of selected wood logs formed after cutting out the cant is also demonstrated.

This paper presents a photogrammetry-based volume measurement framework for the particle density estimation of Lightweight expanded clay aggregate (LECA). The results are compared with computed tomography (CT) and Archimedes’ method measurements. All of the steps required in order to apply the proposed approach are explained. Next, we discuss how the interpretation of open pores affects the results of volume measurements. We propose to process the shapes obtained from different methods by applying an Ambient Occlusion algorithm with the same threshold, t = 0.175. The difference between the CT and SfM methods is less than 0.006 g/cm³, proving that the photogrammetry-based approach is accurate enough. The Archimedes’ method significantly overestimates the density of the particles. Nevertheless, its accuracy is acceptable for most engineering purposes. Additionally, we evaluate the accuracy of shape reconstruction (in terms of the Hausdorff distance). For 95% of the grain’s surface, the maximum error is between 0.073 mm and 0.129 mm (depending on the grain shape). The presented approach is helpful for measuring the particle density of porous aggregates. The proposed methodology can be utilized in order to estimate intergranular porosity, which is valuable information for the calibration of DEM models.

Ninety percent of high temperature electronic devices operate in temperatures in the range of 150 to 300 °C, and for such temperature needs, technologies typical for the military range might be adapted. To make it possible, new joining techniques are developed, one of them is use of pastes with silver nanoparticles sintered with low temperature joining technique. Silver sintered joints have three times higher thermal conductivity and five times lower electrical resistivity than typical solders, while being able to operate in temperatures reaching 350 °C. In this paper, the authors show the impact of additions of carbon nanoparticles on joints prepared in low temperature joining technology (LTJT). The authors prove that an addition of few percent of graphene nanoplatelets (GNPs) or carbon nanotubes (CNTs) improves joints mechanical, thermal, and electrical properties, while ensuring proper rheology of pastes.

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.