Search Results (440)

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with sample block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

Fault diagnosis of rolling bearings in wind turbines is significantly challenged by strong noise, non-stationary signals, and multi-source interference. To address these issues, a Multi-Scale Attention Residual Graph Convolutional Network (MSAR-GCN) is proposed. First, a fully connected graph is constructed in the frequency domain using a temporal segmentation strategy, which preserves full spectral resolution and captures cross-frequency coupling features via node embeddings. Second, a multi-scale residual module with a cross-layer pyramid structure is designed to extract features at varying granularities, integrated with a dynamic multi-head attention mechanism to adaptively emphasize damage-sensitive frequency bands. Additionally, a hierarchical feature distillation mechanism is employed to compress high-dimensional features, ensuring model lightweighting while retaining critical fault information. Experimental validations on CWRU and JNU datasets demonstrate that MSAR-GCN achieves 97.02% and 92.5% accuracy under −10 dB Gaussian noise, respectively, outperforming existing methods by over 4%. Specifically, the model exhibits exceptional robustness, maintaining 93.09% accuracy under severe non-Gaussian impulsive noise. With verified feature separability and high computational efficiency, the proposed method offers a promising solution for high-precision, real-time industrial fault diagnosis. Full article

Titanium matrix composites reinforced with carbon nanotubes (CNTs) have attracted significant attention due to their potential to overcome the inherent limitations of titanium alloys in hardness, wear resistance, and strength–toughness balance. With the rapid development of additive manufacturing (AM) technologies, the integration of CNT reinforcements into titanium matrices provides new opportunities for fabricating high-performance lightweight components. This review systematically summarizes recent progress in the preparation and application of CNT-reinforced titanium matrix composites for AM. Key powder preparation strategies, including mechanical mixing, chemical coating, and in situ growth methods, are critically compared in terms of CNT dispersion uniformity, structural integrity preservation, powder flowability, and process compatibility. The influence of CNT incorporation on AM behavior and final material performance is discussed, with particular emphasis on multiscale strengthening mechanisms such as enhanced laser absorption, load transfer effects, grain refinement, and dispersion strengthening induced by TiC formation. Current challenges mainly involve achieving homogeneous CNT distribution, controlling interfacial reactions, and balancing dispersion efficiency with structural damage. Future research directions are proposed, focusing on advanced powder engineering techniques, interface regulation strategies, and deeper understanding of the relationships between processing parameters, microstructure evolution, and mechanical properties. This work provides a comprehensive reference for the design and fabrication of next-generation CNT-reinforced titanium-based materials. Full article

Dynamic covalent bonds are commonly used to maintain the self-healing properties of polyurethanes and facilitate resource recycling. However, relying on a single type of dynamic covalent bond often makes it difficult to effectively regulate both mechanical and self-healing properties across a wide temperature range. In this study, a self-synthesized chain extender containing disulfide bonds was introduced into a polyurethane system, leading to the development of a novel dual-dynamic covalent bond self-healing polyurethane (SSDA-PU). Innovatively, this SSDA-PU demonstrates self-healing properties across a wide temperature range. The successful synthesis of the chain extender and the incorporation of both disulfide bonds and Diels–Alder (DA) bonds were confirmed using FTIR and Raman spectroscopy. The physical characteristics and self-healing performance were comprehensively evaluated through multi-scale testing and characterization, including thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), hardness testing, mechanical tensile tests, and self-healing experiments. The underlying synergistic self-healing mechanism was subsequently elucidated. Findings showed that a higher R-value (isocyanate index) in SSDA-PU leads to over-crosslinking, while an R-value of 1.7 achieves the best overall mechanical performance, with tensile strength and elongation at break reaching 21.1 MPa and 755.17%, respectively. Additionally, SSDA-PU demonstrated the capacity for multiple healing cycles, with an initial self-healing efficiency of 90.38%, which remained notably high at 59.21% even after three damage-healing cycles. Importantly, SSDA-PU exhibited healing capabilities even at relatively low temperatures. Cracks in SSDA-PU can be effectively repaired through the synergistic action of disulfide bond exchange, hydrogen bond dissociation, and thermally reversible DA reactions. SSDA-PU also shows excellent recyclability, offering valuable insights for the practical engineering application of functional polyurethanes. Full article

This review examines fatigue damage in cement-based materials across the micro-, meso-, and macroscales, with emphasis on how damage initiates, transfers, and becomes structurally observable under cyclic loading. At the microscale, capillary pores, unhydrated cement particles, and the calcium–silicate–hydrate (C-S-H) phase govern local stress concentration, bond rupture, limited healing, and microcrack development. At the mesoscale, the interfacial transition zone (ITZ), cement paste, aggregates, and fiber reinforcement effects control crack initiation, deflection, bridging, and coalescence. At the macroscale, specimen size, boundary conditions, loading regime, and environmental exposure shape stiffness degradation, residual strain accumulation, crack growth, and fatigue life. Beyond summarizing existing studies, this review synthesizes a causal damage transfer interpretation that links microscale deterioration, mesoscale crack interaction, and macroscale response. Current gaps include the limited quantitative link between microstructure-informed models and three-dimensional experimental observations, the still-incomplete validation of multiscale predictive frameworks, and the insufficient treatment of coupled fatigue–environment effects. Addressing these gaps is essential for more reliable fatigue life prediction and for developing durable, resource-efficient concrete infrastructure. Full article

Friction braking is the most spread braking system in vehicles, where the morphologies of the disc and the braking pads are essential to ensure that friction reduces rotation speed efficiently. However, modern braking systems are submitted to a complex balance between functionalities: braking ability, resistance to wear, and limited noise emission, i.e., squealing. This article studies the evolution of the morphology of a braking pad in a pin-on-disc configuration to further understand its influence over surface functionalities. Data collected from a pin-on-disc tribometer, and topographies are coupled to perform a multiscale and multiphysics analysis of the braking pad surface. Relevancy of roughness parameters regarding braking ability, surface wear, pad temperature and noise emission is evaluated with a bootstrap-based relevancy analysis. Relevant scales of the pad morphological structures are identified for surface wear (446 µm), braking ability (19.5 µm), pad temperature (2717 and 446 µm) and squealing frequency (1720 and 15.7 µm). Correlations between test bench data and roughness parameters highlighted the role of wear plateaus on the braking pad surface. These plateaus are formed by the damaged surface peaks during braking or by compaction of the third body trapped across the braking pad surface. Full article

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang, eastern China. Leveraging nine years (2008–2016) of multi-source data—including high-resolution on-site meteorological observations and daily records from surrounding regional stations—we engineered a comprehensive set of predictive features capturing local microclimatic, regional synoptic, and short-term temporal dynamics. A two-stage feature selection approach, combining Spearman correlation screening with SHAP-based importance ranking, identified an optimal subset of 14 robust predictors. Among eight benchmarked models, XGBoost achieved the best performance on a chronologically held-out test set, yielding a CSI of 0.736, accuracy of 91.0%, F1-Score of 0.848 and AUC-ROC of 0.968. Ablation experiments demonstrated the added value of data integration: model performance improved from a CSI of 0.617 (using only local data) to 0.736 (with full multi-source inputs). SHAP interpretability analysis further revealed that the model’s predictions align with established frost formation physics, highlighting key drivers such as nocturnal cooling rate and regional humidity. This work demonstrates that integrating multi-scale meteorological data with interpretable machine learning offers a reliable, transparent, and operationally viable tool for frost risk management—providing actionable insights to enhance resilience in precision horticulture for perennial crops like tea. Full article

This study investigates the multi-scale tribo–thermo–viscoelastic performance of a sustainable bio-based FormuLITE epoxy reinforced with single and hybrid carbon nanofillers (0.1 wt.% total loading) under dry sliding up to 50 N. Pin-on-disk tests at 10, 30, and 50 N showed a consistent reduction in contact pressure and wear volume in the order: neat epoxy > 0.1 CNT > 0.1 GNP > 0.1 ND > 0.1 CNT/GNP > 0.1 CNT/ND > 0.1 GNP/ND. At 50 N and 1500 m sliding distance, neat epoxy exhibited a wear volume of 13.43 mm³ and contact pressure of 13.4 N/cm², while the GNP/ND hybrid reduced wear to 4.86 mm³ and contact pressure to 6.2 N/cm², corresponding to reductions of 64% and 54%, respectively. The accelerating wear coefficient decreased from 2.9 × 10⁻⁶ to 8.5 × 10⁻⁷, confirming slower damage accumulation in hybrid systems. Time-dependent contact pressure analysis revealed reduced asymptotic intensity and suppressed mid-cycle pressure spikes, indicating enhanced tribolayer stability. Effective surface hardness increased from 0.18 GPa (neat epoxy) to 0.30 GPa (GNP/ND), while normalized wear decreased from 1.00 to 0.36. Enhanced damping behavior and improved thermal conductivity in hybrid systems promoted stress redistribution and minimized flash-temperature localization. An interfacial energy-partition framework calibrated to experimental wear data quantitatively linked effective driving pressure, tribofilm stabilization, and surface hardness to material removal. The results demonstrate that wear mitigation in sustainable bio-epoxy systems is governed by coupled mechanical, viscoelastic, and thermal energy redistribution, with GNP/ND hybrids providing the most stable tribological interface under severe sliding. The findings contribute to the development of durable and sustainable bio-epoxy composite systems for engineering applications, supporting broader goals of responsible material utilization and sustainable industrial innovation aligned with the United Nations Sustainable Development Goals (SDG 9 and SDG 12). Full article

In this work, an accelerated non-uniform corrosion method controlled by time and current was employed to fabricate power-on accelerated corrosion specimens of hybrid steel fiber-reinforced concrete (HSFRC) gradient beams. Experimental research was conducted to investigate their impact resistance, revealing the dynamic response patterns of these gradient beams with varying steel fiber contents. By analyzing the evolutionary characteristics of impact load, displacement, energy dissipation, equivalent impact bearing capacity, and dynamic amplification factor, the influence of steel fibers with different sizes and contents on the bearing capacity degradation and mechanical properties of HSFRC gradient beams under the same corrosion conditions was clarified. The synergistic enhancement mechanism of multi-scale steel fibers in the beams was elucidated, highlighting the complementary effects of long fibers and short fibers at different stages of material damage. Results show that the incorporation of steel fibers can effectively improve the impact resistance of reinforced concrete gradient beams, with a maximum improvement of approximately 2.5 times. Compared with gradient beams reinforced with single long fibers, the peak impact force of HSFRC gradient beams increases by about 16%, and different steel fiber ratio plays a significant role in regulating impact resistance. Within the corrosion range of 3% to 5%, the equivalent impact bearing capacity of gradient beams is negatively correlated with the reinforcement corrosion rate. Full article

The use of ultrasonic guided waves (UGWs) is an efficient damage monitoring technique. Due to their characteristics of a wide monitoring range and low power consumption, UGWs have been widely applied in various structural health monitoring fields. In practice, the transducers and coupling agents used for UGW excitation and reception are prone to failure due to service environmental factors, resulting in abnormal UGW signals. To ensure reliable damage monitoring, this paper proposed an abnormal UGW signal identification method based on the UGW reconstruction errors. First, a multi-scale progressive reconstruction network (MPRN) is proposed to accurately reconstruct normal UGW signals. Leveraging the inherent differences between normal and anomalous UGW signal characteristics, the reconstruction errors increase significantly when abnormal UGW signals are input into the MPRN, which has been trained exclusively on normal data. This discrepancy in reconstruction errors enables the identification of abnormal signals. The experimental results show that sensor failure causes frequency shifts in the received UGW signals. When reconstructing normal UGW signals, the proposed MPRN achieves high fidelity, with an average NRMSE as low as 0.0036 and an average PSNR as high as 40.04 dB. In contrast, when reconstructing abnormal UGW signals, the average NRMSE is no lower than 0.62, and the average PSNR is no higher than 16.67 dB. The proposed reconstruction-error-based abnormal UGW signal identification method achieves a maximum accuracy of 93.43%. Full article

Physics-based constitutive modelling remains a cornerstone for predicting ductile damage and fracture in metallic materials, particularly where microstructural mechanisms govern macroscopic response. Over the past two decades, a wide range of crystal plasticity, porous plasticity, and void-based fracture models have been proposed to capture deformation localisation, void growth, and coalescence under complex loading paths. However, these developments are often presented in isolation, obscuring their shared physical assumptions and limiting their transferability across material systems and length scales. This article provides a microstructure-sensitive perspective on the constitutive modelling of ductile damage and fracture, with particular emphasis on crystal plasticity-based frameworks, void growth and coalescence mechanisms, and interface-driven fracture. Rather than attempting an exhaustive review, this review highlights the unifying concepts, modelling trade-offs, and recurring challenges related to parameter identifiability, scale bridging, and predictive robustness. It further clarifies how physics-based constitutive descriptions can be systematically integrated into modern fatigue and fracture assessments and situates these developments relative to emerging data-assisted and machine-learning-enhanced modelling strategies. By reframing established constitutive models within a coherent physical narrative, this perspective aims to support more transparent model selection, improve interpretability, and guide future developments in the multiscale damage and fracture modelling of metallic materials. While these frameworks offer enhanced microstructure sensitivity, their parameter richness and experimental calibration demand currently limit widespread industrial deployment, motivating ongoing work on reduced-order and data-assisted variants. Full article

To address the critical challenge of drilling fluid invasion in deep coalbed methane (CBM) reservoirs, this study provides novel insight into the micro-scale sealing mechanism and pore structure evolution by leveraging Low-Field Nuclear Magnetic Resonance (LF-NMR) as a quantitative probe. Unlike traditional macroscopic evaluations, we utilized dynamic NMR T₂ spectral analysis to decipher the synergistic behavior of a proposed “Bridging–Filling–Densifying” ternary sealing system, which integrates a nano-plugging agent, micro-fillers, and size-matched skeletal agents. The results demonstrate a significant improvement in sealing efficiency. The optimized hierarchical architecture reduced the NMR signal intensity of the invaded cores by over 99.8% under a differential pressure of 10 MPa, effectively eliminating fluid invasion channels. Crucially, the study reveals that while multi-scale particle size matching is the precondition for sealing, the mechanical rigidity of the skeletal particles is the determinant for maintaining filter cake integrity against high-pressure deformation. These findings elucidate the transition from a “macropore-dominated” structure to a “zero-detectable” sealed state, establishing a robust theoretical framework for designing non-damaging drilling fluids tailored to the complex geomechanics of deep CBM exploration. Full article

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas. Full article

With the rapid development of the aviation industry, engines operate under extreme conditions of high temperature, high pressure, and high vibration, making them prone to surface damage such as ablation. Ablation not only affects the structural integrity of engine components but also threatens flight safety, making efficient and accurate detection of paramount importance. Traditional detection methods rely on manual visual inspection and non-destructive testing, which suffer from high subjectivity and low efficiency. In recent years, deep learning has achieved significant progress in industrial defect detection. However, conventional CNN-and Transformer-based architectures still suffer from substantial computational overhead and inadequate boundary segmentation accuracy in aero-engine ablation detection. This paper proposes a novel dual-pathway network Visual State-Space Residual Neural Network (VSS-ResNet) based on Mamba that combines Visual State Space (VSS) modules with ResNet50. This architecture leverages the global modeling capability of VSS modules and the local feature extraction capability of CNNs, effectively enhancing the accuracy and robustness of ablation boundary detection with the support of multi-scale feature fusion modules. Experimental results demonstrate that the proposed method achieves superior performance in mIoU, mPA, and Acc compared to mainstream segmentation models such as U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLab V3+ on a self-constructed engine endoscopic ablation dataset, validating its potential in intelligent aero-engine inspection. Full article