Surface Texture Research Articles

Effect of temperature and capillary number on wettability and contact angle hysteresis of various materials. Modeling taking into account porosity

The effect of surface temperature and laser texturing on the wettability and contact angle hysteresis of Cu, Cu/G, AlMg3, Cu-SiC was considered. Modeling using molecular dynamics qualitatively confirms the experimental results: 1) an increase in the contact angle hysteresis on a textured surface; 2) influence of the surface wettability on contact angle hysteresis; 3) influence of the contact line velocity on contact angle hysteresis. Heating the sample of different composite materials can lead to either an increase in the contact angle or a decrease. It was shown that after laser texturing, a system of nano-micro pores was formed in the surface layer of the material, which leads to an abnormally high contact angle hysteresis on the Cu-SiC composite. A new model is proposed that explains the different qualitative behavior of the contact angle hysteresis in different studies with increasing capillary number (Ca), as well as with increasing sample temperature.

Surface engineering of additively manufactured Ti-6Al-4V alloys: A comparative study on micro/nanoscale topographies for biomedical applications

This study investigates the generating micro/nanoscale topographies surface texture and comparison of their bioactivity performance on Ti-6Al-4V alloys fabricated using DMLS and EBM methods for potential biomaterial applications. To achieve the desired micro/nanoscale roughness, the EBM and DMLS samples underwent a sequence of sandblasting (S), acid-etching (E) and anodization (A), resulting in SEA-treated surfaces. The modified alloys were then analysed and characterized in terms of their microstructure, surface roughness, topography, and wettability. Furthermore, cell culture experiments were performed in vitro to assess the bioactivity of these surfaces. SEM images showed that both EBM and DMLS samples exhibited micro/nanoscale morphology and topography. After surface modification, the Ra surface roughness values were calculated to be approximately 0.86 μm for the EBM sample and 0.76 μm for the DMLS sample. The contact angle measurements indicated that the EBM sample had a contact angle of 37.55º, while the DMLS sample had a contact angle of 19.24º after undergoing SEA treatment. HOB cell viability on the SEA-treated surfaces was found to be 97.03 % for the DMLS sample and 93.80 % for the EBM sample after 72 h. Throughout the incubation period, the HOB cells were observed to attach and spread out on the surfaces of both the DMLS and EBM samples.

Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg

This study introduces a Hybrid Intelligence approach to investigate the Process-Structure-Property-Performance (PSSP) relationship in additively manufactured (AM) materials, specifically focusing on V-notched laser powder bed fused (L-PBF) AlSi10Mg specimens. The Humen Intelligence (HI) component managed the design, manufacturing processes, post-processing, structural characterization, mechanical testing, and data collection. In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. This integration of HI and AI enabled a more thorough exploration of these relationships; data collected from our previous research were complemented with new experiments conducted to assess the effects of various heat treatments (HTs) and surface post-treatments (SPTs) on the fatigue behavior of the specimens. The techniques applied included stress relief (SR), T6 thermal treatments, sand blasting (SB), shot peening (SP), severe vibratory peening (SVP), laser shock peening (LSP), tumble finishing (TF), abrasive flow machining (AFM), chemical polishing (CP), electrochemical polishing (ECP), and chemical milling (CM), along with their combinations. A total of 54 different post-processing techniques were examined in this study. The experimental data, covering surface texture, microstructure, porosity, hardness, and residual stress, were used to develop an ML model that analyzed the fatigue behavior of the specimens. This approach represents a significant advancement toward integrated mechanistic and data-driven materials engineering, offering valuable insights for optimizing fatigue performance in practical applications.

Optimizing superelastic shape-memory alloy fibers for enhancing the pullout performance in engineered cementitious composites

Abstract This study explores the effect of integrated superelastic shape-memory alloy fibers (SMAFs) on the mechanical performance of engineered cementitious composites (ECCs). Various SMAF configurations – linear-shaped SMAFs (LS-SMAFs), hook-shaped SMAFs (HS-SMAFs), and indented-shaped SMAFs (IS-SMAFs) – with diameters of 0.8 and 1.0 mm were incorporated into ECC matrices, and surface texturization was achieved through abrasive paper treatment. Their mechanical properties were assessed through single fiber pullout tests on ECC mixtures containing 1.5 and 2.0% polyvinyl alcohol (PVA), subjected to both monotonic and cyclic loading conditions. Qualitative analysis, employing scanning electron microscopy, demonstrated that the IS-SMAF configuration provided superior mechanical interlocking and fiber–matrix adhesion, with a distinct flag shape observed during tensile testing. Quantitative data indicated that IS-SMAFs significantly improved the tensile strength and pullout resistance, with slip distances of ≥5 mm and average pullout loads ranging from 263 to 403 N. LS-SMAFs demonstrated better performance compared to HS-SMAFs and LS-SMAFs in terms of tensile and pullout characteristics. Additionally, ECCs with increased PVA content exhibited enhanced withdrawal performance. Thermogravimetry analysis and X-ray diffraction provided insights into the high-temperature stability and crystalline structure of the composites. These results underscore the effectiveness of IS-SMAFs in enhancing ECC properties, offering significant implications for the development and optimization of high-performance composite materials in civil engineering applications.

Single Level Fast Multipole Method for frictionless rough contact problems

A perfectly smooth contact surface does not exist in nature and industrial applications. Even a body, that seems perfectly smooth to the naked eye, will show surface roughness at a higher magnification. Due to the roughness of the surface, there are areas of contact and separation, which increases the complexity of the contact calculation. However, this computational complexity increases further due to the multi-scale nature of road surface texture and large contact area in the case of tyre/road interaction. To reduce the computational complexity of this contact problem, the Single Level Fast Multipole Method (SLFMM) is developed within this paper. The contact problem is based on Boussinesq’s contact theory and for the time being, the influence of friction and lateral displacement are neglected. To validate the accuracy and reduction in computational complexity, the SLFMM was applied to rough surfaces of different complexities and compared to a reference method, the so-called Matrix Inversion Method (MIM). Results indicate that the new method computes the pressure distribution and displacement accurately, with a global error of less than 1%. The advantage of the new method compared to the MIM is the multipole expansion, which clusters adjacent contact points to a single center point. As a result, the computational complexity of the contact calculation is reduced. Overall, the Single Level FMM computes the results faster than the reference method. These results demonstrate that the Fast Multipole Method meets the requirements of accuracy and accelerated computation for rough contact problems.

Influence of material orientation, loading angle, and single-shot repetition of laser shock peening on surface roughness properties

Titanium alloy Ti6Al4V is extensively utilized in biomedical applications due to its excellent biocompatibility, corrosion resistance, and mechanical properties. The design of dental implant surface textures has changed throughout time to address issues with oral rehabilitation in both healthy and damaged bones. The longevity of an implant is significantly impacted by surface roughness. This study examines the use of laser shock peening (LSP) as a surface modification technique to improve the mechanical properties of implants. A numerical model is developed using the commercial finite elements software in ABAQUS/Explicit for simulating dynamic conditions. The aim of the study is to develop surface roughness parameters using computational methods such as studies have not yet been contemplated. The single shot angle, shot repeat, time, material orientation, and laser power are applied for the first time simultaneously to evaluate the impact of material orientation and loading angles on surface roughness parameters. The study showed that the developed computational model’s compressive residual stress was −578.45 MPa, while the experimental samples were −592.18 MPa. Consequently, the difference between the computational and experimental results was 2.32%. Without regard to material orientation or angle, the compressive residual stress of the samples under examination was found to be −578.450 MPa after three repetitions and to decreased to −1.620 MPa after four. These results demonstrate that by varying the material orientation and loading angle, the Ra value may be increased four times.

Robust air pocket stability of various liquids droplet on micro cavity structures

Surface texturing is carried out to realize a robust liquid-repellent surface, that is, a surface with a high contact angle for various liquids. Surfaces with an array of micropillars, microcavities, or a hierarchical structure composed of micropillars and microcavities were fabricated, and their wetting behaviors were evaluated. The microstructural arrays were fabricated on a flexible polymer substrate using ultraviolet nanoimprint lithography and contact angles of liquids with different surface tensions were measured and compared. Furthermore, the long-term liquid repellency of the developed surfaces was evaluated by measuring static and dynamic contact angles. The results revealed that the microcavity structure is suitable for preventing the wetting of the surface with low-surface-tension liquids, such as hexane and silicone oil. Unlike the pillar array, the microcavity structure prevented the wetting of the surface with such liquids.

Effectiveness of cold atmospheric plasma in decontaminating enterococcus faecalis colonized collagen and PTFE membranes used in guided bone regeneration: a comparative in vitro investigation

PurposeWound healing disorders caused by bacterial infections in dental surgery, especially where membranes are used, are a common issue in oral surgery. Cold atmospheric plasma (CAP) offers a non-invasive solution for surface decontamination, including dental implants. The aim of this study was to evaluate the antibacterial effectiveness of CAP on various clinically applied membranes made of collagen and polytetrafluoroethylene (PTFE).Materials and methodsTo assess the antibacterial properties of CAP, enterococcus faecalis were seeded on different membranes: Memlock (collagen), Memlock Pliable (collagen), Agronaut (collagen), and PermaPro (PTFE); n = 4. After in vitro cultivation for 6 days, CAP using a kINPen® MED with an output of 5 W was applied 5 min and 10 min. Bacterial colony-forming units (CFU) were quantified to detect decontamination effectiveness. In addition, live and dead staining as well as scanning electron microscopy (SEM) of membranes was performed for validation and surface texture analysis.ResultsBacterial colonization was highest on collagen-based membranes (CFU Memlock: 14.38 ± 8.91). The results showed that CAP significantly reduced bacterial colonization on all membrane types after 10 min application of CAP; Memlock (CFU after 10 min 0.22 ± 0.16^106; p = 0.0256), Argonaut (CFU after 10 min 0.02 ± 0.01^106; p = 0.0129) and PermaPro (complete bacterial decontamination; p = 0.0058). This was paralleled by fluorescence and scanning electron microscopy. CAP was most effective on smooth membrane surfaces as SEM revealed.ConclusionCAP thus offers a non-invasive, cost-effective method to reduce bacterial infections in guided bone regeneration using membranes.Graphical

Pre-breeding highlights of intraspecific polymorphism and genetic estimates of seed traits of African yam bean (Sphenostylis stenocarpa Hochst Ex. A. Rich.) Harms breeding lines

Abstract Discriminatory morpho-metric features are obvious on legume seeds. This study utilized seven quantitative and 11 qualitative seed traits to characterize 139 African yam bean (AYB) breeding lines which were developed through single seed descent procedure. The seven quantitative data were subjected to analysis of variance, their means were combined with qualitative scores for genetic distance, principal component (PC) and clustering analyses. Significant (P ≤ 0.001) variation existed among the breeding lines for the seven traits. Mean ranges of seed length (SL), width (SW), thickness (ST) and a single seed weight (SSW) among the 139 breeding lines were respectively: 6.77–10.22 mm, 5.70–7.86 mm, 4.96–7.45 mm and 0.15–0.42 g. Positive and significant (P ≤ 0.05) genotypic correlation existed among SSW, SL, SW and ST. Seed colours, pattern, shapes, sizes, surface texture, brilliance varied among the breeding lines. Ranges of phenotypic and genotypic coefficient of variation and broadsense heritability were: 5.49–23.84%, 2.95–19.88% and 28.91–69.54% respectively. Fourteen (quantitative and qualitative) traits contributed higher (≥ 0.30) eigenvector loadings to the first three PC axes which explained 57.9% of the total variation among the breeding lines. Similarity among the lines was 0.75. Four clusters ensued in the dendrograph and each group had genetic similarities of: 0.85 (I), 0.82 (II), 0.78 (III) and 0.80 (IV). This research unveiled significant variation among AYB breeding lines with promising reliability for breeding opportunities of the qualitative and quantitative seed traits, which could contribute to higher grain yield and acceptability.

Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach

This paper investigates the capability of Laser Surface Textruing (LST) to induce texture on Ti-6Al-4V, aiming on optimizing process parameters viz. average power, pulse frequency, scanning speed, and gas pressure using the Driving Training-based Optimization (DTBO) algorithm. Both single and multi-objective optimizations are conducted to determine optimal parametric settings. The study systematically examines the impression of these LBM process parameters on various responses. Comparative analyses was performed with five other metaheuristic algorithms such as Ant colony optimization, Particle swarm optimization, Differential evolution, Firefly algorithm, Teaching-learning-based optimization, and Artificial bee colony. Furthermore, statistical validation via paired t-tests confirms the unique effectiveness of the DTBO algorithm. Detailed examination through developed box plots and convergence diagrams consistently demonstrates DTBO superior performance in terms of accuracy, minimal variability in optimal solutions, and reduced computational effort. The DTBO achieves a higher MRR by 35.7 %, 20 %, 11.9 %, 54.7 %, and 33.3 % compared to ABC, ACO, FA, DE, and TLBO, respectively. Simultaneously, DTBO also achieves a lower ATW by 13.6 %, 14.8 %, 3.02 %, 15.9 %, and 16.1 % compared to the same algorithms. These results underscore DTBO's superior performance in achieving improved MRR values and reduced ATW values across the considered optimization algorithms. Hence, The DTBO algorithm demonstrates robustness and applicability in optimizing LBM processes in context of laser texturing, which may enhance manufacturing efficiency and product quality significantly.

Image processing and artificial neural network based determination of surface mean texture depth on lab-controlled chip seal pavement samples

Because surface texture is nearly the sole indicator of pavement functional properties and highly correlated with critical operational characteristics of roadways like traffic noise and safety, the change in pavement surface texture because of traffic loadings and environment has to be evaluated routinely. There are numerous direct or indirect evaluation techniques in the market. However, most of these methods have some limitations like requiring lane closure or being expensive. In this study, a 2D image processing method was established to estimate the surface mean texture depth (MTD) of chip sealed pavements. We produced chip sealed pavement samples in the laboratory with different aggregate type, shape, and size ranging between 2 and 19 mm to cover wide range of live conditions. Two well-known conventional test methods, Sand Patch (SP) and Hydrotimer (HT), were used to determine MTDs of chip seal samples. Subsequently numerous photos were taken on surface of the samples with a camera for 2-D image processing that was done based on surface void ratio (SVR) approach. With the image processing, SVR of all samples were determined. At the point of whether there is a relationship or not, correlation analysis was made between the MTDs obtained with SP and HT and the data obtained by SVR approach with the artificial neural network method. The results show that the proposed SVR approach construed on 2D image processing method can be a reliable alternative to evaluate the surface texture of pavements.

Modeling and characterizing of surface texture produced by scraping process

Abstract Scraping technology is widely used in the guideways manufacturing of high-precision machine tools, yet the scraped surface texture formation mechanism is still not fully understood owing to the complexity and randomness of the scraping process, which limits the study of the tribological properties of scraped bond surfaces. To reveal the formation mechanism of the scraped surface texture, this study adopts the stratified theory to model and characterize scraped surfaces based on the characteristics of the scraping process. The research found tri-Gaussian stratification features in a scraped surface texture and establishes surface stratified model. Tri-Gaussian segmented separation and continuous separation methods are proposed to calculate the component surface parameters for characterizing the scraped surface texture. Segmented and continuous separation methods are respectively applied to analyze and reconstruct simulated tri-Gaussian stratified surfaces and experimental scraped surfaces. The results indicate that the continuous separation method is superior to the segmented separation method and leads to almost the same height and process parameters as the measured surface in the surface reconstruction. This verifies the feasibility and accuracy of the scraped surface texture model and characterization methods. This paper reveals the surface texture formation mechanism from the viewpoint of the scraping process and provides new insights for modeling and characterizing research on scraped surface texture.

A novel deep learning‐based technique for efficient characterization of engineered cementitious composites cracks for durability assessment

AbstractEngineered Cementitious Composites also known as Strain‐hardening cementitious composites (SHCCs) has unique cracking patterns like cracks that have tiny widths and showcase high density. All of this makes it difficult and laborious to compute crack parameters from crack patterns. Unfortunately, this is an essential part of assessing durability and micromechanical modeling. SHSnet is developed to perform end‐to‐end semantic segmentation of SHCC cracks. SHSnet is efficient, attention based deep encoder‐decoder network with large receptive field. Loss function based on Tversky function were used for training the model. SHSnet with loss function shows promising result with mPrecision, mF1Score and mIoU of 0.87, 0.84 and 0.83 respectively for complex SHCC cracks while requiring at least an order of fewer computational parameters than those in the literature. An image processing unit is then used to estimate the width, number, and length of the cracks from the segmentation mask. Test results show that the computed crack parameters with SHSnet are exactly the same as that computed with an optical microscope but require ~100× less time. Results demonstrate that SHSnet works equally well in SHCCs with different surface textures, crack density, and widths; the final result was far superior to a conventional technique. This technique also shows promising results in an automatic evaluation of crack parameters relevant to durability and visualizing crack patterns even in the presence of artifacts during progressive testing. The results also demonstrate the necessity to accurately and densely calculate crack length and maximum crack width; else the durability results are expected to be significantly more conservative than the actual value.

Study on the Surface Texture-Microparticles Synergistic Lubrication Mechanism of Artificial Joint

Abstract After total joint replacement surgery, frequent injection of lubricating substances is required due to lubrication failure caused by the absorption of lubricants by the human body, which will cause serious physiological and psychological burdens on patients. In this study, with the help of surface texture aggregation of particles, gelatin microgel particles with a diameter of about 4 μm were used as lubricating substances to achieve aggregation in the texture, thus improving the friction environment under low-concentration lubricants. The study found that the rectangular cross-sectional texture demonstrated the most effective anti-friction properties, resulting in a 26% reduction in friction coefficient compared to non-textured surfaces. Additionally, utilizing comsol for particle flow simulation, researchers observed the motion behavior of particles within the texture and clarified the mechanism of particle aggregation and the improvement of lubrication on the surface. This article confirms the beneficial effects of combining surface texture and particles on lubrication, thus providing valuable insight for improving lubrication in dilute particle solutions.

Correction to: Nanosecond multi‑passes laser surface texturing on AISI 301LN TRIP steel

Correction to: Nanosecond multi‑passes laser surface texturing on AISI 301LN TRIP steel

Generation mechanism and anti-friction effect evaluation of continuous micro-groove texture machined by ball-end milling process

Abstract The tool path in ball-end milling process is flexible and controllable considering the structure characteristics and moving way of the cutter. This paper aims to reveal the generation method of continuous surface micro-groove texture by using ball-end milling process and evaluate the corresponding anti-friction performance by combining simulation and friction experiments. Results indicate that continuous micro-grooved surface texture can be obtained by combining small feed per tooth and large radial cutting depth. Radial depth of cut and tool radius affect aspect ratio of micro-groove. The bearing capacity of the oil film shows an increasing and then decreasing trend with the increase of radial depth of cut and orientation angle. Radial depth of cut increased from 0.1 mm to 0.3 mm, resulting in a maximum 4.87% increase in the bearing capacity of oil film because of the enhanced wedge effect. However, further increase in radial depth of cut leads to increased vortex, weakening the wedge effect. The oil film bearing capacity is reduced so that the friction coefficient increases by a maximum of 510%. Moreover, as the orientation angle increases from 10° to 20°, the bearing capacity of the oil film increases by 22.7%. The minimum friction coefficient currently is 0.0215. However, when the micro-groove orientation angle is further increased, the countercurrent effect is enhanced and the dynamic pressure effect is weakened, which finally leads to a decrease in the bearing capacity of the oil film. The friction coefficient has increased by 30.2% maximum. This study is of great significance which provides a new method for guiding the friction reduction on the die surface.

Multi-scale texture filtering-based quantitative machining mechanism characterisation for ultra-precision machined surfaces

Ultra-precision machined surfaces with nanometric surface texture have realised widespread applications. However, it is still challenging to quantitatively characterise the complex generation mechanisms due to the coupling of the machining factors. To address this issue, this work developed a multi-scale texture filtering-based method for ultra-precision machined surfaces. First, experiments were conducted including diamond turning, diamond milling, and ultra-precision grinding. Next, power spectrum density was utilised to reveal the multi-scale characteristics of surface texture generation. Then, the discrete wavelet transform was proposed for multi-scale texture filtering. Finally, root mean square height Sq was evaluated on S-F surfaces according to ISO 25178-2 and the machining mechanisms were quantitatively characterised by the contribution percentages of each scale. The results demonstrated that surface texture generation was dominated by multi-scale machining mechanisms to manifest as cutting residual, tool mark, material effect, spindle vibration, and ductile-brittle transition. Surface texture characterisation quantitatively indicated that small-scale features (cutting residuals and material effects) are crucial factors at low material removal volume (MRV). Due to the increased MRV, there is a transition since the middle/large-scale features (tool marks, spindle vibration, and ductile-brittle transition) gradually play their important role. This work provides novel thoughts to quantify machining mechanisms by multi-scale surface texture metrology for ultra-precision machining.

Development of surface-texturized black silicon through metal-assisted chemical etching and its application in the thermophotovoltaic field: a review and recommendation

Abstract The Shockley-Queisser limit poses a significant challenge in solar technology research, limiting the theoretical efficiency to around 30%. Thermophotovoltaic (TPV) systems have emerged as a solution by incorporating a thermal absorber in traditional solar cell setups to achieve total efficiency beyond the limits. The efficiency of the overall system heavily depends on the performance and quality of the thermal absorber, which absorbs photons from the heat source and transfers them to the TPV cell. However, complex and expensive fabrication processes have hindered widespread adoption of TPV technology. The well-established metal assisted chemical etching (MACE) method could be the best choice to mitigate these as it is a cost-effective, scalable, and mass-production-friendly process, which is widely used for surface texturization, creating nanostructures like nanopores, pyramids, and nanowires. MACE
technique is also suitable for producing highly efficient silicon-based thermal absorbers with over 90% absorption rate, which can contribute to increased total conversion efficiency. However, it does not come without challenges such as maintaining control over the etch rate in order to achieve uniformity. This paper comprehensively reviews the utilization of MACE for fabricating silicon-based thermal absorbers in TPV systems with the range of effective wavelengths of 600 – 2000 nm which corresponds to the energy level of 0.55 – 1.85 eV. The advantages and challenges of MACE, along with characterization techniques, are extensively discussed. By providing valuable insights, this paper aims to support researchers interested in advancing TPV technology.

Investigation of Laser Surface Texturing and Injection Molding Parameters in Advanced High‐Strength Steel‐Polyamide Direct Joining

This study investigates the joining of dissimilar materials, specifically galvannealed advanced high‐strength steel (AHSS) and glass fiber‐reinforced polyamide 6 (PA 6) to achieve lightweight automotive. AHSS and PA 6 are widely used in the automobile industry; however, a direct joining of those two materials is difficult owing to their great difference in physical properties. Therefore, laser surface texturing (LST) and plastic injection molding are adopted to join AHSS and PA 6, which can create a strong mechanical interlock between AHSS and PA 6, resulting in an excellent joint strength compared to other joining techniques. The effects of LST parameters (groove depth and hatch distance) and injection molding temperatures on the joint strength are examined. The maximum AHSS‐PA 6 joint strength is measured at 72.3 MPa. Additionally, the microstructure and mechanical properties of recast, which develops over the original metal surface by melting and resolidifying, are analyzed. Compared to the base metal, the recasts exhibit higher hardness due to smaller grain sizes. PA 6 fills the laser‐textured grooves and spaces between the recasts, acting as a mechanical interlock that enhances joint strength under tensile shear testing.

Study on the skid resistance decay of submerged asphalt pavements based on texture parameters

It is well known that prolonged rainwater erosion can adversely affect the surface texture of asphalt pavements, leading to a rapid decline in their skid resistance. This study utilized a small-scale accelerated loading device, a high-precision 3D scanner, and digital image processing technology to investigate the surface texture wear process and skid resistance decay trends of basalt asphalt pavement and steel slag asphalt pavement under water erosion and traffic load. The results indicate that under submerged conditions, the skid resistance (BPN) of asphalt pavement declines rapidly during the first 500,000 load cycles, and the rate of decline gradually stabilizes after 500,000 cycles. After 1.2 million load cycles, the BPN of basalt pavement decreased by 28.10%, while that of steel slag pavement decreased by 21.18%, indicating that the skid resistance of steel slag pavement is significantly better than that of basalt pavement. Texture parameters—namely, root mean square height, peak material volume, core material volume, void volume of the core, and valley void volume—exhibited the same decay trend as BPN. The average correlation coefficients between BPN and texture parameters were 0.846, 0.848, 0.898, and 0.916, respectively, indicating that texture parameters can be used as evaluation indicators for skid resistance decay. Finally, the decay of pavement skid resistance was predicted using an exponential decay equation.