Search Results (74)

Multimode interference photonic nanodevices have been increasingly used due to their broad functionality. In this study, we present a methodology based on machine learning algorithms for inverse design capable of providing the output port position (x-axis coordinate) and MMI region length (y-axis coordinate) for achieving higher optical signal transfer power. This is sufficient to design Multimode Interference 1 × 2, 1 × 3, and 1 × 4 nanodevices as power splitters in the wavelength range between 1350 and 1600 nm, which corresponds to the E, S, C, and L bands of the optical communications window. Using Multilayer Perceptron artificial neural networks, trained with k-fold cross-validation, we successfully modeled the complex relationship between geometric parameters and optical responses with high precision and low computational cost. The results of this project meet the requirements for photonic device projects of this nature, demonstrating excellent performance and manufacturing tolerance, with insertion losses ranging from 0.34 dB to 0.58 dB. Full article

We developed a three-degree-of-freedom touch trigger probe integrated with two optical sensors. The probe includes an XY-axis cantilever stylus and a Z-axis structure supported by four parallel leaf springs. A laser diode combined with 1D and 2D position-sensing detectors (PSDs) detects angular shifts and displacement when the probe tip touches the measured surface. The optical path change amplifies the PSD response, enhancing sensitivity. Finite-element analysis verifies structural performance, and experimental validation shows the probe achieves a unidirectional repeatability of 0.18 μm. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

This research tackles the intricate machining properties of cylindrical aspheric surfaces with a versatile adaption approach utilizing a robotic arm and a compact tool head, incorporating trajectory optimization. A three-step integrated error compensation framework was established as the core to address spatial inaccuracies in robotic systems, incorporating coordinate measuring machine (CMM)-based cylindrical generatrix offset correction, laser tracker-assisted progressive coordinate calibration, and contour profiler-driven feedback compensation. Complemented by a curvature-driven trajectory design, the method ensures uniform polishing coverage for non-uniform curvature surfaces. Experimental validation on S-TiH53 glass cylindrical aspheric components demonstrated a surface profile accuracy of peak-to-valley (PV) value ≤ 2 μm, meeting stringent requirements for high-power laser applications. This systematic approach enhances both efficiency and accuracy in robotic polishing, offering a viable solution for high-end optical manufacturing. Full article

Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is a widely used additive manufacturing (AM) method for thermoplastic materials due to its low cost, accessibility, and ability to produce fully functional machine parts. Cylindrical components, common in mechanical devices, require precise dimensional and form accuracy to ensure long service life. To assess their quality, cylindricity deviation measurements are essential, as they reveal defects generated during the printing process. This study investigates the potential of optical scanning for measuring form deviations specifically cylindricity and roundness of ABS components manufactured via FDM. The influence of printing orientation (0°, 45°, 90°) on dimensional accuracy was examined using experimental models comprising three series of ten samples each, with identical process parameters except orientation. Measurements were performed using a Zeiss Prismo Navigator (Zeiss, Oberkochen, Germany) coordinate measuring machine and an ATOS II Triple Scan (GOM, Brunswick, Germany) optical scanner. Results indicate that print orientation significantly affects cylindricity deviation. The lowest deviations were achieved for specific orientations, offering guidelines for producing cylindrical surfaces of acceptable quality. The findings also show that optical scanners are not suitable for precise form deviation analysis in FDM-printed parts, confirming the higher accuracy of tactile coordinate measurement methods. Full article

Optical coordinate measurement is a universal technique that aligns with the rapid development of industrial technologies and new materials. Nevertheless, can this technique be consistently effective when applied to the precise measurement of all types of materials? As shown in this article, an analysis of optical measurement systems reveals that some materials cause difficulties during the scanning process. This article details the matting process, resulting, as demonstrated, in lower measurement uncertainty values compared to the pre-matting state, and identifies materials for which applying a matting spray significantly improves the measurement quality. The authors propose a classification of materials into easy-to-scan and hard-to-scan groups, along with specific procedures to improve measurements, especially for the latter. Tests were conducted in an accredited Laboratory of Coordinate Metrology using an articulated arm with a laser probe. Measured objects included spheres made of ceramic, tungsten carbide (including a matte finish), aluminum oxide, titanium nitride-coated steel, and photopolymer resin, with reference diameters established by a high-precision Leitz PMM 12106 coordinate measuring machine. Diameters were determined from point clouds obtained via optical measurements using the best-fit method, both before and after matting. Color measurements using a spectrocolorimeter supplemented this study to assess the effect of matting on surface color. The results revealed correlations between the material type and measurement accuracy. Full article

This article presents research and a discussion on the proper use of filtration in optical measurements. Measurements were taken using a Werth multisensory machine using a Werth Zoom optical sensor. During optical measurements, the filtration option can be used. The manufacturer defines filters as “Dust”. They allow the machine operator to define the appropriate size depending on the type of inclusions or artifacts created in the production process. They can occur in processes such as punching on presses or production in the injection molding process of plastics. The presented research results and statistical analyses confirm the assumptions regarding the validity of using filters and their values. The use of filters with a higher value significantly affects the obtained results and forces the machine user to make a reasonable choice. Full article

As additive manufacturing technologies continue to gain ground in industrial applications, the need for the accurate metrological evaluation of parts produced with advanced materials becomes increasingly critical. In this context, non-contact metrology plays a key role. This research investigates the performance of conoscopic holography as an optical metrology technique for the inspection of ceramic parts manufactured by stereolithography. However, its reliability needs to be validated, especially as factors such as material properties, surface finish, and color can significantly affect measurement accuracy. Spherical artifacts in alumina were chosen as mathematically well-defined reference elements, and a representative series was produced with the best values for the printing, debinding, and sintering parameters. These spheres were first measured via contact with a coordinate measuring machine (CMM) to establish dimensional (diameter) and geometrical (form error) reference values. These parameters were then compared with measurements obtained via conoscopic holography and optimized by means of Gaussian filters. The results indicated significant dimensional (up to 60 µm) and geometrical (up to 280 µm) deviations from the CMM reference data. The investigation shows that conoscopic holography does not ensure an accurate measurement method for this additive process and ceramic material, making it impossible to achieve power and frequency settings that would allow signal-to-noise ratios above 50%. Full article

Optical window freeform surfaces have emerged as a critical research focus in advanced optical engineering owing to their extensive surface degrees of freedom. These surfaces enable the simultaneous correction of on-axis and off-axis aberrations while satisfying stringent requirements for high-performance, lightweight, and compact optical systems. In the initial metrological characterization of these surfaces, coordinate measuring machines (CMMs) are conventionally employed for target point cloud acquisition. However, the achievable measurement accuracy (>2 μm) inherently constrained by CMM precision imposes fundamental limitations for subsequent optical inspections requiring sub-micron to nanometer-level resolution. Meanwhile, although optical measurement methods can result in higher measurement accuracy, they also lead to an increase in costs and testing difficulties. To overcome these limitations, we propose an accelerated point cloud registration methodology based on point-to-triangulation distance estimation. In simulation, using optimal coordinate transformation enabled good capabilities for exceptional surface characterization: peak-to-valley (PV) surface error of 10⁻⁶ nm, residual error of 5 nm, and registration accuracy of log₁₀ (mm/°). Further, in the experiment, the PV surface error was reduced from 27.3 μm to 6.9 μm, equivalent to a reduction of 3.95 times. These results confirm that the point-to-triangulation distance approximation maintains sufficient fidelity to the nominal point-to-surface distance, thereby empirically validating the efficacy of our proposed methodology. Notably, compared with conventional 3D alignment methods, our novel 2D estimation registration approach with point-to-triangulation surface normal vectors demonstrates significant advantages in computational complexity, which achieved a 78% reduction from O(n³) to O(n) while maintaining sub-millisecond alignment times. We believe that the method has potential for use as a low-cost optical precision measurement in manufacturing technology. Full article

Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion–extension (FE) and radial–ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose ($FE-99$; $RUD-25.2$). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM. Full article

Surveys of fragmentations, especially in the early stages of the given event, are fundamental for determining the number of fragments, identifying and cataloging them, and monitoring their future evolution. The development of a ground-based optical survey strategy, i.e., a suitable observation and detection method for the fragments generated by these events, is an important contribution to acquiring data and monitoring these catastrophic phenomena. An optical survey offers an interesting and cost-effective method that supports radar operations in the Low Earth Orbit regime and can monitor higher orbits where radar cannot be used. This paper presents a developed optical survey strategy for multi-observatory observations. The strategy was tested on the fragmentation event of FREGAT R/B CLUSTER 2, a rocket body with a “dummy” payload, fragmented on 8 April 2024 on a Highly Elliptical Orbit. The observational campaign involved different observatory systems, and it represented a key collaboration within the Inter-Agency Space Debris Coordination Committee. The survey started from a simulation of the cloud of fragments and was implemented by the planification and coordination of different observatory systems with different schemes and methods to scan the sky vault. The acquired survey data were analyzed using machine learning methods to identify the unknown objects, i.e., the fragments. The data acquired were compared with the simulated cloud used for the survey, and a correlation of measurements belonging to the same object was performed. Also, the parent body was characterized in its tumbling motion by the light curve acquisition. Full article

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards. Full article

A compact and miniaturized laser collimation system was proposed to measure the four-degrees-of-freedom of an optical payload in high-altitude space. Compared with other systems, this system has a simple structure and low cost, high measurement accuracy, and a large measurement range. The optical structure of the system was designed, the measurement principle of the four-degree-of-freedom was described in detail, the interference between the distance measurement and the angle measurement in the optical path was analyzed, and the installation error was analyzed. The error was minimized under different temperature conditions to improve the robustness of the system. An engineering prototype was built based on the system design scheme and an experiment was conducted to measure a target with a measured distance of 500 mm. The current indicators reached the requirements for the ground testing of optical payloads. The application of the system can be used to measure six degrees of freedom simultaneously by installing two systems in different coordinate systems. The system can also be used in industry; for example, by measuring the machine tool error in real time and compensating for it, the system can improve the positioning and motion accuracy. It can also be used for feedback control of the robot’s motion by measuring and controlling it. Full article

Magnetorheological finishing (MRF) of aspherical optical elements usually requires the coordination between the translational axes and the oscillating axes of the machine tool to realize the processing. For aspheric optical elements whose steepness exceeds the machining stroke of the equipment, there is still no better method to achieve high-precision and high-efficiency error convergence. To solve this problem, an MRF method combining virtual-axis technology and a spiral scanning path is proposed in this paper. Firstly, the distribution law of the magnetic induction intensity inside the polishing wheel is analyzed by simulation, the stability of the removal efficiency of the removal function within the ±7^∘ angle of the normal angle of the polishing wheel is determined, and MRF is expanded from traditional single-point processing to circular arc segment processing. Secondly, the spiral scanning path is proposed for aspherical rotational symmetric optical elements, which can reduce the requirements of the number of machine tool axes and the dynamic performance of machine tools. Finally, an aspherical fused silica optical element with a curvature radius of 400 mm, K value of −1, and aperture of 100 mm is processed. The PV value of this optical element converges from 189.2 nm to 24.85 nm, and the RMS value converges from 24.85 nm to 5.74 nm. The experimental results show that the proposed combined process has the ability to modify curved optical elements and can be applied to ultra-precision machining of high-steepness optical elements. Full article

Additive Manufacturing (AM) is advancing technologically towards the production of components for high-demand mechanical applications with stringent dimensional accuracy, leveraging metallic and ceramic raw materials. The AM process for ceramic components, known as Ultraviolet Laser Stereolithography (SLA), enables the fabrication of unique parts or small batches without substantial investments in molds and dies, and avoids the problems associated with traditional manufacturing, which involves multiple stages and final machining for precision. This study addresses the need to produce reference elements or targets for metrological applications, including verification, adjustment, or calibration of 3D scanners and mid- to high-range optical sensors. Precision spheres are a primary geometry in this context due to their straightforward mathematical definition, facilitating rapid and accurate error detection in equipment. Our objective is to exploit this novel SLA process along with the advantageous optical properties of technical ceramics (such as being white, matte, lightweight, and corrosion-resistant) to materialize these reference objects. Specifically, this work involves the fabrication of alumina hemispheres using SLA. The manufacturing process incorporates four design variables (wall thickness, support shape, fill type, and orientation) and one manufacturing variable (the arrangement of spheres on the printing tray). To evaluate the impact of the design variables, dimensional and geometric parameters (GD&T), including diameters, form errors, and their distribution on the surface of the sphere, have been characterized. These measurements are conducted with high accuracy using a Coordinate Measuring Machine (CMM). The study also examines the influence of these variables in the dimensional and geometric accuracy of the spheres. Correlations between various parameters were identified, specifically highlighting critical factors affecting process precision, such as the position of the piece on the print tray and the wall thickness value. The smallest diameter errors were recorded at the outermost positions of the tray (rear and front), while the smallest shape errors were found at the central position, in both cases with errors in the range of tens of micrometers. In any case, the smallest deformations were observed with the highest wall thickness (2 mm). Full article