Search Results (459)

This study investigates the mechanical, tribological, and electrochemical behaviour of a novel precipitation-hardenable martensitic alloy produced by selective laser melting (SLM). The alloy was specifically engineered with an optimised composition, free from cobalt and molybdenum, and featuring reduced nickel content (7 wt.%) and 8 wt.% chromium. It has been developed as a cost-effective and sustainable alternative to conventional maraging steels, while maintaining high mechanical strength and a refined microstructure tailored to the steep thermal gradients inherent to the SLM process. Several ageing heat treatments were assessed to evaluate their influence on microstructure, hardness, tensile strength, retained austenite content, dislocation density, as well as wear behaviour (pin-on-disc test) and corrosion resistance (polarisation curves in 3.5%NaCl). The results indicate that ageing at 540 °C for 2 h offers an optimal combination of hardness (550–560 HV), tensile strength (~1700 MPa), microstructural stability, and wear resistance, with a 90% improvement compared to the as-built condition. In contrast, ageing at 600 °C for 1 h enhances ductility and corrosion resistance (Rp = 462.2 kΩ; Ecorr = –111.8 mV), at the expense of a higher fraction of reverted austenite (~34%) and reduced hardness (450 HV). This study demonstrates that the mechanical, surface, and electrochemical performance of this novel SLM-produced alloy can be effectively tailored through controlled thermal treatments, offering promising opportunities for demanding applications requiring a customised balance of strength, durability, and corrosion behaviour. Full article

The conventional manufacturing of refractory complex concentrated alloys (RCCAs) for high-temperature applications is complicated, particularly when material costs and high melting points of the materials processed are considered. Additive manufacturing (AM) could provide an effective alternative. However, the extreme temperatures involved represent significant challenges for manufacturing defect-free alloys using this approach. To address this issue, we investigated the preparation of a CrNbTiZr quaternary complex concentrated alloy from an equimolar blend of elemental powders using commercially available powder-blown L-DED technology. Initially, the alloys exhibited some defects owing to the internal stress caused by the temperature gradients. This was subsequently resolved by optimizing the deposition strategy. SEM, XRD and EDS were used to analyze the alloy in the as-deposited condition, revealing a BCC phase and a secondary Laves phase. Furthermore, Vickers hardness testing demonstrated a correlation between the hardness and the volume fraction of the Laves phase. Finally, successfully performed compression tests confirmed that the prepared material exhibits high-temperature strength and therefore is promising for high-temperature application under extreme conditions. Full article

Due to the limitations of high speed and short time in plasma-spraying experiments, this study established a simulation model of Y₂O₃ multi-particle deposition to discuss the influence of laser loading on coating-deposition behavior and performance. According to the simulation results, the temperature of coating particles under laser loading displays a gradient distribution, with the surface having the highest temperature. The particles deposit on the substrate to form uniform pits of a certain depth. Plastic deformation causes maximum stress to occur at the edges of the pits and maximum strain to occur on the sidewall of the pits. The deposition region had both compressive and tensile stresses, and laser loading greatly reduced the tensile stresses’ magnitude while having less of an impact on the particle strains. Laser assistance promotes further melting of particles, reduces coating thickness, lowers coating porosity to 3.94%, increases hardness to 488 MPa, reduces maximum pore size from 68 µm to 32 µm, and causes particle sputtering to gradually evolve from being disc-shaped to being finger-shaped, creating cavities at the coating edges. The comparison between the surface morphology and the cross-section pores of the experimentally prepared coating verified the rationality and viability of the simulation work. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

The structural units with different characteristic scales in gradient nanostructured (GS) 316L stainless steel act synergistically to achieve the matching of strength and plasticity, and the intrinsic plasticity of nanoscale and ultrafine grains is fully demonstrated. The macroscopic stress–strain responses of each material unit in the GS surface layer can be measured directly by tension or compression tests on microspecimens. However, the experimental results based on microspecimens do not reflect either the extraordinary strengthening effect caused by non-uniform deformation or the intrinsic plasticity of nanoscale and ultrafine grains. In this paper, a method for constructing depth-dependent constitutive relationships of GS materials was proposed, which combines strain hardening parameter (hardness) with physics-informed neural networks (PINNs). First, the microhardness distribution on the specimen cross-sections was measured after stretching to different strains, and the hardness–strain–force test data were used to construct the depth-dependent PINNs model for the true strain–hardness relationship (PINNs_εH). Hardness–strain–force test data from specimens with uniform coarse grains were used to pre-train the PINNs model for hardness and true stress (PINNs_Hσ), on the basis of which the depth-dependent PINNs_Hσ model for GS materials was constructed by transfer learning. The PINNs_εσ model, which characterizes the depth-dependent constitutive relationships of GS materials, was then constructed using hardness as an intermediate variable. Finally, the accuracy and validation of the PINNs_εσ model were verified by a three-point flexure test and finite element simulation. The modeling method proposed in this study can be used to determine the position-dependent constitutive relationships of heterogeneous materials. Full article

In the context of critical challenges in curcumin-modified polyurethane synthesis—including limited curcumin bioavailability and suboptimal biodegradability/biocompatibility—a novel polyurethane material (Cur-PU) with good mechanical, shape memory, pH-responsive, and biocompatibility was synthesized via a one-pot, two-step synthetic protocol in which HO-PCL-OH served as the soft segment and curcumin was employed as the chain extender. The experimental results demonstrate that with the increase in Cur units, the crystallinity of the Cur-PU material decreases from 32.6% to 5.3% and that the intensities of the diffraction peaks at 2θ = 21.36°, 21.97°, and 23.72° in the XRD pattern gradually diminish. Concomitantly, tensile strength decreased from 35.5 MPa to 19.3 MPa, and Shore A hardness declined from 88 HA to 65 HA. These observations indicate that the sterically hindered benzene ring structure of Cur imposes restrictions on HO-PCL-OH crystallization, leading to lower crystallinity and retarded crystallization kinetics in Cur-PU. As a consequence, the material’s tensile strength and hardness are diminished. Except for the Cur-PU-3 sample, all other variants exhibited exceptional shape-memory functionality, with R_f and R_r exceeding 95%, as determined by three-point bending method. Analogous to pure curcumin solutions, Cur-PU solutions demonstrated pH-responsive chromatic transitions: upon addition of hydroxide ion (OH⁻) solutions at increasing concentrations, the solutions shifted from yellow-green to dark green and finally to orange-yellow, enabling sensitive pH detection across alkaline gradients. Hydrolytic degradation studies conducted over 15 weeks in air, UPW, and pH 6.0/8.0 phosphate buffer solutions revealed mass loss <2% for Cur-PU films. Surface morphological analysis showed progressive etching with the formation of micro-to-nano-scale pores, indicative of a surface-erosion degradation mechanism consistent with pure PCL. Biocompatibility assessments via L929 mouse fibroblast co-culture experiments demonstrated ≥90% cell viability after 72 h, while relative red blood cell hemolysis rates remained below 5%. Collectively, these findings establish Cur-PU as a biocompatible material with tunable mechanical properties, and pH responsiveness, underscoring its translational potential for biomedical applications such as drug delivery systems and tissue engineering scaffolds. Full article

Generating adversarial examples under black-box settings poses significant challenges due to the inaccessibility of internal model information. This complexity is further exacerbated when attempting to achieve a balance between the attack success rate and perceptual quality. In this paper, we propose QTRL, a query-efficient two-phase reinforcement learning framework for generating high-quality black-box adversarial examples. Unlike existing approaches that treat adversarial generation as a single-step optimization problem, QTRL introduces a progressive two-phase learning strategy. The initial phase focuses on training the agent to develop effective adversarial strategies, while the second phase refines the perturbations to improve visual quality without sacrificing attack performance. To compensate for the unavailability of gradient information inherent in black-box settings, QTRL designs distinct reward functions for the two phases: the first prioritizes attack success, whereas the second incorporates perceptual similarity metrics to guide refinement. Furthermore, a hard sample mining mechanism is introduced to revisit previously failed attacks, significantly enhancing the robustness and generalization capabilities of the learned policy. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that QTRL achieves attack success rates comparable to those of state-of-the-art methods while substantially reducing query overhead, offering a practical and extensible solution for adversarial research in black-box scenarios. Full article

Room-temperature rotary swaging was conducted on microalloyed high-ductility Mg-0.7Sm-0.3Zr alloy rods to investigate microstructural and mechanical variations across different swaging passes and radial positions. The results indicate that following room-temperature rotary swaging, the alloy rods exhibit a large number of tensile twins and low-angle grain boundaries, leading to significant grain refinement. After two swaging passes, the microstructure exhibits a pronounced radial gradient, characterized by progressively finer grain sizes from the core to the edge regions, with a hardness difference of 3.8 HV between the edge and the core. After five swaging passes, the grain size was refined from an initial 4.37 μm to 2.02 μm. The yield strength and ultimate tensile strength of the alloy increased from 157 MPa and 210 MPa in the extruded state to 292 MPa and 302 MPa, respectively. This observed strengthening is primarily attributed to grain refinement, dislocation hardening, and texture strengthening, with grain refinement playing the dominant role. The grain refinement process during rotary swaging can be divided into two stages: in the initial stage, coarse grains are subdivided by tensile twinning; in the later stage, high-stress-induced grain boundary bulging leads to new dynamic recrystallization, further refining the microstructure. Full article

Nickel-based coatings have been demonstrated to effectively enhance the surface performance of stainless-steel components. The present study investigates the deposition efficiency and quality of Colmonoy 227-F nickel alloy coatings on AISI 304 stainless steel using direct energy deposition (DED). The work focuses on the relationships between process parameters, microstructural features, and mechanical properties. A total of sixteen process parameter combinations were studied, varying laser power and scanning speed to establish optimal deposition conditions and to evaluate coating morphology, surface topology, dilution behavior, and mechanical performance. The surface geometry was analyzed using three-dimensional digital confocal microscopy. New material distribution (MD) indices were developed to quantify spatial uniformity and integrity of single coating scan tracks (CSTs) across the XY, XZ, and YZ planes. The optimal process was identified around 900 W laser power, balancing deposition efficiency and structural integrity. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) reveal a gradual compositional transition between coating and substrate. The results of the microhardness test demonstrate a consistent gradient in mechanical properties, extending from the coating to the substrate. Coatings were found to achieve a hardness level of up to 600 HK. These findings establish a new benchmark for evaluating DED high-performance coatings and offer a scalable methodology for optimizing additive manufacturing processes in surface engineering applications. Full article

This article presents a comprehensive study of the mechanical and tribological properties of detonation coatings in the NiCr-Al system. Using the detonation spraying technology, NiCr-Al homogeneous (HC) and gradient coatings (GCs) were produced, and their characteristics were determined. Modern analytical instruments were used in the course of the study. The results showed that the microhardness of the NiCr-Al GC was approximately 30% higher compared to the NiCr-Al HC. According to nanoindentation results, the elasticity modulus and nanohardness of the NiCr-Al GC were twice as high as those of the NiCr-Al homogeneous coating. Tribological tests conducted using the rotational ball-on-disk contact geometry showed that the wear rate of the NiCr-Al GC was significantly lower, while the friction coefficients of both coatings were approximately similar. According to the adhesion strength tests, the strength of the NiCr-Al GC was recorded at 38.7 ± 6.9 MPa, while that of the NiCr-Al HC was approximately 25.4 ± 3.1 MPa. High-temperature tribological tests revealed that the wear resistance of the NiCr-Al GC was 2.5 times higher than that of the NiCr-Al HC. The conducted studies demonstrated that the coating structure, particularly the distribution of elements, has a significant influence on its mechanical and tribological properties. Overall, the NiCr-Al GC exhibited superior mechanical and tribological performance. Full article

Background/Objectives: Differentiated Thyroid Cancer (DTC), comprising papillary and follicular carcinomas, is the most common type of thyroid cancer. This is highly infectious and increasing at a higher rate. Some patients experience recurrence even after undergoing successful treatment. Early signs of recurrence can be hard to identify, and the existing health care system cannot always identify it on time. Therefore, predicting its recurrence accurately and in its early stage is a significant clinical challenge. Numerous advanced technologies, such as machine learning, are being used to overcome this clinical challenge. Thus, this study presents a novel approach for predicting the recurrence of DTC. The key objective is to improve the prediction accuracy through hyperparameter optimization. Methods: In order to achieve this, we have used a metaheuristic algorithm, the whale optimization algorithm (WOA) and its modified version. The modifications that we introduced in the original WOA algorithm are a piecewise linear chaotic map for population initialization and inertia weight. Both of our algorithms optimize the hyperparameters of the Extreme Gradient Boosting (XGBoost) model to increase the overall performance. The proposed algorithms were applied to the dataset collected from the University of California, Irvine (UCI), Machine Learning Repository to predict the chances of recurrence for DTC. This dataset consists of 383 samples with a total of 16 features. Each feature captures the critical medical and demographic information. Results: The model has shown an accuracy of 99% when optimized with WOA and 97% accuracy when optimized with the modified WOA. Conclusions: Furthermore, we have compared our work with other innovative works and validated the performance of our model for the prediction of DTC recurrence. Full article

Effective trail management is essential for preventing environmental degradation and promoting sustainable recreational use. This study proposes a GIS-based, explainable machine learning framework for predicting forest trail degradation using exclusively environmental variables, eliminating the need for costly visitor monitoring data that remains unavailable in most operational forest settings. Field surveys conducted in Geumjeongsan, South Korea, classified trail segments as degraded or non-degraded based on physical indicators such as erosion depth, trail width, and soil hardness. Environmental predictors—including elevation, slope, trail slope alignment (TSA), topographic wetness index (TWI), vegetation type, and soil texture—were derived from spatial analysis. Three machine learning algorithms (Binary Logistic Regression, Random Forest, and Gradient Boosting) were systematically compared using confusion matrix metrics and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). Random Forest (RF) was selected for its strong performance (AUC-ROC = 0.812) and seamless integration with SHAP (SHapley Additive exPlanations) for transparent interpretation. Spatial block cross-validation achieved an AUC-ROC of 0.729, confirming robust spatial generalization. SHAP analysis revealed vegetation type as the most significant predictor, with hardwood forests showing higher degradation susceptibility than mixed forests. A susceptibility map generated from the RF model indicated that 40.7% of the study area faces high to very high degradation risk. This environmental-only approach enables proactive trail management across data-limited forest systems globally, providing actionable insights for sustainable trail maintenance without requiring visitor use data. Full article

Functionally graded materials (FGMs) are fabricated on Ti-6Al-4V alloy surfaces to improve insufficient surface hardness and wear resistance. Microstructure and mechanical properties and strengthening–toughening mechanisms of FGMs were investigated. The FGM cladding layer exhibits distinct gradient differentiation, demonstrating gradient variations in the nanoindentation hardness, wear resistance, and Al/V elemental composition. Molten pool dynamics analysis reveals that Marangoni convection drives Al/V elements toward the molten pool surface, forming compositional gradients. TiN-AlN eutectic structures generated on the FGM surface enhance wear resistance. Rapid solidification enables heterogeneous nucleation for grain refinement. The irregular wavy interface morphology strengthens interfacial bonding through mechanical interlocking, dispersing impact loads and suppressing crack propagation. FGMs exhibit excellent wear resistance and impact toughness compared with Ti-6Al-4V titanium alloy. The specific wear rate is 1.17 × 10⁻² mm³/(N·m), dynamic compressive strength reaches 1701.6 MPa, and impact absorption energy achieves 189.6 MJ/m³. This work provides theoretical guidance for the design of FGM strengthening of Ti-6Al-4V surfaces. Full article

Different from general underground engineering, the micro-damage prior to failure of the surrounding rock has a significant influence on the geological disposal of high-level radioactive waste. However, the quantitative research on pre-failure dilatancy damage characteristics and stress path influence of hard brittle rocks under high stress levels is insufficient currently, and especially, the stress path under simultaneous unloading of axial and confining pressures is rarely discussed. Therefore, three representative mechanical experimental studies were conducted on the Beishan granite in the pre-selected area for high-level radioactive waste (HLW) geological disposal in China, including increasing axial pressure with constant confining pressure (path I), increasing axial pressure with unloading confining pressure (path II), and simultaneous unloading of axial and confining pressures (path III). Using the deviatoric stress ratio as a reference, the evolution laws and characteristics of stress–strain relationships, deformation modulus, generalized Poisson’s ratio, dilatancy index, and dilation angle during the path bifurcation stage were quantitatively analyzed and compared. The results indicate that macro-deformation and the plastic dilatancy process exhibit strong path dependency. The critical value and growth gradient of the dilatancy parameter for path I are both the smallest, and the suppressive effect of the initial confining pressure is the most significant. The dilation gradient of path II is the largest, but the degree of dilatancy before the critical point is the smallest due to its susceptibility to fracture. The critical values of the dilatancy parameters for path III are the highest and are minimally affected by the initial confining pressure, indicating the most significant dilatancy properties. Establish the relationship between the deformation parameters and the crack-induced volumetric strain and define the damage variable accordingly. The critical damage state and the damage accumulation process under various stress paths were examined in detail. The results show that the damage evolution is obviously differentiated with the bifurcation of the stress paths, and three different types of damage curve clusters are formed, indicating that the damage accumulation path is highly dependent on the stress path. The research findings quantitatively reveal the differences in deformation response and damage characteristics of Beishan granite under varying stress paths, providing a foundation for studying the nonlinear mechanical behavior and damage failure mechanisms of hard brittle rock under complex loading conditions. Full article

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios. Therefore, this paper proposes a model based on YOLOv8, namely HGCS-Det, for detecting garbage in complex scenarios. The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. Additionally, to weigh the attention-feature contributions, an Attention Feature Fusion module is employed to complement the attention weights of each channel. Subsequently, an Instance Boundary Reinforcement module is established to capture the fine-grained features of garbage by combining strong gradient information with semantic information. Finally, the Slide Loss function is applied to dynamically weight hard samples arising from the complex detection environments to improve the recognition accuracy of hard samples. With only a slight increase in parameters (3.02M), HGCS-Det achieves a 93.6% mean average precision (mAP) and 86 FPS on the public HGI30 dataset, which is a 3.33% higher mAP value than from YOLOv12, and outperforms the state-of-the-art (SOTA) methods in both efficiency and applicability. Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. These characteristics significantly improve its practical applicability, making the model well suited for deployment in embedded devices and real-world garbage classification systems. This method can serve as a valuable technical reference for the engineering application of garbage classification. Full article