Abstract To address the unclear correlation between the microgeometry generated by scraping and wear properties, this study analyses the relationship between 3D surface texture parameters and wear rate. The effects of these parameters on the wear properties of scraped surfaces are examined. Five groups of scraped specimens with different accuracy grades are prepared, and nine 3D surface texture parameters are calculated in accordance with ISO 25178. Friction coefficients and wear rates are measured by reciprocating sliding tests under dry friction. Based on grey system theory, the grey relational grade between each parameter and the wear rate is calculated to identify the key texture parameters, and a fractional accumulation prediction model is established. The model is evaluated using verification specimen data. As the accuracy of the scraped specimens decreases, the height and volume parameters generally increase, while the peak to valley fluctuations and contact non-uniformity become more pronounced. Both the friction coefficient and wear rate increase as the accuracy of the scraped specimens decreases. Kurtosis ( S ku ) and core material volume ( V mc ) have the two highest grey relational grades with wear rate and were therefore selected as model inputs. After incorporating these parameters into the prediction model, the average relative error between the measured and predicted wear rates of the verification specimens is 2.136%. The results indicate that, under dry friction point contact conditions, S ku and V mc are the parameters most strongly associated with the wear rate. The results provide a quantitative basis for comparing the relative wear behaviour of scraped surfaces and for evaluating scraping process quality under consistent test conditions.
Abstract Hydrostatic thrust bearings are critical components of the support system in mechanical machining equipment. Fabricating surface textures on their working surfaces can significantly enhance the lubrication performance of the clearance oil film. This paper takes the clearance oil film of hydrostatic thrust bearings as the research object, employing Ansys CFX to conduct simulations and analyze the lubrication performance of the oil film at the oil sealing edge with non-textured, rectangular, triangular, and trapezoidal surface textures. The effects of texture shape, size, and quantity distribution on the lubrication performance of the clearance oil film were investigated. The results demonstrate that, when comprehensively considering the bearing capacity and temperature rise characteristics of the oil film, the introduction of textures on the downstream side of the oil sealing edge can effectively improve the bearing capacity of the clearance oil film, while inducing nearly no change in its temperature rise. Among the textures studied, the triangular texture demonstrated the best performance in enhancing oil film lubrication, followed by the trapezoidal texture, while the rectangular texture exhibited slightly inferior performance. In addition, a parametric study on the number and dimensional parameters of the triangular texture was conducted, and the optimal triangular texture parameter set for maximizing the oil film lubrication performance was obtained.
Abstract Scraping is a surface finishing process used to improve contact performance and is widely applied to joint interfaces, such as precision machine-tool guideways and surface plates. However, owing to the complex morphology of scraped surfaces, the mechanism by which scraping enhances contact characteristics remains unclear. To reveal this contact mechanism, an efficient deterministic numerical framework combining the boundary element method, fast Fourier transform, and conjugate gradient algorithm was developed and experimentally validated. The results indicate that the scraped surface ( S tr = 0.6–0.78) exhibits a more isotropic texture in the real contact area compared to the ground surface ( S tr = 0.07–0.11). The uniformity of the real contact area for high‐precision scraping ( C v = 0.28–0.45) is similar to that for the ground surface ( C v = 0.22–0.46), and both are higher than that for low‐precision scraping ( C v = 0.49–0.89). Scraping shifts the load-bearing mechanism of ground surfaces to discrete high points, effectively mitigating stress concentration and enhancing contact performance. This study provides a theoretical basis for optimizing high-precision surface finishing processes and evaluating the performance of machine-tool joints.
Abstract Loosely packed palladium (Pd) microstructures exhibit exceptional catalytic efficiency and chemical versatility, making them pivotal for advanced catalytic technologies including gas sensing, electrochemical energy conversion, and hydrogen storage. However, it remains challenging to simultaneously achieve precise spatial localization and morphological control of loosely packed Pd microstructures. This study provides a site-controlled deposition method based on scratch-induced fabrication of loosely packed Pd microstructures on silicon substrates. To optimize the fabrication process, the effects of scratch and deposition conditions were systematically investigated. The selective formation mechanism was elucidated through detailed microstructure and electrical conductivity analysis. It was found that the normal load governs the lateral confinement of the structures, whereas the interplay between deposition time and concentration regulates particle diameter, thereby achieving tailored surface morphologies. The fabricated Pd microstructures exhibit tailorable electronic transport properties, as evidenced by I – V curve analysis. The catalytic ability of the loosely packed architectures was validated through hydrogen-sensing evaluations, with the gas concentration ranging from 0.1% to 1%. This work offers a new insight into the cost-effective, site-controlled preparation of loosely packed Pd micropatterns.
Abstract In this study, a combination surface modification technique integrating high-speed cutting and solid projectile entrained water jet (HSC-SPEWJ) was employed to strengthen the surface of a 7075-T6 aluminum alloy. By combining analyses of surface morphology, microhardness, residual stress, transmission electron microscopy (TEM), and numerical simulation, the evolution of surface properties and the underlying microstructural transformation mechanisms during the combined modification process were systematically investigated. The results indicate that, after samples after HSC machining, the sample surface was mainly characterized by machining feed marks accompanied by localized plastic deformation. Following the introduction of SPEWJ surface modification, the surface morphology was dominated by uniformly distributed plowing grooves, while localized impact pits and irregular damage features were significantly reduced. As the jet pressure increases from 5 MPa to 11 MPa, the surface residual compressive stress increases from −176.57 MPa to −300.85 MPa, and the depth of the compressive residual stress layer increases from 247.87 μ m to 375.39 μ m. In contrast, both microhardness and residual compressive stress show a negative correlation with the jet standoff distance. Compared with single HSC and SPEWJ treatments, the HSC-SPEWJ combined modification significantly enhances the surface strengthening effect of 7075-T6 aluminum alloy, increasing the surface microhardness to 188.46–207.22 HV, corresponding to improvements of approximately 10.37%–21.36% and 4.13%–14.49% relative to single HSC and single SPEWJ, respectively. Finite element simulations indicate that the water jet impact induces stress superposition within the cutting-induced plastic layer and promotes stress propagation toward deeper regions. TEM analyses reveal that a high density of dislocations with a non-uniform distribution is formed during the samples after HSC machining stage, whereas SPEWJ surface modification facilitates dislocation multiplication and rearrangement, leading to a more homogeneous plastic deformation behavior.
Abstract Introduction. Multisensory exploration of materials plays a central role in our daily lives, influencing our perceptions, emotions, and decisions. Intermodal processes integrate and assemble each stimulus to form a unified and coherent sensory image. However, the contribution of each modality to the material experience is not clearly understood. In the case of visual and tactile modalities, the two sensory channels are closely linked, which complicates the analysis of the contribution of each modality. Faced with this complexity, we have developed a virtual reality (VR) device to separate the sources through unimodal and bimodal approaches. Understanding these integrations is essential to increase the knowledge on perceptual processes and innovate in design or immersive technologies fields. Method. VR device was developed to study the emotional reactions induced by contact with five emotional materials (fur, velvet, wood, sandpaper). The emotional reactions produced in the Unimodal-Tactile only and Bimodal-visio-tactile approaches were evaluated on a panel of 28 people. The emotional feeling is measured on two dimensions: valence (tactile pleasure from unpleasant to pleasant) and Arousal (intensity of the emotional response from low to high). The link between ‘emotion’ and ‘surface topography’ is evaluated using perceptual measures (hardness, roughness, stickiness) as well as an instrumental measurement performed with an artificial finger (Touchyfinger). Result. A first unimodal study conducted in both real life and VR allowed us to conclude that our VR configuration was suitable for carrying out a multimodal study. Then two VR experiments were conducted: Unimodal VR-tactile only mode vs Bimodal VR-visio-tactile mode. No significant differences were observed, indicating that in this context, visual information does not influence tactile experience. This result supports touch dominance , which is an attentional process known as biased competition -Colavita effect. Moreover, a very strong correlation was found between the estimated valence and (i) ‘hardness, roughness, stickiness measure’s describing the materials, and (ii) the ‘instrumental measurements’ made with an artificial finger which records the topographical properties of the materials. Conclusion. Our experiments seem to reveal the existence of a tactile dominance which could be due to the intermodal integration process which degrades the visual signals coming from the virtual environment in favor of tactile perceptions.
The friction-coupling in a switch machine is vulnerable to coefficient of friction (COF) drift and localized wear under complex service conditions, which may degrade its torque-limited performance. Pin-on-disk tests were conducted to clarify how normal load and rotational speed affect friction response, wear evolution, and contact stability. To further reveal the role of surface topography, a coupled contact-wear model and finite element simulations were employed to evaluate the effect of surface texture orientation. The results show that increasing normal load mainly shortens the running-in stage and reduces the initial COF, while the steady-state COF exhibits a non-monotonic dependence on load. In contrast, increasing the rotational speed markedly intensifies steady-state COF fluctuations and wear damage. Surface texture orientation has a stronger influence on wear localization than on total wear volume. The 0 degrees and 45 degrees orientations show similar wear volumes and shallow maximum wear depths, while the 90 degrees orientation produces higher wear volume and more pronounced localized damage. Simulations show that the 45 degrees texture promotes redistribution of contact pressure and migration of high-pressure regions. These findings establish a link between surface texture orientation and wear stability, providing guidance for safe operating windows and surface-topography optimization of friction-coupling.
Ti6Al4V (TC4) titanium alloy exhibits low wear resistance; therefore, a combined surface modification process involving micro-arc oxidation and hollow-cathode plasma nitriding was employed to modify it. By optimizing the gas pressure during hollow-cathode plasma nitriding, 220 Pa was determined to be the optimal process parameter. A comparative study was conducted on the microstructure and properties of the TC4 substrate, hollow-cathode plasma nitrided samples (PN), and composite samples treated with micro-arc oxidation and hollow-cathode plasma nitriding (MAO-PN). The results indicate that the composite layer consists primarily of rutile-type TiO2, anatase-type TiO2 and TiN phases, with a thickness of approximately 30 mu m; the microhardness of the composite modified layer increased significantly to 1232 HV, approximately 3.5 times that of the substrate; under dry friction conditions, the MAO-PN sample exhibited a coefficient of friction of approximately 0.8, the lowest wear rate, and the shallowest and narrowest wear marks, effectively suppressing adhesive wear. The porous ceramic layer formed by MAO pretreatment provides diffusion pathways for nitriding, whilst the subsequently generated TiN phase fills the pores, forming a dense and hard composite barrier layer, thereby significantly improving the wear resistance of the titanium alloy.
Abstract Surface topography is widely recognized as influential in orthopedic implant performance. Here we summarize advances in 2D to 3D areal surface characterization and discuss their functional relevance to metallic, ceramic, and polymeric biomaterials. We outline strengths and limitations of commonly used techniques and present selected examples that illustrate links between topography and biological/tribological responses. The review details how surface features affect biological processes (osteoblast adhesion, macrophage polarization, bacterial colonization) and mechanical phenomena (tribocorrosion, wear particle generation, lubrication regimes). Representative case studies illustrate the interplay between topography and clinical performance, especially in textured and coated surfaces. Finally, this review identifies the current metrological challenges and presents emerging trends—including time-resolved 4D metrology, AI-assisted interpretation, and digital twin frameworks—as strategic tools for predictive implant development. By bridging metrological precision with biological function and regulatory context, this article provides a comprehensive roadmap to guide the design, optimization, and validation of next-generation orthopedic implants.
Ti-6Al-4V titanium alloy was used as the substrate material, and hexagonal boron nitride (h-BN) particles at concentrations of 0, 2, 4, and 6 g l-1 were added to a phosphate-silicate base electrolyte, respectively. h-BN modified composite coatings were prepared by microarc oxidation (MAO) technology, and the effects of h-BN particle addition on the microstructure, phase composition, microhardness, and friction and wear properties of the coatings were systematically investigated. The results showed that with increasing h-BN concentration, the coating color gradually changed from dark brown to grayish white, and the average coating thickness monotonically decreased from 41 mu m to 27 mu m, representing an approximately 34% thickness reduction. XRD analysis confirmed that h-BN remained stable in the coating in its original phase form. The surface microstructure of the coating gradually transformed from typical crater-like macroporous structures to fine and uniform microporous structures. When the h-BN concentration was 4 g l-1, the porosity reached the minimum average value of 12.64 +/- 0.55%; thereafter, as the h-BN particle concentration further increased to 6 g l-1, the porosity increased again. Due to the densification of the MAO coating, the microhardness increased by 15% compared to the base coating, reaching a maximum value of 445 HV. Friction and wear tests indicated that at 4 g l-1, the coating exhibited the lowest average friction coefficient (0.285) with a 33% reduction compared with the base coating, with a short running-in period and stable curve, demonstrating optimal friction-reducing and wear-resistant performance. At 6 g l-1, due to excessive h-BN agglomeration, the coating structure became loose, and the friction coefficient increased to 0.352. A concentration of 4 g l-1 was determined to be the optimal addition level for h-BN particle modified MAO coatings on Ti-6Al-4V titanium alloy. The excellent comprehensive performance originates from a unique non-monotonic synergistic mechanism, namely the competitive relationship between the coating growth inhibition effect of insulating h-BN particles and their dual functions of microstructure densification and solid lubrication. This trade-off effect creates a distinct optimal performance window, rather than a simple monotonic property variation with h-BN dosage, achieving the best matching of coating microstructure optimization and h-BN lubrication performance.
Abstract The continuous discharge of household and industrial wastewater, along with frequent oil spills, has a negative impact on both the environment and human health. Herein, we have developed a freestanding superhydrophobic membrane composed of polyethylene terephthalate (PET) and SiO 2 through the electrospinning technique that exhibits both effective oil–water separation performance and enhanced reusability. The interwoven nanofibers, along with embedded sol-gel processed hydrophobic SiO 2 nanoparticles, not only produce a rough surface structure but also significantly reduce the surface energy of the membrane. It results in superhydrophobic and superoleophilic properties with a water contact angle of 157.6 ± 2° and an oil contact angle of nearly 0°. The as-prepared membrane exhibits an average permeation flux of 907 ± 63 L/m 2 ·h, with a separation efficiency of over 95.51% for various types of oils and organic solvents, including petrol, diesel, hexane, toluene, and xylene. Additionally, it maintains an impressive separation efficiency of over 86.47% even after 24 consecutive oil–water separation cycles. The membrane also showed good stability against ultrasonication, adhesive tape, and ultraviolet irradiation tests. Moreover, it exhibits exceptional chemical resistance for up to three days of immersion in both acidic and basic solutions. The fabricated composite membrane exhibits remarkable self-cleaning capabilities, enhancing its suitability for practical applications.
The surface roughness of parts directly determines their service performance, and the influencing factors during the turning process are complex and interrelated. This paper focuses on the two core influencing factors: tool structure and turning parameters, conducting a study on the mechanisms affecting parts' surface roughness. First, a tool cutting edge model including the tool nose radius and main inclination angle is constructed, combined with the motion trajectory to generate point cloud data of the turned surface topography; second, multiple sets of comparative turning experiments are designed, and the surface roughness of parts under different feed rates is measured to verify the accuracy of the proposed topography simulation method; finally, based on this simulation method, the coupled influence patterns of tool structure and turning parameters on parts' surface roughness are systematically analyzed, providing theoretical support and technical reference for the optimization of turning processes and surface quality control.
Additive manufacturing (AM) has rapidly evolved into a groundbreaking technology in biomedical engineering, offering unprecedented capabilities for fabricating patient-specific, anatomically complex structures with high precision. This review presents a comprehensive and critical overview of recent innovations in AM-applied biomaterials, focusing on the integration and application of hydrogels, biopolymers, ceramics, metals, and composite systems. These materials, each with unique biological and mechanical attributes, are pivotal in advancing regenerative medicine, tissue engineering, and the development of next-generation medical implants and devices. Special emphasis is placed on hydrogel-based bioinks and photopolymerizable networks used in 3D bioprinting, which offer tunable properties, excellent biocompatibility, and the ability to mimic extracellular matrix environments. Furthermore, the synergistic design of structural and functional materials in AM platforms is explored to address critical challenges such as mechanical durability, degradation kinetics, immunomodulation, and dynamic cell–matrix interactions. By synthesizing current progress in material science, biofabrication strategies, and translational pathways, this review highlights the transformative potential of AM in shaping the future of personalized and precision medicine—bridging the gap between innovative material design and clinically viable biomedical solutions.
Nickel-titanium (NiTi) alloys are widely used in the medical field due to their excellent strength, biocompatibility, and tribological properties. Additionally, NiTi exhibits unique characteristics such as shape memory and pseudo-elasticity, which confer significant advantages in the manufacturing of components with specific functions. However, friction and wear inevitably occur during service, compromising component performance and potentially necessitating replacement in severe cases. Surface texture effectively modulates tribological performance across micro and macro scales through alterations in surface geometric morphology and microstructure, thereby regulating friction, wear, and lubrication characteristics. Numerous surface texture processing methods are currently available. This paper systematically reviews texture processing methods categorized by machining principle, including conventional methods (turning, milling, grinding) alongside advanced techniques such as laser processing, electrical discharge machining (EDM), abrasive jet machining (AJM), electron beam melting (EBM), electrochemical machining (ECM), chemical etching, and hybrid approaches. The study comparatively analyzes the advantages and limitations of each method while evaluating their respective influences on the tribological properties of resultant surface textures. Furthermore, current challenges and future prospects in NiTi surface texture technology are addressed, offering critical insights for advancing research on texture processing methods and their tribological performance in medical NiTi applications.
Drawing on the excellent anti-erosion characteristics of non-smooth biological surfaces, laser processing was employed in this study to fabricate array groove structures on the surface of ductile iron, and its cavitation resistance was investigated in engine coolant. Within the hundred-micron scale, the results show that samples with a groove width-to-spacing ratio L/W of 0.5 and 1 exhibited greater cavitation resistance than conventional polished samples. Notably, sample with L= 200 mu m and W =200 mu m (L/W= 1) exhibited optimal anti-cavitation performance, which had a significant 42.0% reduction in cumulative mass loss and nearly twice the anti-cavitation performance relative to the polished sample. Furthermore, an evaluation model was established by introducing a new dimensionless service performance parameter Gs. Based on the model analysis, the anti-cavitation mechanism of grooved structures did not merely prolong the incubation period. It significantly slowed down the damage progression during the acceleration period and mitigated long-term cumulative erosion damage during the decay period, thereby achieving full-life cavitation protection. This study provides a valuable reference for the design of cavitation-resistant surface structures in engineering.
To address the fretting wear challenges of propeller hub bearing interfaces, the effects of optimizing laser surface texturing (LST) processes and texturing parameters on the tribological performance of nickel-aluminum bronze alloys have been investigated. The orthogonal experimental design, fretting wear tests, and microstructural characterization were employed to evaluate the relationship between laser processing parameters, surface-textured structure parameters, and anti-wear resistance performance. Investigations into LST processing characteristics revealed that while increasing power triggers a plasma shielding effect that limits ablation efficiency, elevating the scanning speed to 240 mm s-1 can reduce recast layer thickness by over 80%. A deterministic surface texturing with superior structural integrity was achieved. Compared to smooth sample surfaces, LST specimens maintained an attenuated and stable friction response (friction coefficient: 0.151-0.169) and significantly suppressed the transition from abrasive wear to severe plastic deformation. This superior performance is attributed to the 'multi-functional reservoir' effect of the surface-textured structures, which facilitates hydrodynamic lubrication and sequesters deleterious debris to mitigate three-body abrasion and thermal softening. While intensified edge stresses (140 MPa) under extreme marine service condition (>= 55 MPa) can trigger localized fracture and lateral extrusion, this research underscores that deterministic control of texture geometry is critical for sustaining the long-term service reliability of marine propulsion systems.
In the field of tribology, surface texturing is crucial because it has proven highly effective in reducing the coefficient of friction (COF), mitigating wear, and regulating hydrodynamic properties in mechanical systems. It also offers significant benefits for energy efficiency in plain bearings widely used in industrial and automotive applications. This research focused on the design, fabrication, and tribological evaluation of deterministic textures on concave curved surfaces manufactured from SAE 63 and SAE 67 bronzes. These textures were produced by multi-axis CNC micromachining to ensure geometric accuracy along the internal concave perimeter of the specimens. The results revealed that W-type textures on SAE 67 bronze reduced the COF by approximately 20% and the wear rate by about 30% compared with those of untextured samples, due to the formation of a stable Pb-rich transfer film that enhances lubrication and load distribution. In contrast, G-type textures on SAE 63 exhibited moderate wear reduction mainly associated with debris evacuation. The novelty of this work lies in the application of deterministic texturing on concave conformal surfaces using multi-axis CNC micromachining, and in the comparison between alloys with different soft-phase (Pb) contents, providing quantitative insights for the design of textured conformal components such as bearings and bushings.
The ISO standard Gaussian surface roughness filter cannot be directly applied to scattered point cloud surface measurement data, as it was originally developed for gridded areal surfaces. This study presents a Gaussian filtering approach that enables direct three-dimensional surface roughness characterisation from point cloud datasets, eliminating the need for conversion to a gridded format. The transmission characteristics of the proposed filter are analysed and shown to depend on both the amplitude and wavelength of the input signal. To mitigate this dependency, the alpha parameter in the ISO standard Gaussian equation is adapted, allowing the proposed filter's transmission behaviour to more closely match that of the ISO-standard Gaussian filter. The method's performance is evaluated using both simulated point cloud data and measured surfaces produced by additive manufacturing, including a hemispherical geometry and an internal U-bend channel. The results demonstrate that the proposed approach enables direct 3D surface characterisation without data compression or simplification, effectively extending Gaussian filtering to the analysis of non-gridded measurement data.
Automated detection of surface defects in construction machinery has become a critical factor in ensuring equipment safety and stable operation. However, in practical applications, construction machinery often operates under complex illumination conditions, where defect areas exhibit significant variations across different illumination scenarios. Additionally, the scarcity of high-quality annotated samples presents challenges to the robustness and transferability of traditional deep learning methods. In this paper, a cross-modal few-shot segmentation network (CFSNet) is proposed to enhance surface defect segmentation performance under complex illumination conditions. The method utilizes Retinex decomposition to enhance the reflection information of RGB images, which effectively suppresses illumination interference and improves feature characterization. At the same time, infrared (IR) modal information is incorporated to provide stable structural priors. A semantic matching module and a feature guidance enhancement module are proposed to achieve multi-modal fusion and robust representation across varying illumination conditions. Under conditions of sparse annotated samples, CFSNet improves detection accuracy and generalization capabilities across illumination scenarios through structural alignment and semantic completion strategies. The experimental part constructs a construction machinery surface defect dataset containing multiple illumination conditions. The results show that the proposed method achieves the best performance compared to the state-of-the-art methods for 1-shot and 5-shot tasks, which validates its adaptability and practical value in complex illumination environments.
Art authentication requires converging evidence, especially for high value works where attribution uncertainty has major cultural and economic consequences. Here we propose a noninvasive, metrology-inspired approach that characterizes pictorial texture through the areal surface fractal dimension (ISO 25 178 Sfd) computed with the box-counting (Minkowski-Bouligand) method. Very high-resolution painting images are converted into 3D-like topographic maps using grayscale intensity as a height proxy, and fractal dimensions are extracted at two complementary levels: the whole artwork and selected homogeneous areas representative of brushwork. Using nine works attributed to Vincent van Gogh, we model the reference distribution of fractal dimensions and evaluate two debated cases. The analysis rejects the known forgery 'The plowmen' as a strong outlier (Z = - 2.336) while supporting the consistency of the recently authenticated 'Sunset at Montmajour' (Z = 1.64) with the reference corpus. To test extensibility beyond a single painter, we additionally compare Van Gogh with David Kl & ouml;cker Ehrenstrahl (1628-1698) using eight paintings per artist; the fractal-dimension distributions are clearly shifted (Van Gogh mean 2.7488 vs Ehrenstrahl mean 2.5963) with statistically robust separation (t-test p approximate to 0.0012). Overall, these results indicate that fractal descriptors of the paint layer can serve as a quantitative morphological signature that complements established authentication workflows, while motivating larger, style-controlled corpora and standardized acquisition pipelines.
