Surface Texture Characteristics Research Articles

The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

Effect of the Building Orientation on Additively Manufactured Copper Alloy: Hydraulic Performance of Different Surface Roughness Channels

The additive manufacturing capabilities open new frontiers in the design of complex geometries in many different application fields, especially thermal science, in which multi-functional, efficient, compact components with internal cooling or heating channels are becoming more and more requested. Among the possible additive manufacturing technologies, the laser powder bed fusion process has recently been proven to manufacture high-conductivity metals, with good mechanical and thermal properties (i.e. pure copper and copper alloys), attracting the attention of the heat transfer community. However, depending on the material, design, and process parameters, the surface roughness of the components can remarkably change and become a critical issue in cooling applications. Currently, the surface texture characteristics and the final channel size cannot be accurately estimated a-priori, and thus the initial hypothesis of the current design methods may lead to unpredictable and undesired results. This work experimentally and numerically explores the effects of the building orientation on the fluid dynamic behaviour in CuCrZr alloy channels, which is considered one of the most promising additive manufacturing materials for thermal applications. By coupling the experimental hydraulic results with surface characterization and X-ray computer tomography dimensional measurements of the channels, a novel simplified methodology to properly correlate the pressure drop to the surface texture of a single wall is proposed and numerically validated.

Electro-mechanical analysis of nanostructured polymer matrix composite materials for 3D printing using machine learning

Recently, additive manufacturing (AM) techniques like 3D printing have emerged as a potentially game-changing example of digital manufacturing. However, high entry barriers of a tiny material library, different processing defects, and unpredictable product quality are still holding back its widespread use in the industry. Due to its remarkable success in data tasks like classification, regression, and clustering, machine learning (ML) has recently gained a great deal of interest in the subject of the material library. This paper examines the current state of ML applications in several key areas of AM, including polymer matrix composite materials and machine parameter optimization. Composite filaments have been extruded using Polylactic Acid (PLA) as it is a biodegradable material and shows how High-Density Poly Ethylene (HDPE) enhances physical strength. All the parameters for the filament extruder have been designed by machine learning. Thermal stability is a significant concern for polymers that have been overcome by introducing Titanium Dioxide nanoparticles. The microstructure, surface texture, electro-mechanical behavior, and other general features of extruded filaments made from recycled plastics have been investigated. The extrusion temperature, approximated using ML, is in excellent agreement with the surface texture and microstructure of the polymers, as confirmed by FESEM, EDX, and Particle analysis. Extruded filaments experienced 2500 Vs and confirmed their non-conductivity up to 77.7GΩ. Tensile strength and elongation at break, two measures of mechanical properties, have been examined. Incorporation of Titanium Dioxide Nanoparticles improved mechanical properties significantly. When it comes to 3D printing, the physical properties and potential uses of each composite material are different.

Analysis of Road Surface Texture for Asphalt Pavement Adhesion Assessment Using 3D Laser Technology

Pavement adhesion plays a crucial role in driving safety, while traditional test methods exhibit some limitations. To improve the efficiency and accuracy of asphalt pavement texture characterization and adhesion assessments, this paper uses three-dimensional (3D) laser technology to detect the continuous point cloud data of road surface and reconstruct the 3D topography of pavement texture. On this basis, a volume parameter Volume of peak materials (Vmp) is innovatively proposed to comprehensively characterize the 3D spatial characteristics of road surface texture. The correlation analysis between the proposed Vmp and the traditional adhesion evaluation index Transversal Adhesion Coefficient (CAT) is conducted, and then refined graded adhesion prediction models based on the proposed Vmp are proposed. Results show that the proposed volume parameter Vmp can reliably and accurately characterize the asphalt pavement texture by considering more structural properties of the road surface texture. According to the research findings of this paper, it is feasible to achieve rapid and correct assessment of asphalt pavement adhesion using 3D laser detection technology by comprehensively considering the 3D characteristics of the road surface texture.

Pharmacognostic Study and Standardization of Leaves and Stem of Mentha spicata Linn.

Background:: Various Mentha species are cultivated all over the world due to their aroma and valuable therapeutic constituents. Along with its traditional use as a food flavor, it is well-established folklore medicine for treating digestive and cardiovascular problems. Mentha spicata L. is a perennial, aromatic herb belonging to the Lamiaceae family. M. spicata leaves have been used in traditional systems of medicines for treating various digestive system disorders since old times. Aim:: The present research work encompasses the pharmacognostic study, phytochemical screening and physicochemical parameter estimation of leaves and stem of M. spicata L. for standardization of Mentha plant. Materials and Methods:: Morpho-anatomical characterization, physicochemical analysis and preliminary phytochemical investigation of leaves and stems were performed. These investigations were done as per WHO guidelines to determine the main diagnostic characteristics for correct identification and standardization of whole plant and powdered drug of M. spicata L. Results:: The dimensions, size, color, and surface texture characteristics of fresh crude drug (leaf and stem) were determined. Compound microscope images of the transverse section (TS) of M. spicata leaf and stem revealed the cellular arrangement and main identification characters. Phytochemical and physicochemical analysis of powdered drugs confirmed the presence of important phytoconstituents such as phytosterol, terpeneoids, phenolic compounds, saponins, volatile oil, etc. Conclusion:: The results obtained are helpful for the characterization of the crude drug. Morphoanatomical characterization and physicochemical analysis are inexpensive methods and serve as a quality control tool for stem and leaf standardization. The ethanolic and aqueous extract of leaves and stem showed the presence of valuable medicinally active phytoconstituents.

An investigation of the effect of inducing molecular alignment on the lubrication performance of 5CB liquid crystal

The lubrication alignment behaviors of 4-Cyano-4′-pentylbiphenyl (5CB) liquid crystal were investigated. A 60 s-long running-in pre-treatment was first performed using micro-sized diamonds. The ball was then deflected by a certain direction and an alignment agent (Sebacic Acid) was applied on the disc. Results show that the coefficients of friction for GCr15 and Si3N4 self-matching pairs were reduced to 0.036 and 0.017 respectively, 45.5 % and 67.3 % lower than the ones lubricated directly with 5CB. The surface morphology after running-in-deflection was smoother and flatter, with enhanced surface texture characteristics. It is believed that increased the order parameter of 5CB, induced by the synergistic effect of the alignment agent and running-in-deflection, is the reasons for the improvement in lubricating performance.

Activated Carbon Anchoring Site Enrichment Through B and N Codoping for Boosting Bi2Mo2.5(S,O)10 Oxysulfide Catalyst Stability and Visible‐Light‐Driven Hydrogen Evolution

AbstractBoron and nitrogen can enter the carbon lattice and provide structural disorder, porous structure, and active sites for better catalyst dispersions and activity. Herein, Bi2Mo2.5(S,O)10 oxysulfide (BiMoOS) anchored on unmodified and surface‐modified activated carbon (AC) via B and N doping is synthesized for efficient PHER, aiming that B and N doping into carbon framework can modify the surface textural features which act as anchoring sites for the host Bi2Mo2.5(S,O)10 and boost its photocatalytic activity by increasing specific surface area via preventing aggregation through a uniform distribution. Thus, the BiMoOS@B─N─AC catalyst achieved excellent stability and PHER performance (564.2 µmol h−1 H2 under visible light). The doped B and N in AC create structural disorder/defects, active sites and induce electron delocalization in B─N─AC, used as the anchoring sites for BiMoOS catalysts and stimulate the adsorption and activation kinetics of the H2O molecules, and also provide a highly conductive network that enhances charge transport and stability of BiMoOS@B─N─AC. With the advantages of the modified B─N─AC, the oxygen vacancy‐anchored BiMoOS exhibited superb PHER. Hence, B and N co‐doping into the carbon lattice is a promising approach to enriching the anchoring site for boosting metallic nanocatalyst stability and catalytic performance.

Prediction of Pavement Surface Friction Based on Pavement Surface Texture Characteristics Using Three-Dimensionally Printed Model Specimen

Prediction of Pavement Surface Friction Based on Pavement Surface Texture Characteristics Using Three-Dimensionally Printed Model Specimen

Numerical simulation of the lubrication characteristics of bionic surface textures on mechanical sealing rings

Inspired by the shark scale surface in natural world, five different bionic surface textures were designed. Through numerical simulation, the influence mechanism of the geometrical parameters on the lubrication characteristics of the bionic surface textures on the mechanical sealing rings under refrigerant conditions was investigated. The results revealed that the lubrication characteristics of the bionic surface textures is jointly influenced by the texture shape, arrangement, vortex phenomenon, and hydrodynamic pressure effect. The lubrication characteristics of all five bionic surfaces designed in this study was superior to those of the smooth surface. The effects of density and texture depth on the lubrication characteristics of the surface textures exhibits criticality. The results provide valuable insights into the effective design of bionic surface textures on mechanical seal rings, especially in improving their lubrication characteristics under refrigerant conditions. The effectiveness of numerical simulation techniques in evaluating and optimizing the performance of bionic texture design in various micro- and nanofabrication processes is further validated.

Accurate superposition modeling method of grinding-shot peening surfaces

Abstract The coexistence of initial surface texture features and shot peening texture features in the final surface topography is a significant feature of shot peening. After the initial surface topography is strengthened by shot peening, the wave peak will produce more substantial deformation relative to the wave valley, which has not been considered in the existing superposition modeling methods. And this will affect the correlation study between surface topography and performance. Taking the surface generated by the grinding-shot peening process as the research object, based on the feature extraction of measured topography, the concept of ‘Shot peening texture depth distribution surface’ is put forward. And the proposed precise superposition method uses the texture depth distribution surface to optimize the characteristic baseline of shot peening based on the existing theory. The measured grinding-shot peening surfaces verify the given method. It is found that the topographical and spatial features of the surfaces superimposed by this method are consistent with those of the measured surfaces, and the error of roughness parameters defined by ISO25178 are all less than 8%. Among them, the percentage errors of the critical parameters Spd, Sdq, and Sdr related to friction and lubrication are significantly reduced to 5%.

Multi-label oxide classification in float-zone silicon crystal growth using transfer learning and asymmetric loss

AbstractFloat-Zone (FZ) crystal growth process allows for producing higher purity silicon crystal with much lower concentrations of impurities, in particular low oxygen content. Nevertheless, the FZ process occasionally faces the problem of small contamination from oxidation. This can come in the form of a thin oxide layer that may form on un-melted polysilicon surface. The appearance of the oxide layer indicates degraded machine performance and the need for machine maintenance. Therefore, oxide investigation is important for improving both the FZ process and FZ machines, and the first step is oxide recognition. In this study, we characterized oxide into mainly three varieties, according to their surface texture characteristics, which are: (i) spot (ii) shadow and (iii) ghost curtain. We leveraged FZ images captured from the vision system integrated on the FZ machine to establish an oxide dataset. Targeted for data imbalance problem in our dataset, a method based on transfer learning and asymmetric loss for multi-label oxide classification is presented in this work. The results showed that the pre-trained model and the asymmetric loss used for training outperformed the baseline models and improved the classification performance. Furthermore, this study deeply investigated the effectiveness of the components of asymmetric loss. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to explain decision process of the models in order to adopt them in the industry.

Green Space Reverse Pixel Shuffle Network: Urban Green Space Segmentation Using Reverse Pixel Shuffle for Down-Sampling from High-Resolution Remote Sensing Images

Urban green spaces (UGS) play a crucial role in the urban environmental system by aiding in mitigating the urban heat island effect, promoting sustainable urban development, and ensuring the physical and mental well-being of residents. The utilization of remote sensing imagery enables the real-time surveying and mapping of UGS. By analyzing the spatial distribution and spectral information of a UGS, it can be found that the UGS constitutes a kind of low-rank feature. Thus, the accuracy of the UGS segmentation model is not heavily dependent on the depth of neural networks. On the contrary, emphasizing the preservation of more surface texture features and color information contributes significantly to enhancing the model’s segmentation accuracy. In this paper, we proposed a UGS segmentation model, which was specifically designed according to the unique characteristics of a UGS, named the Green Space Reverse Pixel Shuffle Network (GSRPnet). GSRPnet is a straightforward but effective model, which uses an improved RPS-ResNet as the feature extraction backbone network to enhance its ability to extract UGS features. Experiments conducted on GaoFen-2 remote sensing imagery and the Wuhan Dense Labeling Dataset (WHDLD) demonstrate that, in comparison with other methods, GSRPnet achieves superior results in terms of precision, F1-score, intersection over union, and overall accuracy. It demonstrates smoother edge performance in UGS border regions and excels at identifying discrete small-scale UGS. Meanwhile, the ablation experiments validated the correctness of the hypotheses and methods we proposed in this paper. Additionally, GSRPnet’s parameters are merely 17.999 M, and this effectively demonstrates that the improvement in accuracy of GSRPnet is not only determined by an increase in model parameters.

On the Assessment of Strawberries' Shelf-Life and Quality, Based on Image Analysis, Physicochemical Methods, and Chemometrics.

The aim of the present study was to evaluate Marisol strawberries' (Fragaria × ananassa) physicochemical quality and shelf-life during storage, using an integrated analytical approach. More specifically, the research aimed to assess the strawberries' color, texture, and nutritional quality, over an 11-day storage period, employing physicochemical analyses, spectrophotometric assays, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy, image analysis, and statistical tools. The results revealed significant changes in the outer surface texture and color characteristics, indicating spoilage progression. Physicochemical parameters such as water activity, moisture content, soluble solids, titratable acidity, and ascorbic acid content exhibited significant alterations, influencing the taste profile and freshness perception. Antioxidant and antiradical activities showed fluctuations, suggesting a potential decrease in phenolic content during storage. Moreover, the ATR-FTIR spectra findings confirmed the results regarding the moisture content, soluble solids, and total phenolic content. The integration of physicochemical and image analysis-derived features through a principal component analysis (PCA) enabled the accurate classification of samples based on storage days. Regression analysis, using these features, successfully predicted the storage day with high accuracy. Overall, this integrated analytical approach provided valuable information on the estimation of Marisol strawberries' shelf-life and the prediction of their quality deterioration, contributing to better fruit management and the minimization of discards.

Testing and evaluation for skid resistance of steel slag asphalt wearing course based on surface texture characteristics

Steel slag can be utilized in asphalt wearing course as a substitute for aggregates. The surface texture of pavement is the critical factor affecting the friction behaviors and skid resistance. However, further research is needed to analyze the relationship between the surface texture and the skid-resistance performance of steel slag wearing course. In this study, four kinds of asphalt wearing course with different steel slag dosage (0%, 25%, 50% and 75% by volume) and two kinds of gradation (SMA-10, OGFC-10) were designed. Skid resistance of different specimen under different abrasion time was tested by sand patch test and British pendulum test. Laser scanning technology was used to capture and analyze the surface texture characteristics. The results showed that replacing basalt with steel slag could increase the skid resistance and anti-abrasion performance significantly and asphalt wearing course with 50% steel slag has the best skid resistance. The correlation coefficients between skid resistance and surface texture indexes of the specimens were higher than 0.9 before and after abrasion. Ra is more suitable than texture depth (TD) to represent the surface texture characteristics of the specimen and predict anti-skid properties of pavements.

Analysis of influence factors of target polarization characteristics

The target polarization characteristics reflect the important information about the medium material, surface texture, and structural characteristics of the target itself. In this paper, 9 kinds of samples with different materials, roughness, and surface texture direction are selected to measure the polarization characteristics in an outdoor environment, and the influencing factors of the target polarization characteristics are analyzed, where the influence of the surface texture direction on the spatial distribution of the target polarization characteristics is emphatically analyzed. The results show that the target polarization characteristics are affected by many factors such as material, roughness, surface texture direction, and detection position, and the greater the angle between the surface texture direction and the principal plane, the more concentrated the target polarization characteristics are in and around the principal plane, which could provide a theoretical basis for comprehensively mastering the target polarization characteristics, improving the ability of target polarization detection and recognition, and enhancing the confidence of the target polarization modeling and simulation.

Streamline video-based automatic fabric pattern recognition using Bayesian-optimized convolutional neural network

Examining fabric weave patterns (FWPs) is connected to image-based surface texture feature (STF) acquisition, which can be difficult due to the structural complexity of woven fabrics. Randomly capturing static images may not correlate with the entire STF of a fabric. Traditionally, FWPs analysis is conducted by human vision, which causes an intensive cognitive load. Ultimately, the human vision-based cognitive load leads to ineffective quality inspection and error-prone FWPs analysis results. Given the above challenges, this study proposes a new streamlined video-based FWPs recognition method by incorporating Bayesian-optimized convolutional neural network (Bayes Opt-CNN). Essentially, this method is capable of leveraging the spatiotemporal features of the fabric’s intricate surface structure. In this study, to validate the effectiveness of the proposed method, seven types of fabric structures were captured as streamline videos, which were then converted into sequences of image frames. Subsequently, the Bayesian optimization process was introduced to select the best hyperparameters during CNN-based supervised learning for pattern recognition. The evaluation demonstrates that the proposed method outperforms the benchmark method for identifying FWPs.

Crater array generated on Al alloy surface by nanosecond laser: Surface characteristics and bonding strength

The strength of aluminium (Al) alloy adhesive joints is critical for aerospace and automotive applications. This work uses an infrared nanosecond laser to create a crater array (CA) and thereby modify the substrate bonding area and enhance the strength of 7075-T6 Al alloy joints bonded by epoxy resin adhesives. We study how the parametrisation (crater overlap ratio β and pulse energy density Ф) of the CA affects the surface texture features (surface roughness Sa and fractional increase Sdr in surface area), wettability, chemical properties, and shear strength τ. The mechanism by which CA affects τ is revealed by the surface characteristics and failure modes. Sandblasted specimens were used as a reference. The results show that, for laser treatment, Sdr, Sa, and τ are closely related to β but not to Ф. When β = 30 % or 60 %, the CA surface is superhydrophilic. Regardless of β and Ф, the substrate absorbs carbon-containing pollutants from the air, so that the contact angle of the CA surface gradually increases over time until, 15 days after the laser treatment, the CA surface becomes hydrophobic. Zn, Cu, and Fe are uniformly distributed over the alloy surface, whereas C, O, Mg, and Al are unevenly distributed. The C content decreases with increasing β but is not correlated with Φ. The O content increases with the increasing β and Φ. When β = 30 %, the area of cohesive failure in the substrate bonding area is maximal. When β ≤ 0, the untreated area outside the craters is susceptible to adhesive failure. When β ≥ 60 %, the substrate bonding area is covered by a thin layer formed by molten residue, which is susceptible to substrate failure. Laser treatment is more effective than sandblasting in modifying the texture, wettability, and chemical properties of the bonding area, which can further improve the bonding strength. The maximum strength of the bonded joint obtained by laser treatment is 26.89 MPa at Φ = 21.6 J/cm2 and β = 30 %, which is 16.2 % stronger than sandblasting.

BRDF representation in response to the build orientation in 3D-printed digital materials

Additively manufactured (AM) parts still lack a thorough understanding of their optical properties, particularly surface texture and reflectance characteristics at different viewing angles. This study examines the reflectance properties of material jetting (MJT) parts using bidirectional reflectance distribution functions (BRDFs). The visual appearance of the MJT parts was analyzed using a gonio-spectrophotometer at 328 unique incidence and viewing geometries for seven different wedge angles for build orientation (BO) from 0° to 90° at 15° intervals. The redundancy analysis (RDA) and principal component analysis (PCA) were used to study BO and to determine the prominent measurement geometries. The results indicate higher BOs resulted in more color and texture variation and rougher surfaces, with Sq 4.21 μm vertical compared to 1.42 μm for horizontal BOs. Furthermore, it affected the visual representation and parametric estimation of BRDF, where significantly lower luminance, more diffuse reflection, and less hue distribution were observed for all CMYK resins printed at higher BOs. Accordingly, vertically printed surfaces showed a wider near-to-specular luminance area than other BOs. An analysis of the bidirectional reflectance property suggests that a gonio-spectrophotometer can be embedded in the printing process and quality assurance in AM as a computationally efficient model.

A novel method for measuring and analyzing the characteristics of coarse aggregates in concrete

The characteristic of coarse aggregate is an important factor affecting the building performance of concrete, so it is of great significance to measure and characterize it. In this paper, a method of measuring the characteristics of coarse aggregate in concrete buildings by using borehole camera technology is proposed. For the obtained borehole wall image, the corresponding image processing method is proposed, which can accurately recognize the coarse aggregate structure in the image. On this basis, the characteristics of coarse aggregate in the borehole wall image are calculated and analyzed. The calculation parameters include uniformity, gradation, aspect ratio, inclination angle, shape index, angularity and surface texture. The results show that the coarse aggregate in the borehole wall image used in this paper is not evenly distributed at different depths, the gradation continuity is good, and there are some coarse aggregates with large particle size. The aspect ratio of most coarse aggregates is less than 3 (AR1:97.71%; AR2:96.26%), and the content of needle flake coarse aggregate is less. The inclination angle is mostly distributed in the horizontal direction, accounting for more than 50%. The distribution of shape index and angularity has obvious correlation with the particle size range, and the shape index and angularity in the large particle size range are richer and more uniform. The distribution characteristics of surface texture of coarse aggregate in different particle size range are basically the same, and there is no obvious correlation with particle size. In this paper, a new in-situ measurement and multi characteristic parameter analysis of coarse aggregate in borehole is carried out, which provides a new idea for the evaluation of concrete building performance.

Visual measurement of grinding surface roughness based on feature fusion

Due to the random surface texture and weak features of the grinding processing, most current visual measurement of grinding surface roughness is predicted by designing feature indicators, but its imaging environment is more demanding and the indicator design is more artificial. While deep learning can achieve feature self-extraction, the existing deep learning-based feature extraction uses a single convolutional model to extract features, which tends to make the extracted features noise, with low resolution and poor perception of details. To address the above problems, this paper proposes a feature fusion-based method for measuring grinding surface roughness. The method adopts different feature extraction ways and fuses the extracted features to obtain more features while also improving the generalization ability of the model, and verifies the adaptability of the model in different lighting environments. The experimental results show that deep learning self-extracted features based on feature fusion can effectively solve the problem of weak feature information on grinding surface roughness that is difficult to identify, and the model has high detection accuracy across different lighting environments, thus laying the foundation for the automated visual online measurement of grinding surface roughness.