RGB-D cameras are employed in several research fields and application scenarios. Choosing the most appropriate sensor has been made more difficult by the increasing offer of available products. Due to the novelty of RGB-D technologies, there was a lack of tools to measure and compare performances of this type of sensor from a metrological perspective. The recent ISO 10360-13:2021 represents the most advanced international standard regulating metrological characterization of coordinate measuring systems. Part 13, specifically, considers 3D optical sensors. This paper applies the methodology of ISO 10360-13 for the characterization and comparison of three RGB-D cameras produced by Intel^® RealSense™ (D415, D455, L515) in the close range (100–1500 mm). ISO 10360-13 procedures, which focus on metrological performances, are integrated with additional tests to evaluate systematic errors (acquisition of flat objects, 3D reconstruction of objects). The present paper proposes an off-the-shelf comparison which considers the performance of the sensors throughout their acquisition volume. Results have exposed the strengths and weaknesses of each device. The D415 device showed better reconstruction quality on tests strictly related to the short range. The L515 device performed better on systematic depth errors; finally, the D455 device achieved better results on tests related to the standard.

RGB-D cameras have been widely used in various research fields and applications in recent years. With the growing availability of commercial products, choosing the most suitable sensor for a specific application has become more complex. The lack of tools to evaluate and compare the metrological performance of these sensors was addressed by the recent ISO 10360-13:2021 standard, which defines the standards for the metrological characterization of 3D optical coordinate measuring systems. This paper applies the ISO 10360-13 methodology to characterize and compare four RGB-D cameras: three Intel® RealSense™ (D415, D435i, D455) and one Microsoft® Azure Kinect DK. The procedures described in the standard were integrated with three tests: the analysis of systematic errors measured acquiring a planar surface at increasing distances; a test to analyze the resolution performances with a specifically devised artifact; the evaluation of 3D reconstruction performances on two objects with different geometric characteristics. The results highlighted the strengths and weaknesses of each device. The D415 showed better performance in reconstructions from close-range acquisitions. The Azure Kinect DK had the best results for systematic error and resolution. The D455 generally achieved the best performance in the standard tests, followed by the D435i.

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Technological advances have increased the use of plastic-based additive manufacturing for production in a variety of industries that require high mechanical properties while maintaining geometric precision. The Arburg Plastic Freeformer (APF) process, unlike other filament-based technologies, uses standard plastic granules used in mass production in injection moulding machines. This study focuses on optimising the interaction of three critical process parameters: layer thickness (LT), droplet aspect ratio (DAR) and discharge rate (DR). Previous studies have mainly varied individual parameters to evaluate mechanical and geometrical properties. In contrast, this work analyses these parameters as a whole and evaluates their combined effects on residual porosity and geometric accuracy. APF relies heavily on supports to sustain the printed part, with a smaller achievable self-supporting overhang angle compared to the Fused Filament Fabrication (FFF) printing process. For this reason, this study investigates two compatible materials, ABS Terluran GP35 and a water-soluble PVP compound named Armat11. Cylindrical and cubic samples were 3D printed using different combinations of LT, DAR and DR. A total of 150 cubic samples were printed to assess the geometric dimensional accuracy and repeatability of printing in the plate position using permutations in the printing plate. Subsequently, 30 cylindrical samples were printed for micro-CT analysis, reconstructed by CT file segmentation and compared with the ideal CAD model, in addition to the characterisation of residual porosity. The results show that optimal combinations of LT, DAR and DR produce high density parts with repeatable geometric properties. In addition, a quantitative analytical model was developed to optimise arbitrary parameter sets. This comprehensive investigation provides important insights into the APF process and increases its potential for wider industrial applications.

To implement and commission a rotational Total Skin Electron Therapy (TSET) technique using high-dose-rate electron (HDRE) beams on an Elekta VersaHD linac and a custom-made flattening filter (FF), with the aim of improving delivery efficiency and dose uniformity. Two HDRE beams (6 and 8 MeV) were characterized at an extended source-to-skin distance (SSD = 490 cm) using EBT3 film dosimetry. A customized FF was designed to optimize field uniformity in both vertical and horizontal directions. Percentage depth dose (PDD) and profile measurements were acquired in static and rotational setups. Monte Carlo simulations with EGSnrc/BEAMnrc and DOSXYZnrc were used to model the linac head and validated against measurements. Output ratios (ORs) were measured on a phantom to estimate the monitor units needed for rotational delivery. End-to-end (E2E) testing with an Alderson-Rando phantom and in vivo dosimetry on the first 50 patients were performed to assess dose uniformity and reproducibility. The custom FF significantly improved field flatness. Measured surface doses per 1000 MU were 82 cGy (HDRE1) and 106 cGy (HDRE2), with ORs of 0.52 and 0.56, respectively. The rotational technique allowed dose rates of 12.8-17.9 cGy/min and treatment times of 6-8 min. PDDs from Monte Carlo simulations matched measurements. E2E and in vivo dosimetry confirmed high uniformity and <1 % variability. In vivo dosimetry confirmed adequate surface dose coverage and intra-patient consistency. The rotational TSET technique using HDRE beams on a VersaHD linac with a custom FF is feasible, reproducible, and dosimetrically robust, representing a valid alternative to conventional dual-field approaches.