Search Results (576)

The waviness defect, a common manufacturing flaw in composite structures, can significantly impact the mechanical performance. This study investigates the effects of wrinkles on the ultimate load and failure modes of two Carbon Fiber Reinforced Composite (CFRC) laminates through compressive experiments and simulation analyses. The laminates have stacking sequences of [0]_10S and [45/0/−45/90/45/0/−45/0/45/0]_S. Each laminate includes four different waviness ratios (the ratio of wrinkle amplitude to laminate thickness) of 0%, 10%, 20% and 30%. In the simulation, a novel multiaxial progressive damage model is implemented via the user material (UMAT) subroutine to predict the compressive failure behavior of wrinkled composite laminates. This multiscale analysis framework innovatively features a 7 × 7 generalized method of cells coupled with stress-based multiaxial Hashin failure criteria to accurately analyze the impact of wrinkle defects on structural performance and efficiently transfer macro-microscopic damage variables. When any microscopic subcell within the representative unit cell (RUC) satisfies a failure criterion, its stiffness matrix is reduced to a nominal value, and the corresponding failure modes are tracked through state variables. When more than 50% fiber subcells fail in the fiber direction or more than 50% matrix subcells fail in the transverse or thickness direction, it indicates that the RUC has experienced the corresponding failure modes, which are the tensile or compressive failure of fibers, matrix, or delamination in the three axial directions. This multiscale model accurately predicted the load–displacement curves and failure modes of wrinkled composites under compressive load, showing good agreement with experimental results. The analysis results indicate that wrinkle defects can reduce the ultimate load-carrying capacity and promote local buckling deformation at the wrinkled region, leading to changes in damage distribution and failure modes. Full article

This study presents a comparative investigation of the mechanical performance of epoxy-based composites reinforced with ±45° multiaxial non-crimp fabrics (NCFs) made from natural flax fibers and conventional glass fibers. Flax fibers, despite their attractive sustainability profile and favorable specific mechanical properties, are typically processed into twisted yarns for textile reinforcement, which compromises fiber alignment and reduces composite performance. A novel yarn-free flax NCF was developed using false twist stabilization of aligned slivers to eliminate the negative effects of yarn twist. Composite laminates were manufactured via vacuum-assisted resin infusion (VARI) under identical processing conditions for both flax- and glass-based reinforcements and tested for tensile, compressive, and flexural behavior. The results show that, although glass fiber composites exhibit superior absolute strength and stiffness, flax-based NCF composites offer competitive specific properties and benefit significantly from improved fiber alignment compared to yarn-based variants. This work provides a systematic comparison under standardized conditions and confirms the mechanical feasibility of flax NCFs for semi-structural lightweight applications. Full article

The huge growth in the utilisation of six-axis robots in various technological applications in production calls for a detailed focus on the process of preparing Numerical Control (NC) programmes for effective robot control. Considerable attention is currently being paid to optimisation by increasing stiffness, but there is also a need to focus on reducing energy consumption in robot control. Focusing on reducing energy consumption is highly justified given the widespread adoption of robotic systems across diverse manufacturing technologies and the significant potential for application. This is particularly relevant today, when minimising production costs is a critical industrial objective. A redundant degree of freedom—which is the possibility to rotate around the end-effector axis and thus influence the adjustment of the rotation of the individual robot joints—can be used for this purpose. Therefore, this paper exploits this redundant degree of freedom to set up a proper robot configuration that reduces energy consumption. The user-friendly solution, including the algorithm design and processing through a function, could be effectively implemented within an industry-standard post-processor solution for generating NC programmes for robots. This solution is unique as it is used for the optimisation of the working section of the toolpaths, where continuous control of the end-effector movement during manufacturing operations occurs. The solution was verified on a KUKA KR60 HA robot; however, it is applicable to any industrial six-axis robot. Substantial energy savings were obtained in multi-axis toolpath operations, with a 7.5% reduction in total energy consumption when using the optimised NC programme. Full article

Road event detection plays a key role in tasks such as monitoring, anomaly identification, and urban traffic optimization. Conventional methods often rely on feature extraction and classification or classical machine learning models, which may struggle when processing high-frequency signals in real time. In this work, we propose a CNN-based classification approach designed to handle multi-axial acceleration and vibration signals captured from road scenarios. Instead of relying on static feature sets, our method leverages a convolutional neural network architecture capable of automatically learning discriminative patterns from raw sensor data. We structure the time-series input into temporal windows and use it to train models that can identify different event categories, including “Speed Bumps”, “Potholes”, and “Sudden Braking” events. The experimental results show that our approach achieves an accuracy of 93.51%, with a precision of 93.56% and a recall of 93.51%. Further, the average AUC score of 0.9855 confirms the strong discriminative power of our proposal. In contrast to rule-based methods, which require frequent tuning to adapt to new datasets, our approach generalizes better across different road conditions and offers a practical alternative for real-time deployment in dynamic environments, outperforming rule-based approaches by over 10% in F1-score, while preserving deployment efficiency on embedded hardware. Full article

Most existing deep learning-based camera pose estimation methods rely solely on visible images, making them vulnerable to challenges such as drastic lighting variations and poor nighttime imaging quality. These limitations reduce the robustness and accuracy of traditional approaches. To address these issues, we propose VIFPE (Visible- and Infrared-Fusion-Based Pose Estimation), a novel framework that integrates features from both visible and infrared images to enhance pose estimation in urban environments. By leveraging the complementary advantages of visible images, which are rich in texture, and infrared images, which are resilient to illumination changes, VIFPE achieves robust and accurate localization across diverse lighting conditions. The framework consists of multiple key modules, including a feature extractor, a multimodal fusion module, a pose regressor, and a multi-task learning strategy. Specifically, VIFPE employs Multi-axis Vision Transformer (MaxViT) encoders to extract features from both modalities, which are then fused in a shared feature space through a cross-modal fusion module. A pose regression network is subsequently trained to estimate the camera’s position and orientation based on the fused representation. Our experimental results on diverse urban datasets demonstrate that VIFPE significantly outperforms conventional methods in terms of both accuracy and robustness, particularly under challenging lighting conditions. This work underscores the potential of multimodal fusion for camera localization in large-scale urban environments and lays a foundation for future advancements in the field. Full article

A novel multi-degree-of-freedom bionic Soft Pneumatic Actuator (SPA) inspired by the shoulder joint of a sea turtle is proposed. The SPA is mainly composed of a combination of oblique chamber actuator units capable of omnidirectional bending and bi-directional twisting, which can restore the multi-modal motions of a sea turtle’s flipper limb in three-dimensional space. To address the nonlinear behavior of the complex structure of SPA, traditional modeling is difficult. The attitude information of each axis of the actuator is extracted in real time using a high-precision Inertial Measurement Unit (IMU), and the attitude outputs of the SPA are modeled using six machine learning methods. The results show that the XGBoost model performs best in attitude modeling. Its R² can reach 0.974, and the average absolute errors of angles in Roll, Pitch, and Yaw axes are 1.315°, 1.543°, and 1.048°, respectively. The multi-axis attitude of the SPA can be predicted with high accuracy in real time. The studies on deformation capability, actuation output performance, and underwater validation experiments demonstrate that the SPA meets the bionic sea turtle shoulder joint requirements. This study provides a new theoretical foundation and technical path for the development, control, and bionic application of complex multi-degree-of-freedom SPA systems. Full article

Aviation contrails significantly impact climate via radiative forcing, but their segmentation in thermal infrared satellite images is challenged by thin-layer structures, blurry edges, and cirrus cloud interference. We propose MFcontrail, a deep learning model integrating multi-axis attention and frequency-domain enhancement for precise contrail segmentation. It uses a MaxViT encoder to capture long-range spatial features, a FreqFusion decoder to preserve high-frequency edge details, and an edge-aware loss to refine boundary accuracy. Evaluations on OpenContrails and Landsat-8 datasets show that MFcontrail outperforms state-of-the-art methods: compared with DeepLabV3+, it achieves a 5.03% higher F1-score and 5.91% higher IoU on OpenContrails, with 3.43% F1-score and 4.07% IoU gains on Landsat-8. Ablation studies confirm the effectiveness of frequency-domain enhancement (contributing 69.4% of IoU improvement) and other key components. This work provides a high-precision tool for aviation climate research, highlighting frequency-domain strategies’ value in satellite cloud image analysis. Full article

This study proposes an integrated fatigue life assessment methodology to accurately evaluate the time-domain evolution in tubular joint fatigue damage in offshore wind turbine (OWT) jacket structures under long-term combined wind and wave actions. A customized post-processing module was developed via secondary development on the MLife platform, employing a conditional probability distribution model to perform joint probabilistic modeling of measured marine environmental data, thereby establishing a long-term joint wind–wave distribution database. The reconstruction of hotspot stress time histories at the tubular joints was achieved through a hybrid analytical–numerical approach, integrating analytical formulations of nominal stress with a multi-axial stress concentration factor (SCF) matrix. Long-term fatigue damage assessment was implemented using the Palmgren–Miner linear cumulative damage hypothesis, where a weighted summation methodology based on joint wind–wave probability distributions rigorously accounted for the statistical contributions of individual design load cases. An ultimate bearing capacity analysis was also conducted based on S-N fatigue endurance characteristic curves. This research specifically investigates the influence mechanisms of tuned mass dampers (TMDs) on the time-domain-coupled fatigue performance of tubular joints subjected to long-term combined wind and wave loads. Numerical simulations demonstrate that parametrically optimized TMD systems significantly enhance the fatigue life metrics of critical joints in jacket structures. Full article

Compressor blades are subjected to multiaxial loads during operation. Using uniaxial life prediction formulas to predict their fatigue life can result in significant errors. Therefore, by analyzing the loading conditions of the blades, a fatigue life prediction model suitable for compressor blades was developed. This model was established by applying the load of a specific engine type to a notched bar specimen and considering the gradient and strengthening effects. Firstly, the parameters of the SWT model were used as the damage parameters to determine the critical plane location based on the principle of coordinate transformation, and these results were compared with the actual fracture angles. Additionally, the physical mechanisms of multiaxial fatigue crack initiation and propagation were investigated at the microscopic level. Secondly, the non-uniform stress field on the critical plane was obtained using the finite element method. The stress distribution from the critical point to the specimen’s principal axis was extracted and normalized to calculate the equivalent stress gradient factor. Finally, the results of the comprehensive fatigue life prediction model were computed. Comparisons between the calculated results of the proposed model, the SWT model, and the Shang model with the experimental fatigue life showed that the prediction accuracy of the proposed model is higher than that of the SWT model and the Shang Deguang model. Full article

Fiber-reinforced composites are widely used in aerospace and other fields due to their excellent specific strength, specific stiffness, and corrosion resistance, and further study of their failure criteria is essential to improve the accuracy and reliability of failure behavior prediction under complex loads. There are still some limitations in the current composite failure criterion research, mainly reflected in the lack of promotion of three-dimensional stress state, lack of sufficient consideration of multi-modal coupling effects, and the applicability of the criteria under multiaxial stress and complex loading conditions, which limit the wider application of composites in the leading-edge fields to a certain degree. In this work, a generalized Mohr failure envelope function approach is adopted to obtain the stress on the failure surface as a power series form of independent variable, and the unknown coefficients are determined according to the damage conditions, to extend the Chang–Chang criterion to the three-dimensional stress state, and to consider the coupling effect between the fiber and matrix failure modes. The modified Chang–Chang criterion significantly enhances the failure prediction accuracy of composite materials under complex stress states, especially in the range of multi-axial loading and small off-axis angles, which provides a more reliable theoretical basis and practical guidance for the safe design and performance optimization of composite structures in aerospace and other engineering fields. Full article

While size effects in composite structures have been widely studied under quasi-static uniaxial loading, their influence under fatigue conditions, particularly in the presence of multiaxial stress states and elevated temperatures, remains insufficiently understood. This study investigates the fatigue behaviour of thick-walled $\pm {45}^{\circ }$ braided glass fibre-reinforced polyurethane composite box structures under varying temperature and loading conditions. A combined experimental approach is adopted, coupling quasi-static and fatigue tests on large-scale structures with reference data from standardised coupon specimens. The influence of temperature (23–80 °C) and multiaxial shear–compression loading is systematically evaluated. The results demonstrate a significant temperature-dependent decrease in compressive strength and fatigue life, with a linear degradation trend that aligns closely between the box structure and coupon data. Under moderate multiaxial conditions, the fatigue life of box structures is not significantly impaired compared to uniaxial test coupon specimens. Complementary non-destructive testing using air-coupled ultrasound confirms these trends, demonstrating that guided-wave phase-velocity measurements capture the evolution of anisotropic damage and are therefore suitable for in situ structural health monitoring applications. Furthermore, these findings highlight that (i) the temperature-dependent fatigue behaviour of thick-walled composites can be predicted using small-scale coupon data and (ii) small shear components have a limited impact on fatigue life within the studied loading regime. Full article

The vertical profile of PM_2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM_2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO₂ and SO₂—to evaluate the vertical distribution of PM_2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM_2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM_2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM_2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM_2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM_2.5 mass concentration in Beijing, a high-altitude PM_2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM_2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM_2.5 every hour. Full article

Steel slag aggregate concrete (SAC) is widely recognized as a high-performance and sustainable construction material. However, its broader structural application has been impeded by the limited development of reliable constitutive models. Building upon the well-established non-uniform hardening plasticity theory, this study proposes a comprehensive theoretical framework to establish a stress–strain relationship model for SAC under complex stress states. To this end, a multiaxial elastoplastic constitutive model for SAC is developed through the following steps: (1) The Guo–Wang failure criterion is employed as the bounding surface, from which a yield criterion is formulated to capture the characteristic mechanical responses of SAC under multiaxial loading; (2) Based on fundamental plasticity theory, the stress–strain relationship is derived by integrating the proposed yield function with a non-associated flow rule using a Drucker–Prager-type plastic potential function, while ensuring consistency conditions are satisfied; (3) A parameter calibration methodology is introduced and applied using experimental data from uniaxial and multiaxial tests on SAC; (4) A numerical implementation scheme is developed in MATLAB 2024a, and the model is validated through computational simulations. The validation results confirm that the proposed model reliably captures the stress–strain behavior of SAC under complex loading conditions. Overall, this study not only delivers a robust multiaxial constitutive model for SAC, but also offers a systematic modeling approach that may serve as a reference for the further development of constitutive theories for steel slag-based concretes and their broader application in structural engineering. Full article

Accurate estimation of multiaxial fatigue life plays a critical role in maintaining the structural integrity and operational reliability of mechanical components subjected to complex loading conditions. Under non-proportional loading, fatigue life tends to decrease significantly due to the emergence of additional damage mechanisms, such as dislocation accumulation, cyclic hardening, and accelerated propagation of micro-cracks. This study conducts a systematic investigation into the primary factors that influence fatigue behavior under non-proportional loading conditions. A novel damage factor is proposed, which quantifies the additional strengthening effects caused by complex stress and strain interactions. Based on this factor, a new prediction model is developed through the combination of critical plane theory and an energy-based framework. This model captures the influence of non-proportional strengthening on fatigue strength with improved accuracy. Experimental validation is carried out using En8, TC4, and Al7050-T7451 materials under tension and torsion loading conditions. Comparative analysis with three conventional models shows that the proposed method improves the accuracy of predictions and offers a dependable approach for practical engineering applications. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article