Search Results (351)

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article

Micro-textures are crucial for enhancing surface performance in diverse applications, but traditional radial electrochemical micromachining (REMM) suffers from process complexity and workpiece damage. This study presents radial ultrasonic rolling electrochemical micromachining (RUREMM), an advanced technique integrating an ultrasonic field to improve electrolyte renewal, disrupt passivation layers, and optimize electrochemical reaction uniformity on SS304 surfaces. Aimed at overcoming challenges in precision machining, the research explores the synergistic effects of ultrasonic energy and flow field dynamics, offering novel insights for high-quality metal micromachining applications. The research establishes a mathematical model to analyze the interaction between the ultrasonic energy field and electrolytic machining and optimizes the flow field in the narrow electrolytic gap using Fluent software, revealing that an initial electrolyte velocity of 4 m/s and ultrasonic amplitude of 35 μm ensure optimal stability. High-speed photography is employed to capture bubble distribution and micro-pit formation dynamics, while SS304 surface experiments analyze the effects of machining parameters on micro-dimple localization and surface quality. The results show that optimized parameters significantly improve micro-texture quality, yielding micro-pits with a width of 223.4 μm, depth of 28.9 μm, aspect ratio of 0.129, and Ra of 0.205 μm, providing theoretical insights for high-precision metal micromachining. Full article

Nickel-based superalloy Hastelloy C276 is widely used in high-performance industries due to its strength, corrosion resistance, and thermal stability. However, these same properties pose substantial challenges in machining, resulting in high tool wear, surface defects, and dimensional inaccuracies. This study investigates methods to enhance machining performance and surface quality by evaluating the tribological behavior of TiSiN/TiAlN-coated carbide inserts under six cooling and lubrication conditions: dry, MQL with coconut oil, Cryo-LN₂, Cryo-LCO₂, MQL–Cryo-LN₂, and MQL–Cryo-LCO₂. Open-slot finishing was performed at constant cutting parameters, and key indicators such as cutting zone temperature, tool wear, surface roughness, chip morphology, and microhardness were analyzed. The hybrid MQL–Cryo-LN₂ approach significantly outperformed other methods, reducing cutting zone temperature, tool wear, and surface roughness by 116.4%, 94.34%, and 76.11%, respectively, compared to dry machining. SEM and EDS analyses confirmed abrasive, oxidative, and adhesive wear as the dominant mechanisms. The MQL–Cryo-LN₂ strategy also lowered microhardness, in contrast to a 39.7% increase observed under dry conditions. These findings highlight the superior performance of hybrid MQL–Cryo-LN₂ in improving machinability, offering a promising solution for precision-driven applications. Full article

The Electrochemical Machining (ECM) method is one of the most widely used processing methods in metal surface processing, due to its unique advantages. However, the electrolyte in ECM causes stray corrosion on the workpiece. To overcome these shortcomings, we have developed a no-stray-corrosion ECM method called the controllable electrolyte distribution ECM (CED-ECM) method. However, its practical application in metal surface processing remains largely unexplored. In this study, to improve the CED-ECM method, we delved deeper into the aforementioned aspects by simulating the actual ECM process using COMSOL Multiphysics and rigorously validating the simulation results through practical experimental observations. Then, our efforts led to the application of the CED-ECM method to metal surface processing for the SUS304 workpiece, producing noteworthy results that manifest in diverse cross-sectional profiles on the processed surfaces. This research demonstrates a validated simulation framework for the CED-ECM process and establishes a method for creating user-defined surface profiles by controlling pass intervals, enabling new applications in surface texturing. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

During the service life of automotive panel stamping dies, the surface is often subjected to high loads and repeated friction, resulting in excessive wear. This leads to die failure, reduced machining accuracy, and decreased production efficiency. To enhance the anti-friction and wear-resistant performance of die steel surfaces, this study introduces the concept of biomimetic engineering in surface science. By mimicking microstructural configurations found in nature with outstanding wear resistance, biomimetic functional surfaces were designed and fabricated. Specifically, quadrilateral dimples inspired by the back of dung beetles, pentagonal scales from armadillo skin, and hexagonal scales from the belly of desert vipers were selected as biological prototypes. These surface textures were fabricated on Cr12MoV die steel using high-speed ball-end milling. Finite element simulations and dry sliding wear tests were conducted to systematically investigate the tribological behavior of surfaces with different dimple geometries. The results showed that the quadrilateral dimple surface derived from the dung beetle exhibited the best performance in reducing friction and wear. Furthermore, the milling parameters for this surface were optimized using response surface methodology. After optimization, the friction coefficient was reduced by 21.3%, and the wear volume decreased by 38.6% compared to a smooth surface. This study confirms the feasibility of fabricating biomimetic functional surfaces via high-speed ball-end milling and establishes an integrated surface engineering approach combining biomimetic design, efficient manufacturing, and parameter optimization. The results provide both theoretical and methodological support for improving the service life and surface performance of large automotive panel dies. Full article

During the dry machining of 6061 aluminum alloy, cemented carbide tools often suffer from severe wear and built-up edge (BUE) formation, which significantly shortens tool life. Inspired by the non-smooth surface structure of dung beetles, this study proposes an elliptical dimple–groove composite bionic micro-texture, applied to the rake face of cemented carbide tools to enhance their cutting performance. Four types of tools with different surface textures were designed: non-textured (NT), single-groove texture (PT), circular dimple–groove composite texture (AKGC), and elliptical dimple–groove composite texture (TYGC). The cutting performance of these tools was analyzed through three-dimensional finite element simulations using the Deform-3D (version 11.0, Scientific Forming Technologies Corporation, Columbus, OH, USA) software program. The results showed that, compared to the NT tool, the TYGC tool exhibited the best performance, with a reduction in the main cutting force of approximately 30%, decreased tool wear, and significantly improved chip-breaking behavior. Based on the simulation results, a response surface model was constructed to optimize key texture parameters, and the optimal texture configuration was obtained. In addition, a theoretical model was developed to reveal the mechanism by which the micro-texture reduces interfacial friction and temperature rises by shortening the effective contact length. To verify the accuracy of the simulation and theoretical analysis, cutting experiments were further conducted. The experimental results were consistent with the simulation trends, and the TYGC tool demonstrated superior performance in terms of cutting force reduction, smaller adhesion area, and more stable cutting behavior, validating both the simulation model and the proposed texture design. This study provides a theoretical foundation for the structural optimization of bionic micro-textured cutting tools and offers an in-depth exploration of their friction-reducing and wear-resistant mechanisms, showing promising potential for practical engineering applications. Full article

This study investigates the potential of Selective Laser Melting (SLM) to tailor the surface characteristics of Ti6Al4V directly during fabrication, eliminating the need for post-processing treatments potentially for dental implants. By adjusting the Volumetric Energy Density (VED) through controlled variations in the laser scanning speed, we achieved customized surface textures at both the micro- and nanoscale levels. SLM samples fabricated at moderate VED levels (50–100 W·mm³/s) exhibited optimized dual-scale surface roughness—a macro-roughness of up to 25.5–27.6 µm and micro-roughness of as low as 58.8–64.2 nm—resulting in significantly enhanced hydrophilicity, with water contact angles (WCAs) decreasing to ~62°, compared to ~80° on a standard grade 5 machined Ti6Al4V plate. The XPS analysis revealed that the surface oxygen content remains relatively stable at low VED values, with no significant increase. The surface topography plays a significant role in influencing the WCA, particularly when the VED values are low (below 200 W·mm³/s) during SLM, indicating the dominant effect of surface morphology over chemistry in these conditions. Biological assays using osteoblast-like MG-63 cells demonstrated that these as-built SLM surfaces supported a 1.5-fold-higher proliferation and improved cytoskeletal organization relative to the control, confirming the enhanced early cellular responses. These results highlight the capability of SLM to engineer bioactive implant surfaces through process-controlled morphology and chemistry, presenting a promising strategy for the next generation of dental implants suitable for immediate placement and osseointegration. Full article

Magnetorheological finishing is widely used in the high-precision processing of optical components, but due to the influence of multi-source system errors, the convergence of single-pass magnetorheological finishing (MRF) is limited. Although iterative processing can improve the surface accuracy, repeated tool paths tend to deteriorate mid-spatial frequency textures, and for complex surfaces such as aspheres, traditional manual alignment is time-consuming and lacks repeatability, significantly restricting the processing efficiency. To address these issues, firstly, this study systematically analyzes the effect of six-degree-of-freedom positioning errors on convergence behavior, establishes a positioning error-normal contour error transmission model, and obtains a workpiece positioning error tolerance threshold that ensures that the relative convergence ratio is not less than 80%. Further, based on these thresholds, a hybrid self-positioning method combining machine vision and a probing module is proposed. A composite data acquisition method using both a camera and probe is designed, and a stepwise global optimization model is constructed by integrating a synchronous iterative localization algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The experimental results show that, compared with the traditional alignment, the proposed method improves the convergence ratio of flat workpieces by 41.9% and reduces the alignment time by 66.7%. For the curved workpiece, the convergence ratio is improved by 25.7%, with an 80% reduction in the alignment time. The proposed method offers both theoretical and practical support for high-precision, high-efficiency MRF and intelligent optical manufacturing. Full article

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L₂₇ orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R²) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R² and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article

Additive manufacturing (AM) stands out for its variable applications in terms of material, quality, and geometry. Wire Arc Additive Manufacturing (WAAM) is remarkable for producing large parts in reduced times when compared to other AM methods. The possibility of producing a part with a near-net shape not only enhances productivity but also reduces resources usage. However, parts produced by WAAM may need post-processing by machining to achieve functional surface requirements. Therefore, it is important that machining, even if minimized, does not lead to a significant environmental impact. In this sense, this work evaluates the effect of using compressed air, dry cut, and synthetic biodegradable cutting fluid at varying nozzle positions and flow rates on the surface quality of ER70S-6 steel produced by WAAM, after milling with TiAlN-coated carbide tools. To analyze the surface roughness, parameters Ra, Rq, and Rz were measured and microscopy was used to further evaluate the surfaces. The surface hardness was also evaluated. The results showed that a flow rate of 10 L/min promotes better surface quality, which can be further improved using compressed air, leading to a surface quality 50% better when compared to dry cutting. Dry cut was not suitable for machining ER70S-6 WAAM material as it resulted in rough surface texture with an Rz = 4.02 µm. Compressed air was the best overall condition evaluated, achieving a 36% Ra reduction compared to dry cutting, the second-lowest hardness deviation at 6.51%, and improved sustainability by eliminating the need for cutting fluid. Full article

Carbon-fiber-reinforced polymer (CFRP) preforms are vital for high-performance composite structures, yet the real-time detection of surface yarn alignment defects is hindered by complex textures. This study introduces a novel machine vision framework to enable the precise, real-time identification of such defects in CFRP preforms. We proposed obtaining the frequency spectrum by removing the zero-frequency component from the projection curve of images of carbon fiber fabric, aiding in the identification of the cycle number for warp and weft yarns. A texture structure recognition method based on the artistic conception drawing (ACD) revert is applied to distinguishing the complex and diverse surface texture of the woven carbon fabric prepreg from potential surface defects. Based on the linear discriminant analysis for defect area threshold extraction, a defect boundary tracking algorithm rule was developed to achieve defect localization. Using over 1500 images captured from actual production lines to validate and compare the performance, the proposed method significantly outperforms the other inspection approaches, achieving a 97.02% recognition rate with a 0.38 s per image processing time. This research contributes new scientific insights into the correlation between yarn alignment anomalies and a machine-vision-based texture analysis in CFRP preforms, potentially advancing our fundamental understanding of the defect mechanisms in composite materials and enabling data-driven quality control in advanced manufacturing. Full article