Surface Topography Data Research Articles

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

Characterisation of surface morphology of high voltage DC relay contacts under arc erosion

Abstract High voltage DC relays are a crucial component in new energy vehicles. Due to the high failure rate caused by contact sticking (fusion welding), existing technologies can only analyze contact morphology after failure. There is an urgent need to determine the quantitative characterization value of contact surface morphology and the correlation between morphology, failure mechanisms, and working conditions. In this study, an Olympus DSX1000 microscope is used to extract surface topography data, and a quantitative topography characterization method based on a machine vision system is proposed to quantitatively analyze contact surface changes after arc erosion. Grey correlation analysis was applied to explore the relationship between electrical parameters such as voltage, current, and capacitance, and the contact morphology features. The results show that current and capacitance have the greatest influence on contact surface features after arc erosion, while voltage has a relatively smaller influence. This conclusion is consistent with the experimentally observed arc erosion features. Finally, an arc ion sputtering deposition model was developed to analyze the material transfer mode and its influence on the contact failure mechanism. The results provide a scientific basis for the analysis and design improvement of high voltage DC relays.

Characterizing the as-built surface topography of Inconel 718 specimens as a function of laser powder bed fusion process parameters

Purpose The ability to use laser powder bed fusion (LPBF) to print parts with tailored surface topography could reduce the need for costly post-processing. However, characterizing the as-built surface topography as a function of process parameters is crucial to establishing linkages between process parameters and surface topography and is currently not well understood. The purpose of this study is to measure the effect of different LPBF process parameters on the as-built surface topography of Inconel 718 parts. Design/methodology/approach Inconel 718 truncheon specimens with different process parameters, including single- and double contour laser pass, laser power, laser scan speed, build orientation and characterize their as-built surface topography using deterministic and areal surface topography parameters are printed. The effect of both individual process parameters, as well as their interactions, on the as-built surface topography are evaluated and linked to the underlying physics, informed by surface topography data. Findings Deterministic surface topography parameters are more suitable than areal surface topography parameters to characterize the distinct features of the as-built surfaces that result from LPBF. The as-built surface topography is strongly dependent on the built orientation and is dominated by the staircase effect for shallow orientations and partially fused metal powder particles for steep orientations. Laser power and laser scan speed have a combined effect on the as-built surface topography, even when maintaining constant laser energy density. Originality/value This work addresses two knowledge gaps. (i) It introduces deterministic instead of areal surface topography parameters to unambiguously characterize the as-built LPBF surfaces. (ii) It provides a methodical study of the as-built surface topography as a function of individual LPBF process parameters and their interaction effects.

Point cloud-based feature extraction and surface reconstruction for parts paring in high-precision assembly

Abstract Assembly plays a crucial role in industrial manufacturing in industrial manufacturing, but the efficiency of conventional manual methods for parts pairing is limited. Previous research has demonstrated the feasibility of deep learning for point cloud feature extraction and 3D reconstruction. An innovative method utilizing deep learning for high-precision feature extraction and surface reconstruction is introduced to optimize parts pairing in this paper. Geometric dimensions and surface topography data are obtained by defining key assembly features and utilizing the Random Sample Consensus method. Deep learning is then used to directly regress the Surface Distance Function from point samples, facilitating comprehensive part surface modeling and supporting assembly simulation in the digital twin. To validate this approach, a case study demonstrates successful matching of 30 shaft parts and 30 hole parts after optimization, with an increase in average uniformity by 0.024. This highlights the proposed method’s superior effectiveness and accuracy in feature extraction and surface reconstruction.

Generation mechanism of optical surface in ultra-precision cutting polycrystalline zinc selenide

Despite having an excellent infrared optical property, efficient material removal of zinc selenide (ZnSe) is yet to be realized and its defect generation mechanisms during ultra-precision cutting are not fully elucidated. In this study, ultra-precision cutting experiments on polycrystalline ZnSe were conducted by considering various cutting parameters and utilising surface topography, chip morphology, cutting force, and X-ray diffraction data to investigate its surface generation mechanisms, which were further augmented by molecular dynamics simulations to analyse the generation and suppression of surface defects. The results revealed the presence of irregular microstructures on the surface of polycrystalline ZnSe, arising from variations in material rebound owing to differing grain orientations, as well as micron-sized craters located at grain boundaries and submicron-sized pits within individual grains. Increasing the cutting speed facilitated instantaneous grain fractures, thus preventing grain peeling and effectively suppressing the generation of micron-sized craters and material rebound. Although the use of a negative rake angle on the tool suppressed the generation of micron-sized craters, it introduced a proliferation of burrs that was detrimental to the surface finish. These findings provide valuable insights for optimising the ultra-precision machining process and enhancing the surface quality of brittle polycrystalline ZnSe.

Multiscale modeling of friction hysteresis at bolted joint interfaces

Friction at connection interfaces plays an important role in understanding the nonlinear vibration response of jointed structures. A reliable friction contact model capable of reproducing nonlinear behaviors at the friction interface is critical in the design and optimization of jointed structures. In this paper, a multiscale friction model is proposed. This approach provides a novel perspective for improving prediction accuracy by combining the predictability offered by a physics-based model and the convenience of a phenomenological model. Specifically, this method considers the actual topography of joint interfaces by measuring the three-dimensional (3D) topography data with high-resolution instruments. The surface topography data is then processed to obtain the geometry data at different scales, and the finite element method is used to determine the physics-based multiscale contact pressure distribution of surfaces. The twofold Weibull mixture model is used to represent the contact pressure distribution and further determine the Iwan density function. The effectiveness of the proposed approach is validated by comparing the model predictions with the experiment results of a new as-built structure. Moreover, the effects of the surface roughness and waviness on the friction behavior are discussed.

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods.

Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods.

Measurement Studies Utilizing Similarity Evaluation between 3D Surface Topography Measurements

In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive similarity-based framework tailored for conducting measurement studies on 3D topography data, aiming to precisely quantify distinct sources of variation through the astute application of similarity evaluation techniques. In the proposed framework, we investigate the mean and variance of the similarity between 3D surface topography measurements to reveal the uniformity of the surface topography measurements and statistical reproducibility of the similarity evaluation procedure, respectively. The efficacy of our framework is vividly demonstrated through its application to measurements derived from additive-fabricated specimens. We considered four metal specimens with 20 segmented windows in total. The topography measurements were obtained by three operators using two scanning systems. We find that the repeatability variation of the topography measurements and the reproducibility variation in the measurements induced by operators are relatively smaller compared with the variation in the measurements induced by optical scanners. We also notice that the variation in the surface geometry of different surfaces is much larger in magnitude compared with the repeatability variation in the topography measurements. Our findings are consistent with the physical intuition and previous research. The ensuing experimental studies yield compelling evidence, affirming that our devised methods are adept at providing profound insights into the multifaceted sources of variation inherent in processes utilizing 3D surface topography data. This innovative framework not only showcases its applicability but also underlines its potential to significantly contribute to the field of quality assurance. By offering a systematic approach to measuring and comprehending variation in 3D topography data, it stands poised to become an indispensable tool in diverse quality assurance contexts.

Constraining subducting slab viscosity with topography and gravity fields in free-surface mantle convection models

Subducted slabs provide the primary driving force for mantle convection and plate tectonics. The strength of the slab directly controls the transfer of forces between it and the lithospheric plates at the surface. However, slab viscosity constrained by surface topography and gravity data is significantly lower than that suggested by mineral physics laboratory experiments. It is unclear whether the free-slip top boundary condition used by many studies affects the inverted slab viscosity with gravity or geoid data. In this study, we constrain the subducting slab viscosity structure by comparing the computed topography and gravity in slab subduction models with a free surface with observations. The free-surface model results support relatively weak slabs (20–120 times more viscous than the upper mantle) at the bending region, consistent with previous studies with a free-slip top boundary. The viscosity of the slab below the bending region barely affects the surface topography and gravity field, and both strong and weak slabs fit the observed topography and gravity field, suggesting that extra independent observations are needed to constrain the deep slab viscosity. We investigate the comprehensive relations between subduction interface viscosity, surface topography and gravity anomaly, and trench motion. Models with trench advance have significantly low topography and gravity above the volcanic arc, contradicting subduction zone observations. Together with present trench motion observations and previous studies, we support the idea that the trench retreats under normal single-slab subduction conditions.

Correlative atomic force microscopy and scanning electron microscopy of bacteria-diamond-metal nanocomposites

Research investigating the interface between biological organisms and nanomaterials nowadays requires multi-faceted microscopic methods to elucidate the interaction mechanisms and effects. Here we describe a novel approach and methodology correlating data from an atomic force microscope inside a scanning electron microscope (AFM-in-SEM). This approach is demonstrated on bacteria-diamond-metal nanocomposite samples relevant in current life science research. We describe a procedure for preparing such multi-component test samples containing E. coli bacteria and chitosan-coated hydrogenated nanodiamonds decorated with silver nanoparticles on a carbon-coated gold grid. Microscopic topography information (AFM) is combined with chemical, material, and morphological information (SEM using SE and BSE at varied acceleration voltages) from the same region of interest and processed to create 3D correlative probe-electron microscopy (CPEM) images. We also establish a novel 3D RGB color image algorithm for merging multiple SE/BSE data from SEM with the AFM surface topography data which provides additional information about microscopic interaction of the diamond-metal nanocomposite with bacteria, not achievable by individual analyses. The methodology of CPEM data interpretation is independently corroborated by further in-situ (EDS) and ex-situ (micro-Raman) chemical characterization as well as by force volume AFM analysis. We also discuss the broader applicability and benefits of the methodology for life science research.

In situ quantitative topographic measurement and corrosion behaviour of low carbon steel in chloride solutions

ABSTRACT This study applied a novel in situ spectral modulation interferometry (SMI) technique to quantitatively measure the spatiotemporal corrosion kinetics on the surface of low carbon steel. The 3D topography map showed an early formation of localised shallow pits on the surface subjected to a chloride-free solution. In contrast, the steel samples in chloride-enriched solution revealed early-age microcracks or intergranular defective sites. The quantitative analysis of the height profile data (acquired from SMI) verified the heterogeneity of the corrosion process of these samples susceptible to pitting corrosion and intergranular corrosion behaviour. The estimated volume loss followed a similar trend to the 3D surface topography data. Still, a distinct behaviour in the volume loss was observed when compared to the void volume obtained from the electrochemical data.

Linking calcite surface roughness resulting from dissolution to the saturation state of the bulk solution

It has long been supposed that mineral dissolution results in surface microtopography features that may be related to the weathering conditions. Detection of etch pits on weathered mineral surfaces is indicative of far-from-equilibrium conditions (Ω➔0), whereas their absence points towards close-to-equilibrium conditions (Ω➔1). However, surface microtopography characterizations limited solely to qualitative comparisons may conceal the potential of reconstructing intermediate saturation conditions. To investigate this prospect, we performed flow-through dissolution experiments at room temperature and atmospheric pCO2, on mechanically-polished {104} calcite surfaces reacting at different saturation states with respect to calcite (0 ≤ Ω ≤0.8), under alkaline conditions (pH = 7.9). Time-resolved topography data of the dissolved calcite surface were acquired ex situ using vertical scanning interferometry (VSI). Quantitative comparisons of the ensuing surface topography data relied on surface roughness characterizations, based on a combination of unidimensional descriptors (Ra) and spatial statistics metrics (power spectral density, or PSD, and semi-variogram). Time-resolved surface roughness analyses suggested a temporal stabilization of the calcite surfaces undergoing dissolution for all targeted saturation states, and that the steady-state surface roughness evaluations corresponding to different Ω values are statistically distinguishable, with larger roughness values corresponding to lower Ω values, according to a seemingly bijective relationship. We then investigated the atomic-scale mechanisms that might partially explain the empirical Ω-roughness relationship derived experimentally at the VSI-scale through kMC and Ising dissolution modeling of a Kossel crystal. We found that the Ising model successfully reproduced the Ω-roughness behavior observed experimentally, and we discussed the features that interpretative stochastic models need to satisfy to agree with the experimental findings. Overall, the present study suggests that, under the investigated conditions, the steady-state calcite surface roughness resulting from dissolution can be used as a proxy to back-estimate the saturation state of the fluid under which the reaction occurred for samples reacted under unknown conditions.

МОДЕЛЮВАННЯ ДИНАМІЧНИХ ПРОЦЕСІВ ДОВКІЛЛЯ ПІД ЧАС РУХУ СУДНА З ВИКОРИСТАННЯМ РІС ТЕХНОЛОГІЙ

Remote methods of Earth’s surface research have become widely used in the 21st century, as they allow for a larger and more comprehensive observation area. These methods can provide valuable information about various Earth objects and phenomena, such as the changes in bottom topography in shallow water under navigation conditions. This article presents a novel approach to forecasting these changes by using natural processes as indicators and developing programs that can track and display them on electronic devices. The article introduces the concept of “scale factor” to determine the significance of different dynamic processes for the research, depending on their spatial and temporal dimensions. The article also proposes a dynamic map modelling method that can predict the siltation of the sea/river bottom for a given period of time and improve the model by comparing the forecast with the actual result. The article suggests that research should take into account the changes in the object over time and under the influence of various factors in a dynamic way. Based on the research, we draw the following conclusions: 1. The “scale factor” should be applied in dynamic navigation map research and compilation using different-scale data of the water surface and bottom topography; 2. A dynamic component should be added to the information block of the navigation cartographic systems ECDIS and Inland ECDIS, enabling the navigator to see the position of the vessel, taking into account the wave height relative to the bottom in real time; 3. The methods of parallel bottom topography transferring rely only on the data of statistical observations using iterations. These methods usually work well on sandy and silty soils, where the relief has clear wave-like forms, as well as frequent external influences following the main general direction. Keywords: RIS, dynamic processes, "scale factor", "chart dynamic model", ECDIS.

Application of Computer Image Processing Technology in Visual Communication System

ABSTRACT This research paper aims to enhance the operation of visual communication systems by incorporating computer image processing technology. The study investigates the potential applications of visual communication systems by utilizing imaging equipment to capture distorted fringe images of height-modulated objects from different angles. Phase information closely associated with the object is then extracted from the images and utilized to obtain the object’s three-dimensional surface topography following calibration. The paper applies multi-frequency phase-shift profilometry and Fourier transforms profilometry to construct further a visual communication system based on computer graphics processing technology. The article contributes to visual communication systems by proposing a novel approach that incorporates computer image processing technology. This approach enhances the performance of visual communication systems, resulting in a more effective means of communication. The study’s experimental analysis demonstrates computer image processing technology’s superiority and ability to enhance the visual communication system’s application. The study’s key findings indicate that computer image processing technology can effectively enhance the effectiveness of visual communication systems. The incorporation of imaging equipment and phase extraction methods leads to the production of accurate three-dimensional surface topography data. Furthermore, multi-frequency phase-shift profilometry and Fourier transform profilometry aid in constructing a visual communication system based on computer graphics processing technology, which leads to an overall enhancement of the visual communication system’s application.

Characterization and filtering of profile and areal surface topography by combining the discrete Legendre and cosine transforms

In the standardized processing of surface topography data, the form removal and filtering operations are clearly separated. This is reflected in the current ISO standards concerning profile surface texture and areal surface texture, the ISO 21920 and the ISO 25178 series respectively. When the scale-limited surface texture is significant compared to the form to be removed, for example with additive manufactured surfaces, the dependence of the surface Fourier spectrum on the removed form and orientation may become significant. This may lead to interaction between the form removal and filtering operations. To counter this interaction, in this paper, the lower-order discrete Legendre polynomials that describe the form are combined with cosine functions that describe the surface texture. This set of base functions is orthonormalized using a Gram-Schmidt procedure. This results in a set of orthonormal functions that allow an independent parameterization of both form and texture. The concept and the related theory are given and illustrated using examples of filtering profiles and areal topographies, description of cylinders and treatment of missing data. The examples show that the concept as presented in this paper is useful for filtering surfaces with a dominant form and can be used in the parametrization of surfaces and cylindrical geometries. Also, the methods presented here can be used for filtering and parametrization in the case of missing points in the data, actual holes in the profile or non-rectangular surfaces.

Experimental Investigation on Al 6061 and Al 2024 Aluminium Alloys for Achieving Surface Roughness Using CNC Vertical Face Milling Process

The influence of process factors and the integrity of the surface roughness are investigated experimentally in the current study on the metals Al6061 and Al2024. In order to conduct vertical cutting tests for both base alloys, a mixed-level L9 orthogonal array is used. An optical emission spectrometer is used to analyze the chemical composition. SEM is used to analyse the microstructure of the materials used to discover tiny inertinitic precipitates in an aluminum matrix using ANOVA Table value. Mild steel was the tool employed, and surface topography and 2D roughness profile data were used to assess the surface integrity of cut surfaces. The experimental trials with various degrees of process parameters, such as Taguchi L9 orthogonal array. In determining surface roughness, speed, feed, and depth of cut are determined to be the most important variables.

Sputtering Behavior of Rough, Polycrystalline Mercury Analogs

The solar wind continuously impacts on rocky bodies in space, eroding their surface, thereby contributing significantly to the exosphere formations. The BepiColombo mission to Mercury will investigate the Hermean exosphere, which makes an understanding of the precise formation processes crucial for evaluation of the acquired data. We therefore developed an experimental setup with two microbalances that allows us to compare the sputter behavior of deposited thin solid layers with that of real mineral samples in the form of pressed powder. In addition, this technique is used to study the angular distribution of the sputtered particles. Using 4 keV He+ and 2 keV Ar+ ions, the sputter behavior of pellets of the minerals enstatite (MgSiO3) and wollastonite (CaSiO3) is studied, because these minerals represent analogs for the surface of the planet Mercury or the Moon. Pellets of powdered enstatite show significantly lower sputter yields than thin amorphous enstatite films prepared by pulsed laser deposition. 3D simulations of sputtering based on surface topography data from atomic force microscopy show that the observed reduction can be explained by the much rougher pellet surface alone. We therefore conclude that sputter yields from amorphous thin films can be applied to surfaces of celestial bodies exposed to ion irradiation, provided the effects of surface roughness, as encountered in realistic materials in space, are adequately accounted for. This also implies that taking surface roughness into account is important for modeling of the interaction of the solar wind with the surface of Mercury.

Elastohydrodynamic Simulation of Pneumatic Sealing Friction Considering 3D Surface Topography

AbstractThis contribution presents an elastohydrodynamic lubrication (EHL) model for pneumatic spool valves. For an accurate estimation of the transient friction of this tribological sealing system, the surface topography of the cylindrical sealing counterfaces of the valve housings are measured and analyzed with an optical surface measurement instrument. Based on the surface topography data, tribological properties and flow factors of the system are derived. It has been found that the consideration of the surface topography has a significant influence on the simulation results of the EHL model, lowering the calculated friction force by up to 20 %.

Three-Dimensional Morphology and Watershed-Algorithm-Based Method for Pitting Corrosion Evaluation

Pitting corrosion and stress concentration at rust pits are the principal reasons for severe degradation in fatigue performance of corroded steel structures. The accurate evaluation of rust pits on rough and uneven corrosion surfaces is the foundation of fatigue life estimation for corroded steel structures. In this paper, a new method for the identification, extraction, and evaluation of rust pits on the surface of corroded steel structures was proposed based on a three-dimensional morphology and watershed algorithm. An accelerated corrosion experiment was first executed to acquire corroded steel plates, and then surface profile measurements were conducted to obtain the three-dimensional morphology of the corroded steel surfaces. Furthermore, the surface topography data of the corroded steel surfaces were written into a gray matrix through coordinate transformation. Then, the gray matrix was successively filtered and gradient-mapped, and the watershed was calculated to obtain the pit mark matrix and pit depth matrix. A calculation method for the size and shape of rust pits was consequently developed, and a statistical analysis of the extraction results of the rust pits was also conducted. The results showed that rust pit density had a peak value at the corrosion duration of 3 months, and rust pit density showed a fluctuating process with corrosion duration that continued to increase until 15 months. The values of the depth diameter ratios of rust pits were concentrated in the range of 0.1~0.8. With corrosion duration increasing from 3 months to 4, 6, 8, 12, and 15 months, the distribution range of the depth diameter ratios of rust pits decreased at first and then increased, followed by decrease and, finally, increase. The width distribution of the rust pits was independent of the depth distribution of the rust pits. The values of the volume ratios were mostly distributed between π/12 and π/4, and the shapes of most rust pits were similar to half (ellipsoidal) spheres.

Deriving Data-Driven Models That Relate Deterministic Surface Topography Parameters of As-Built Inconel 718 Surfaces to Laser Powder Bed Fusion Process Parameters

Abstract Laser powder bed fusion (L-PBF) parts often require post-processing prior to use in engineering applications to improve mechanical properties and modify the as-built surface topography. The ability to tune the L-PBF process parameters to obtain designer as-built surface topography could reduce the need for post-processing. However, the relationship between the as-built surface topography and the L-PBF process parameters is currently not well-understood. In this paper, we derive data-driven models from surface topography data and L-PBF process parameters using machine learning (ML) algorithms. The prediction accuracy of the data-driven models derived from ML algorithms exceeds that of the multivariate regression benchmark because the latter does not always capture the complex relationship between the as-built surface topography parameters and the corresponding L-PBF process parameters in a single best-fit equation. Data-driven models based on decision tree (interpretable) and artificial neural network (non-interpretable) algorithms display the highest prediction accuracy. We also show experimental evidence that thermocapillary convection and melt track overlap are important drivers of the formation of as-built surface topography.