Search Results (1,875)

The operational ability of a unit or mechanism depends mainly on the quality of the mechanically produced working surfaces. Many materials can be assigned to a group of hard-to-cut materials that includes titanium- and aluminum-based alloys, a new class of heat-resistant alloys, SiCp/Al composites, hard alloys, and other alloys. The difficulties in their machining are related not only to the high temperatures achieved on the contact pads under mechanical load and the extreme cutting conditions but also to the properties of those materials, which affect the adhesion of the chip to the tool faces, hindering chip flow. One of the possible solutions to reduce those effects and improve the operational life of the tool, and as a consequence, the final quality of the working surface of the unit, is texturing the rake face of the tool with microgrooves or nanogrooves, microholes or nanoholes (pits, dimples), micronodes, cross-chevron textures, and other microtextures, the depth of which is in the range of 3.0–200.0 µm. This review is addressed at systematizing the data obtained on micro- and nanotexturing of PCD tools for cutting hard-to-cut materials by different techniques (fiber laser graving, femto- and nanosecond laser, electrical discharge machining, fused ion beam), additionally subjected to fluorination and dip- and drop-based coatings, and the effect created by the use of the textured PCD tool on the machined surface. Full article

This paper presents an experimental study on the influence of surface texturing on friction and film thickness in the hydrodynamic lubrication regime. Using a pin-on-disk tribometer equipped with light-induced fluorescence microscopy, simultaneous measurements were conducted on smooth and textured samples under parallel and inclined surface conditions. The circular faces of the pins were partially or fully covered by circular laser-machined textures consisting of dimples with depths of 5 or 10 µm, diameters of 50 or 100 µm, and coverage density of 20%. The results demonstrate that while texturing significantly reduces friction and increases film thickness in parallel gaps, with partial inlet coverage being the most effective, its impact is minimal in inclined wedge gaps. The study further reveals that the global geometric wedge dominates over texture effects in inclined contacts and that in-texture cavitation, prevalent in parallel conditions, is suppressed by surface inclination. Three distinct contributions of the textures were discussed: a global hydrodynamic effect, a local hydrodynamic effect, and the influence of surface non-flatness (waviness). The findings suggest that texturing is primarily beneficial for acting as a pseudo-wedge or as surface roughness in contacts where a physical wedge is absent. Full article

This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250–350 W), scanning speed (1050–1300 mm/s), and hatch spacing (65–90 $\mathsf{\mu }$m). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (LightGBM). All models were trained using the Fast Library for Automated Machine Learning & Tuning (FLAML) framework to predict the relative density of the fabricated samples. Among these, the XGBoost model achieved the highest predictive accuracy, with a coefficient of determination $R^2=0.977$, mean absolute percentage error MAPE = 0.002, and mean absolute error MAE = 0.017. Experimental validation was conducted on 27 newly fabricated samples using ML predicted process parameters. Relative densities exceeding 99.6% of the theoretical value (7.76 g/cm³) for all models except XGBoost LD and KRR. The lowest MAE = 0.004 and the smallest difference between the ML-predicted and PBF-LB validated density were obtained for samples made with LightGBM-predicted parameters. Those samples exhibited a hardness of 604 ± 13 HV0.5, which increased to approximately 630 HV0.5 after tempering at 550 °C. The LightGBM optimised parameters were further applied to fabricate a part of a forging die incorporating internal through-cooling channels, demonstrating the efficacy of machine learning-guided optimisation in achieving dense, defect-free H13 components suitable for industrial applications. Full article

Sapphire (α-Al₂O₃) has been widely used in high-power lasers, optical windows, semiconductor substrates, radomes, and other applications due to its exceptional optical properties, high hardness, excellent chemical stability, and thermal resistance. However, machining sapphire poses significant challenges because of the material’s high hardness and brittleness. Traditional mechanical and chemical–mechanical machine methods often fail to meet the processing requirements for micro and nanoscale structures. Recently, the use of femtosecond lasers—with ultra-short pulses and extremely high peak power—has allowed for the precise machining of sapphire with minimal thermal damage, a method akin to cold processing. Femtosecond laser processing offers significant advantages in fabricating three-dimensional micro- and nanoscale structures, surface and internal modification, optical waveguide writing, grating fabrication and dissimilar materials welding. Thus, this paper systematically reviewed the research progress in femtosecond laser processing of sapphire, covering technical approaches such as ablation, hybrid processing and direct writing micro- and nanoscale fabrication. The capability of femtosecond laser processing to modulate sapphire’s optical properties, wettability and mechanical and chemical characteristics were discussed in detail. The current challenges related to efficiency, cost, process standardization and outlines future development directions, including high-power lasers, parallel processing, AI optimization and multifunctional integration were also analyzed. Full article

Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features. Full article

In many laser application scenarios, concentrated optical energy, high coherence, and narrow spectral linewidth are critical optical characteristics that ensure the excellent performance of lasers. These characteristics can be achieved when a laser operates in single longitudinal mode (SLM) rather than multiple longitudinal mode (MLM). Therefore, it is important to identify whether the laser operates in SLM or MLM accurately and efficiently, especially in scenarios with high real-time requirements such as high-precision time measurement. This study proposes a novel machine learning-based method for laser longitudinal mode identification, which has been effectively utilized in the development of an optical clock. Two machine learning classification models are designed, based on a support vector machine (SVM) and a convolutional neural network (CNN), respectively, with the datasets being the interference fringe data measured by a Fizeau wavemeter integrated in the optical clock. Using a dataset that includes 589 interference fringe samples from two different laser wavelengths, it is demonstrated that the machine learning models can achieve 96% to 100% classification accuracy in distinguishing between SLM and MLM. The methodology in this work offers valuable insights for future space missions that require high-precision measurements and lightweight payloads. Full article

Ancient bronzes are invaluable for studying the cultures and history of ancient societies around the world. However, corrosion can diminish their research and aesthetic value, as well as affect their longevity. Therefore, it is crucial to study the corrosion behavior and mechanisms of these artifacts using advanced characterization techniques. This article provides a systematic review of the corrosion behavior of bronze artifacts and the advanced characterization techniques employed in their study. It summarizes the corrosion mechanisms of bronze artifacts and the factors affecting corrosion, including composition, structure, and the external environment. It also describes advanced analytical techniques for characterizing corrosion products and mechanisms, such as X-ray fluorescence (XRF), laser ablation coupled to quadrupole mass spectrometry (LAMQS), X-ray tomography (CT), and neutron diffraction. Bronze corrosion studies can be enhanced by the integration of artificial intelligence (AI) and machine learning (ML). Finally, it discusses potential future research directions in the field of bronze artifact corrosion and conservation. Full article

Robotics is increasingly used in higher education laboratories, but most commercial robots are costly and designed for industrial use. This paper presents the design, modeling, and experimental evaluation of a low-cost four-degree-of-freedom (DOF) SCARA robot for educational and research purposes. The robot supports pick-and-place and laser engraving tasks. Direct and inverse kinematics were developed using Denavit–Hartenberg parameters, and the mechanical structure was validated through the dynamic analyses. A new machine learning (ML) framework integrating Support Vector Machine (SVM) and Random Forest (RF) models was implemented to enhance motion precision, predict task success, and compensate positioning errors in real time. Experimental tests over 360 cyles under varying speeds, payloads, and object types show that the SVM predicts grasp success with 94.4% accuracy, while the RF model estimates XY positioning error with an RMSE of 1.84 mm and cycle time error with an RMSE of 0.41 s. Moreover, a novel approach in this work that combines it with a laser engraving machine has been suggested. Repeatability experiments report 0.97 mm ISO-standard repeatability, and laser engraving trials yield mean positional errors of 0.45 mm, with maximum deviation of 0.90 mm. Compared to a baseline PID controller, the ML-enhanced strategy reduces RMS positioning error from 3.30 mm to 1.83 mm and improves repeatability by 36.5%, while slightly decreasing cycle time. These results demonstrate that the proposed SCARA robot achieves high-precision, consistent, and flexible operation suitable for both academic and light-duty practical applications. Full article

Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics. Full article

In this paper, laser-assisted machining (LAM) of SiC ceramics was taken as the research object, and the different spectrum and energy spectrum characteristics and their changing trends of acoustic emission (AE) signals under processing states of brittleness, plasticity and thermal damage were analyzed. The numerical characterization of ceramic softening degree was indirectly realized by the energy spectrum characteristics of low-frequency band energy ratio, marking a methodological breakthrough in transitioning from qualitative analysis to quantitative detection for identifying plastic processing state. First, the surface morphology of the machined surface based on the single-factor experiment of laser power was analyzed, and three different processing states and ranges of laser power were determined, namely brittle state (0–185 W), plastic state (185–225 W) and thermal damage state (>225 W). Then, the wavelet packet denoising and spectrum analysis of AE signals under different processing states were carried out to obtain the corresponding frequency of the maximum amplitude and the amplitude change trend of the characteristic frequency (515 kHz) in the high-frequency domain. Finally, the energy spectrum analysis of acoustic emission signals was carried out, and the method of indirect characterization of ceramic softening degree by low-frequency band energy ratio was proposed. This paper provides a numerical characterization method and theoretical guidance for the detection and identification of the plastic processing state of ceramic laser-assisted cutting. Full article

Forested areas in Poland comprise numerous post-mining sites that hinder effective forest management. Such mining remnants may pose a threat to humans, animals, and operating forest machines. This study aimed to determine the feasibility of inventorying such man-made landforms as mining waste heaps, excavations, remnants of shallow shafts, adits, etc., using the Digital Elevation Model (DEM) based on Airborne Laser Scanning (ALS) data provided by the national agency (the Head Office of Geodesy and Cartography—HOGC) as open data. The DEM, when combined with other cartographic materials using GIS, accurately reflects the anthropogenic transformation evident in the topography. This paper presents the results of inventorying remnants of iron ore mining in the present-day forested area located between Krzepice, Kłobuck, and Częstochowa in southern Poland. The identification and inventory of post-mining landforms, mainly mounds resulting from shallow shaft mining operations, were supplemented by their digitization, automatically providing information on parameters such as perimeter (ranged in most cases from 24.3 to 159 m), surface area (46.9 to 1656 m²), length and width (7.8 to 59.2 m). The heights of the investigated structures were also read from the DEM, ranging from 0.3 to 4.1 m. Much larger structures were also identified, but they occurred accidentally (up to 23.5 m in height). In this manner, approximately 823 morphological forms were characterized, resulting in a database. Test fieldwork was then conducted to verify the DEM readings. It was proposed to calculate deformation indexes (Id [%]) for forested areas and apply them when estimating the forest management hindrance index used by the State Forests. The studied forest compartments managed by State Forests were characterized by an Id value from 0.1 to 55.5%. This type of measure provides a helpful tool in planning forestry operations in areas with diverse topography, including those transformed by mining activities. The actual environmental impact is highlighted. Forest management practices in the study area must take into consideration, in particular, topography, as well as geology and hydrology. Studies have shown that the DEM based on the ALS data is sufficiently accurate to detect even minor post-mining deformations (which may be important, in particular, in inaccessible areas). The recorded parameters can be considered when planning management, protection interventions, or reclamation activities. Full article

Ultra-precision single-point diamond turning (SPDT) remains the core process for fabricating optical-grade surfaces with nanometric roughness and sub-micrometer form accuracy. However, machining hard-to-cut or brittle materials such as high-entropy alloys, metals, ceramics, and semiconductors is limited by severe tool wear, high cutting forces, and brittle fracture. To overcome these challenges, a new generation of non-conventional assisted and hybrid SPDT platforms has emerged, integrating multiple physical fields, including mechanical, thermal, magnetic, chemical, or cryogenic methods, into the cutting zone. This review comprehensively summarizes recent advances in hybrid non-conventional assisted SPDT platforms that combine two or more assistive techniques such as ultrasonic vibration, laser heating, magnetic fields, plasma or gas shielding, ion implantation, and cryogenic cooling. The synergistic effects of these dual-field platforms markedly enhance machinability, suppress tool wear, and extend ductile-mode cutting windows, enabling direct ultra-precision machining of previously intractable materials. Recent key case studies are analyzed in terms of material response, surface integrity, tool life, and implementation complexity. Comparative analysis shows that hybrid SPDT can significantly reduce surface roughness, extend diamond tool life, and yield optical-quality finishes on hard-to-cut materials, including ferrous alloys, composites, and crystals. This review concludes by identifying major technical challenges and outlining future directions toward optimal hybrid SPDT platforms for next-generation ultra-precision manufacturing. Full article

Solar sailing has proven to be an effective solution for cost-effective and long-term space missions due to its fuel-free propulsion. While multiple large-scale solar sails based on kilogram-class satellites have been developed and tested in space, solar sails created for lightweight chip-scale satellites are much less. To enable the gram-class satellite of solar sailing for active attitude adjustment and orbital maneuvers, a novel solar sail driven by two-way shape memory effect (TWSME) was proposed in this work. The solar sail base was made of rectangular Al-Kapton thin films, while a U-shaped NiTi beam was developed by 50 μm thin Ni_50.6Ti_49.4 foils. Both of the U-shaped NiTi beam and rectangular Al-Kapton thin films were manufactured by the ultra-fast femtosecond laser cutting machine. Finite element modeling of single U-shaped NiTi beam and assembled solar sail were built to validate that an 80 mm-long TWSME NiTi beam with a curvature of 37.31 m⁻¹ were sufficient to drive the solar sail for solar radiation pressure modulation. A solar sail prototype was developed, and an in situ experiment test of the prototype was conducted with infrared imaging, showing efficient bending behaviors by application of a 0.5 A direct current across the U-shape NiTi beam. These findings reveal that U-shaped TWSME NiTi foils provide an effective driving strategy for lightweight chip-scale satellites, and thus dramatically broaden the space application of the gram-scale satellite. Full article

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN₂ cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN₂, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN₂ (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN₂ provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R² = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R² ≈ 0.878). Full article

In this study, regression-based machine learning models were developed to predict the melt pool width and depth formed during the Laser Powder Bed Fusion (LPBF) process for Fe-3.4Si and Fe-6Si alloys. Based on experimentally obtained melt pool width and depth data, a total of 11 regression models were trained and evaluated, and hyperparameters were optimized via Bayesian optimization. Key process parameters were identified through data preprocessing and feature engineering, and SHAP analysis confirmed that the input energy had the strongest influence on both melt pool width and depth. The comparison of prediction performance revealed that the support vector regressor with a linear kernel (SVR_lin) exhibited the best performance for predicting melt pool width, while the multilayer perceptron (MLP) model achieved the best results for predicting melt pool depth. Based on these trained models, a power–velocity (P-V) process map was constructed, incorporating boundary conditions such as the overlap ratio and the melt pool morphology. The optimal input energy range was derived as 0.45 to 0.60 J/mm, ensuring stable melt pool formation. Specimens manufactured under the derived conditions were analyzed using 3D X-ray CT, revealing porosity levels ranging from 0.29% to 2.89%. In particular, the lowest porosity was observed under conduction mode conditions when the melt pool depth was approximately 1.0 to 1.5 times the layer thickness. Conversely, porosity tended to increase in the transition mode and lack of fusion regions, consistent with the model predictions. Therefore, this study demonstrated that a machine learning-based regression model can reliably predict melt pool characteristics in the LPBF process of Fe-Si alloys, contributing to the development of process maps and optimization strategies. Full article