Surface Measurements Research Articles

Investigation on multiple bending using laser forming of mild steel plate and its bend angle prediction

In manufacturing industries like shipbuilding, automotive, microelectronics, and aerospace, laser forming is a potential new technology. Laser forming is an intricate thermo-mechanical process in which the process mechanisms are determined by the temperature profile across the sheet thickness, which is influenced by laser power, scan speed, number of passes, laser beam diameter, and workpiece geometry. During the process, the material deforms because of compressive stress generated in the underneath region due to the thermal expansion of the irradiated region. This study focuses on the multiple bending and the effect of different process parameters on the laser bending of the sheet. Experiments were performed on 4 mm and 8 mm thick sheets of mild steel sheet of grade IS 2062 E250 using a 2-kW fiber laser with various influencing parameters such as power, scan speed, and laser spot diameter. Hardness and grain size measurements of laser-formed surfaces at different combinations of influencing parameters were performed and the features formed due to laser irradiation were analyzed. An artificial neural network (ANN) model is proposed to predict the bend angle value.

Vertical Gradient of Nitryl Chloride and Implications for Atmospheric Photochemistry in Pearl River Delta, China, during Wintertime.

Nitryl chloride (ClNO2) is a key precursor of chlorine radicals, influencing atmospheric oxidation and secondary pollutants formation. Few studies have examined the ClNO2 chemistry from the perspective of the planetary boundary layer. Here, we conducted a vertically resolved investigation of ClNO2 at six heights (ranging from 5 to 335 m) on a 356 m tower in the Pearl River Delta, China, during winter 2021. Nocturnal ClNO2 is notably lower at the surface than the upper layers, with the nocturnal median concentration at 220 m (51 parts per trillion by volume, pptv) being approximately three times higher than that recorded in the surface layer (16 pptv). Observation-constrained box model simulations show that the NO gradients primarily account for the vertical disparities. Compared to the hydroxyl radical (OH) production via the nitrous acid and ozone photolysis, ClNO2 photolysis contributes to radical formation by 3.8% (1.8%) at 220 m (5 m) in the morning (07:00-08:00), indicates the enhanced significance of ClNO2 chemistry aloft compared with the ground, and may cause the underestimation of ClNO2 photolysis impacts if solely relying on surface measurements. We highlight that more field studies are needed to elucidate ClNO2 chemistry across the boundary layer.

Analysis and Generation of Rough-Surfaced Fractures with Variable Aperture Based on Self-Affine Methods Using Surface Scan Measurements

Abstract Methods founded on self-affine principles have successfully been used to generate synthetic rough-surfaced fractures, which can be based on properties inferred from measurements of fracture surfaces. Two key parameters, the Hurst exponent H and a scaling parameter Sp, together with a function describing the correlation between the surfaces forming the fracture, are needed. There are several methods for determining H and Sp in the literature, which are primarily adopted for measurements of 1-dimensional fracture traces. However, comparatively few studies have used these methods and applied them to aperture generation. In this study, we evaluate two commonly employed methods, the root-mean-square correlation function (RMS-COR) and the Fourier power spectrum (FPS) approach, each with several variations of their possible implementation when applied to measurements of fracture surfaces. We use high-resolution surface scans of a natural fracture sample to obtain an accurate representation of the aperture field and rough surfaces. Multiple realisations of aperture fields are generated for each method variation and their respective ensembles are evaluated against the aperture distribution of the measured fracture sample. For the fracture studied, the RMS-COR method is the most accurate method for obtaining H and Sp. We show that a linear relationship exists between H and Sp which provides a best fit of synthetically generated fractures when compared to the measured sample. Additionally, we introduce an improved approach for representing the correlation function between the two rough surfaces. Finally, we demonstrate that the model can successfully generate upscaled fracture apertures based on restricted subsections of the sample.

Advancements and opportunities to improve bottom-up estimates of global wetland methane emissions

Abstract Wetlands are the single largest natural source of atmospheric methane (CH4), contributing approximately 30% of total surface CH4 emissions, and they have been identified as the largest source of uncertainty in the global CH4 budget based on the most recent Global Carbon Project (GCP) CH4 report. High uncertainties in the bottom-up estimates of wetland CH4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH4 emissions from space. In fact, there are large disagreements between bottom-up estimates versus top-down estimates inferred from inversion of atmospheric CH4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom-up estimates of global wetland CH4 emissions, as well as how they are used in top-down inversions. These bottom-up estimates, using (1) empirical biogeochemical modeling (e.g., WetCHARTs: 125 - 208 TgCH4 yr-1); (2) process-based biogeochemical modeling (e.g., WETCHIMP: 190 ± 39 TgCH4 yr-1); and (3) data-driven machine learning approach (e.g., UpCH4: 146 ± 43 TgCH4 yr-1), were all subject to large uncertainties (~80 TgCH4 yr-1). Major tropical and arctic wetland complexes are regional hotspots of CH4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top-down inversions to improve bottom-up estimates. Recent advances in surface measurements of CH4 fluxes (e.g., FLUXNET-CH4) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom-up estimates of wetland CH4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH4 observations directed at wetlands.

Metal-organic frameworks encapsulating gold nanoclusters and carbon dots for ratiometric fluorescent detection of formaldehyde in real food samples, construction materials and indoor environments.

Anovel fluorescence sensing nanoplatform (CDs/AuNCs@ZIF-8) encapsulating carbon dots (CDs) and gold nanoclusters (AuNCs) within a zeolitic imidazolate framework-8 (ZIF-8) was developed for ratiometric detection of formaldehyde (FA) in the medium of hydroxylamine hydrochloride (NH2OH·HCl). The nanoplatform exhibited pink fluorescence due to the aggregation-induced emission (AIE) effect of AuNCs and the internal filtration effect (IFE) between AuNCs and CDs. Upon reaction between NH2OH·HCl and FA, a Schiff base formed via aldehyde-diamine condensation, releasing hydrochloric acid. The acid triggered the degradation of ZIF-8, releasing CDs and AuNCs, and thereby shifting the fluorescence to blue as the CDs disperse. The nanoplatform demonstrated high sensitivity (LOD of 0.26-2.19 μM), exceptional selectivity, and rapid response time (<1 min) toward FA. Additionally, test strips and hydrogel films integrated with smartphones were prepared for on-site and visual detection of FA. The portable and smartphone-assisted test strips effectively detected indoor FA gas, while wearable and intelligent hydrogel films provided reliable surface measurements of FA on fruits and vegetables. Real sample analyses achieved satisfactory FA recoveries (99.06-115.30%) with relative standard deviations (RSD) from 1.86% to 3.81%. The innovative sensing nanoplatform served as a promising approach for FA detection in food, construction materials, and indoor air quality, offering both analytical accuracy and convenient visualization.

Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror.

Optical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose a dynamic interferometric method based on a machine learning-configured deformable mirror (DM). In this method, a dynamic interferometric system is developed. By using coaxial structure and polarization interference, transient measurement of the measured surface can be realized to meet dynamic requirements, and at the same time, DM transient monitoring can be realized to reduce the accuracy loss caused by DM surface changes and meet dynamic requirements. A transient phase modulation scheme using machine learning to configure the DM surface is proposed, which keeps the system in a measurable state. Compared with the traditional phase modulation scheme that relies on iteration, the scheme proposed in this paper is more efficient and is conducive to meeting dynamic requirements. The feasibility is verified by practical experiments. The research in this paper has significance for guiding the application of dynamic interferometry in the measurement of dynamic surfaces.

Performance enhancement approach for a snapshot interferometer sensor of surface inspection

Due to the high level of interest recently accorded to online surface measurement methods by academia and industry, engineers can now measure engineered surfaces rapidly and accurately. The present work involved illumination and individually examining a snapshot dispersive optical interferometer sensor with two different types of high-powered super-luminescent diodes (SLDs). Fringe pattern visibility is crucial to the effectiveness of this kind of interferometer. Two scenarios were introduced to examine the interference pattern visibility for the first-order (scanning range). Compared to the zero-order (reference beam), the first-order (diffraction/measurement beam) is less intense. Undesirable outcomes are generated by the dispersive line beam, like increased noise at a reduced intensity at the scanning range margin. Noise is among the parameters adversely impacting effective signal in all classes of optical measurement sensors; subsequently, sensor resolution measurement is influenced by any elevation in first-order intensity. The measurement noise peak to peak of the analyzed sample was developed from 28 to 20 nm. Consequently, it is possible to apply the snapshot sensor to a next-generation snapshot fiber link interferometry sensor for surface inspection.

A general anisotropic yield function for K0-consolidated clays

The importance of yield surface for clays lies in describing compression behaviour, shear strength and induced anisotropy. Various yield surfaces were proposed and adopted in modelling, but they not consistent on describing anisotropic consolidation and/or undrained shear with both compression and extension stress paths of K0-consolidated clays. This study proposes a new generalized anisotropic yield function for K0-consolidated clays. First, limitations of existing yield surface are discussed based on comparisons with measurements of yield surfaces on various clays. Taking the more flexible isotropic yield function of Hardin and introducing the inclination angle of yield surface, a new generalized anisotropic yield surface is then proposed. The new anisotropic yield function is successfully examined on the predictive ability on the η-constant compression behaviour, and both the g(θ)-method and the TS-method can be adopted for taking into account the Lode angle effect in three-dimensional strength. Next, the new yield function is applied to develop an anisotropic elasto-plastic bounding surface model. The predictive performance is finally evaluated by comparing experimental and predicted results of anisotropic consolidation and shear tests on two reconstituted clays.

Feature Characterization for Profile Surface Texture

Abstract Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.

A Class of Pursuit Problems in 3D Space via Noncooperative Stochastic Differential Games

This paper investigates three-dimensional pursuit problems in noncooperative stochastic differential games. By introducing a novel polynomial value function capable of addressing high-dimensional dynamic systems, the forward–backward stochastic differential equations (FBSDEs) for optimal strategies are derived. The uniqueness of the value function under bounded control inputs is rigorously established as a theoretical foundation. The proposed methodology constructs optimal closed-loop feedback strategies for both pursuers and evaders, ensuring state convergence and solution uniqueness. Furthermore, the Lebesgue measure of the barrier surface is computed, enabling the design of strategies for scenarios involving multiple pursuers and evaders. To validate its applicability, the method is applied to missile interception games. Simulations confirm that the optimal strategies enable pursuers to consistently intercept evaders under stochastic dynamics, demonstrating the robustness and practical relevance of the approach in pursuit–evasion problems.

MSCL-Net: Multi-Spot Centers Localization Network of Optical Aspheric Surface Measurement with Multi-Beam Angle Sensor

MSCL-Net: Multi-Spot Centers Localization Network of Optical Aspheric Surface Measurement with Multi-Beam Angle Sensor

Large-area surface measurement of spherical parts based on interferometry

Large-area surface measurement of spherical parts based on interferometry

Arctic Sea Ice Prediction Based on Multi‐Scale Graph Modeling With Conservation Laws

AbstractArctic sea ice prediction is critical for exploring climate change, resource extraction, and shipping route planning. This paper introduces a novel neural network model, Ice Graph Attention neTwork (IceGAT), that is trained to predict sea ice concentration (SIC) from a number of atmospheric, oceanic, and land surface measurements. It is based on two design principles: (a) the complex spatial interactions in weather dynamics are captured via a series of graphs corresponding to different spatial resolutions and (b) the incorporation of the physical conservation laws for moisture and potential vorticity. We devise two main variants with 1 hr and 24 hr temporal resolution and determine the optimal input horizon to be 5 days. IceGAT features leading accuracy (96.7%; +2.4% over the current state‐of‐the‐art) and low inference time (1/4 s, on a single GPU). An online implementation (based on data from ERA5) alongside supplementary videos and our shared code are accessible at: https://lannwei.github.io/IceGAT/.

Damage location and area measurement of aviation functional surface via neural radiance field and improved Yolov8 network

To realize high-precision intelligent detection, location and area measurement of aviation functional surface damage, a damage location and area measurement method combining neural radiance field and improved Yolov8 network is proposed in this paper. The high-fidelity NeRF (Neural Radiance Field) and 3DGS (3D Gaussian Splatting) models are trained by acquired multi-view optical images of damaged functional surfaces. The rendered new-view images are used as a new data augmentation method to enhance the training effect of Yolov8 network. The network architecture of Yolov8 model is improved. The backbone is replaced with the latest StarNet feature extraction network, and the context feature fusion module (CFFM) proposed in this paper is used for feature fusion enhancement. The improved context-guide multi-head self-attention (CG-MHSA) is added to the detection Head. The comparison and ablation experiment results show that the improved module proposed in this paper has a good effect on the improvement of Yolov8 model, and improves the damage detection ability and location ability of the model. The application experiment results verify the effectiveness of the proposed method for calculating the damage area of a plane/camber surface, and the accuracy of the damage area measurement is high.

Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts.

Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wildfire smoke and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and the Community Multiscale Air Quality (CMAQ) and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. In the western US, compared against surface PM2.5 measurements from the US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches to yield estimates of smoke PM2.5 concentrations that outperform individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies in effects of high-level smoke exposure across different methods. Our research highlights the differences across estimation methods for understanding the health impacts of wildfire smoke and demonstrates the importance of bench-marking estimates with available surface measurements.

Incoherent light interferometer for single-shot, dual wavelength, areal topography measurements of a reflective surface.

This paper introduces an interferometer for single-shot areal quantitative phase imaging at two wavelengths simultaneously, suitable for use with low coherence sources. It operates in reflection geometry with on-axis illumination, so that it can be conveniently applied to surface texture measurements. The system consists of two identical 4f systems forming the reference and sample arm. The reference arm is terminated by an optical assembly consisting of a dichroic mirror and two diffraction gratings, providing two separate dispersed off-axis reference beams. Two sets of distinct, separable interference fringes allow the use of the synthetic wavelength technique to obtain an extended unambiguous range of measurement recorded in a single frame. The implementation presented here was operating with superluminescent diode (SLED) sources with central wavelengths of 757 nm and 841 nm, giving a theoretical unambiguous range of 3.79 µm. The system was experimentally tested by measuring a sample with a 1 µm deep groove in electroformed nickel. The result shows good agreement with a contact stylus profiler measurement, although further work is needed to limit the noise in the interference signals.

Investigation on the surface texture aspect ratio of freeform surfaced optics and its correlation with the optical performance

Freeform surfaces are known for their versatility in providing complex optical functionalities. The surface texture aspect ratio, representing the relationship between surface height variation and lateral feature size, is examined in relation to vari-focal lenses. Nanometric surface measurement techniques and 3D surface parameters are utilized to quantify the aspect ratio and evaluate its impact on optical performance. Experimental results demonstrate a significant correlation between higher aspect ratios and increased aberrations, leading to decreased optical quality. Controlling the surface texture aspect ratio during manufacturing is crucial for achieving optimal functional performance. This study contributes to the understanding of how the aspect ratio affects the performance of freeform surfaced optic components, providing insights for the design and fabrication of high-performance optical components. Future research can explore optimization strategies and advanced manufacturing techniques to enhance functional performance in optics and optical system design.

The relationship between early life EEG and brain MRI in preterm infants: A systematic review.

To systematically review the literature on the associations between electroencephalogram (EEG) and brain magnetic resonance imaging (MRI) measures in preterm infants (gestational age<37weeks). A comprehensive search was performed in PubMed and EMBASE databases up to February 12th, 2024. Non-relevant studies were eliminated following the PRISMA guidelines. Ten out of 991 identified studies were included. Brain MRI metrics used in these studies include volumes, cortical features, microstructural integrity, visual assessments, and cerebral linear measurements. EEG parameters were classified as qualitative (Burdjalov maturity score, seizure burden, and background activity) or quantitative (discontinuity, spectral content, amplitude, and connectivity). Among them, discontinuity and the Burdjalov score were most frequently examined. Higher discontinuity was associated with reduced brain volume, cortical surface, microstructural integrity, and linear measurements. The Burdjalov score related to brain maturation qualitatively assessed on MRI. No other consistent correlations could be established due to the variability across studies. The reviewed studies utilized a variety of EEG and MRI measurements, while discontinuity and the Burdjalov score stood out as significant indicators of structural brain development. This review, for the first time, provides an extensive overview of EEG-MRI associations in preterm infants, potentially facilitating their clinical application.

Solvability of the Lp Dirichlet problem for the heat equation is equivalent to parabolic uniform rectifiability in the case of a parabolic Lipschitz graph

We prove that if a parabolic Lipschitz (i.e., Lip(1,1/2)) graph domain has the property that its caloric measure is parabolic A∞ with respect to surface measure (which property is in turn equivalent to Lp solvability of the Dirichlet problem for some finite p), then the function defining the graph has a half-order time derivative in the space of (parabolic) bounded mean oscillation. Equivalently, we prove that the A∞ property of caloric measure implies, in this case, that the boundary is parabolic uniformly rectifiable. Consequently, by combining our result with the work of Lewis and Murray we resolve a long standing open problem in the field by characterizing those parabolic Lipschitz graph domains for which one has Lp solvability (for some p<∞) of the Dirichlet problem for the heat equation. The key idea of our proof is to view the level sets of the Green function as extensions of the original boundary graph for which we can prove (local) square function estimates of Littlewood-Paley type.

Randomized clinical trial: MGrx versus standard debridement and expression for meibomian gland dysfunction.

Evaporative dry eye disease, due to meibomian gland dysfunction, causes significant suffering for millions of people globally, yet satisfactory long-term treatment remains elusive for many. Investigation of potential therapies for meibomian gland dysfunction is therefore of high importance to clinicians and their patients. This study aimed to compare the efficacy of a new device for meibomian gland debridement and expression to that of the conventional way of providing this treatment. Thirty participants (mean age, 36.4 ± 15.4 years; 77% female) fulfilling current Tear Film & Ocular Surface Society diagnostic criteria for dry eye disease and meibomian gland dysfunction were recruited (Research Registry, 10340). Fifteen participants each were randomized to receive a single treatment with either traditional debridement (using a golf-club spud), heating (10 minutes of Blepha EyeBag, Théa Laboratories, Clermont-Ferrand, France) and expression (with forceps), or the multimodal MGrx, which comprises a handheld device with heated debridement, massage, and expression attachments. Symptomatology, tear film, and ocular surface measures were assessed at baseline and at 4 and 8 weeks post-treatment. Ocular Surface Disease Index, 5-Item Dry Eye Questionnaire, and Symptom Assessment in Dry Eye symptom questionnaire scores all improved significantly with both treatments (all p<0.001), with no subsequent deterioration for at least 8 weeks. The improvement was similar between treatment groups (all p>0.05). Clinical signs, of blink rate, tear film quality and quantity, ocular surface characteristics, and meibomian gland expressibility, were all unchanged with both treatments (all p>0.05) except for noninvasive tear breakup time, which deteriorated in the conventional treatment group (p=0.006) between 4 and 8 weeks post-treatment. No adverse reactions were reported, and all participants were able to tolerate treatment. A single application of meibomian gland debridement and expression resulted in sustained improvements in the symptoms of dry eye disease, in both treatment groups. The MGrx device provides a safe and effective in-office treatment for evaporative dry eye disease, and has time and space advantages compared with conventional treatment.