Search Results (1,100)

Extracting causal relations from complex dynamic systems has become an appealing topic for decades, especially for machine design engineering, industrial manufacturing, and equipment maintenance, which usually suffer from a large number of tangled relationships. Although many causality detection methods have been utilized, evaluating and choosing appropriate methods, and developing proper workflow remain challenges. In this paper, a causal analysis workflow designed to detect hidden patterns involved with mechanical mechanisms is presented. In particular, various causality measures are ensembled, enabling the search for refined causal mechanisms, the impact of constitutive law, and spatial distribution of causality from the entangled raw network. Based on numerical experiments, several beneficial conclusions can be drawn: Separating typical stages is necessary for a complex process; The constitutive property has a great impact on causal inference; The discrepancy of causality among different locations of monitor points mainly depends on whether it is near the fixed boundary, near to the load, or in contact with friction; Granger Causality is suitable for discovering linear dependencies among material, load, and geometry, while constraint-based and score-based algorithms excel in identifying nonlinear causality in metal plasticity, severe discontinuity in contact, impulsive dynamic load, or damping phenomenon. Full article

The goal of the present paper is to apply a novel biomimetic design strategy for the analysis, emulation, and technical evaluation of design solutions inspired by the morphogenetic logic of the walnut shell microstructure. The shell consists of specialized cells, called sclereids, which develop protrusions and mechanically interlock with neighboring cells, providing exceptional toughness through increased surface contact. To extract and transfer this biological principle, a generative algorithm was developed using the evolutionary solver Galapagos within the Grasshopper visual programming environment. The algorithm generates protrusions on the interfaces of structural blocks and optimizes their contact surface area while maintaining constant block volume. Additional design constraints, including symmetry and manufacturability considerations, were introduced to improve structural performance and computational efficiency. A series of physical specimens with variations in key geometric parameters, such as protrusion number and height, were fabricated using fused filament fabrication (FFF) with PLA material and evaluated through in-plane and out-of-plane three-point bending tests. The results show that increasing the number of protrusions significantly enhances mechanical performance, while increasing their height improves stiffness and interlocking up to a certain threshold, beyond which structural performance decreases due to stress concentration effects. This behavior can be attributed to improved load transfer and stress distribution across the enlarged interfacial area, as well as progressive mechanical engagement between complementary protrusions. The computational model is in good agreement with the experimental results, confirming the validity of the proposed approach. The study demonstrates that biomimetic optimization of interfacial geometry can enhance the mechanical behavior of interlocking systems and provides a framework for translating biological morphogenetic principles into engineering design applications. Full article

The development of high-performance composite supercapacitors remains challenging because the specific capacitance of composite electrodes is jointly governed by electronic percolation, ion accessibility, and interfacial contact, all of which are strongly affected by the balance among active materials, conductive agents, and binders. Traditional equivalent circuit modeling and empirical trial-and-error methods are often inadequate for describing these non-linear relationships and optimizing electrode design. To address this limitation, we establish a physics-guided and interpretable machine learning (ML) framework for predicting the specific capacitance of composite electrodes. Unlike traditional methods that rely on macroscopic mass fractions, our approach constructs a feature space comprising ten descriptors, including two newly introduced binder-related proxy descriptors—Binder-to-Conductive Ratio (BCR) and Specific Binder Loading (SBL)—to better represent the influence of binder content. By systematically evaluating 17 machine learning algorithms on a high-fidelity dataset, we identify the XGBoost model, optimized via Bayesian optimization, as the best predictor, achieving a coefficient of determination (R²) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Angular misalignment of spherical roller bearings in wind turbine main shafts is a known cause of premature failure. Manufacturing and assembly tolerances introduce unavoidable variability in this misalignment—a source of uncertainty typically neglected in deterministic life models, thereby creating a gap between installation quality and system reliability. A probabilistic framework combining a Hertzian contact model, the Ioannides–Harris fatigue theory, and Monte Carlo simulation is developed to predict the fatigue life of double-row spherical roller bearings under uncertain misalignment. The sensitivity of eight geometric parameters, selected based on manufacturing tolerances, is quantified using Sobol indices for global sensitivity analysis, allowing their relative importance to be ranked. Application to a 950-series wind turbine main bearing under nominal and extreme loads shows that even with centered installation a non-negligible failure probability persists under nominal conditions. The strongly asymmetric bearing response requires asymmetrical installation tolerances to ensure high reliability. Global sensitivity analysis identifies the misalignment angle as the dominant source of uncertainty, followed by the roller contour radius. Under extreme loads, the bearing is under-dimensioned relative to the 20-year design life required for wind turbine main bearings, leading to a fatigue failure probability that approaches unity regardless of installation quality. The interaction between misalignment and radial clearance becomes pronounced under extreme overloads. Overall, the proposed framework provides a quantitative basis for reliability-based tolerance specification and emphasizes the necessity of considering the full load spectrum—including assembly variability—in bearing design. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

Background/Objectives: The medial meniscus is crucial for load transmission and knee stability. Meniscal tears disrupt joint biomechanics, increasing the risk of cartilage degeneration. However, few studies have quantitatively compared how different tear types affect stress and contact mechanics using finite element analysis (FEA). This study aims to analyze stress distributions for various meniscal tear types and develop a predictive model for meniscal stress behavior. This study investigates how stress distributions differ between healthy and torn medial menisci under identical loading conditions. The study examines which meniscal tear type produces the highest stress concentrations. The effects of different tear types on penetration, gap formation, pressure distribution, and sliding distance at the meniscus interface are also analysed. Materials and Methods: The FEA model of the knee joint, including femoral and tibial cartilage and the medial meniscus, was developed. Simulations were conducted for a healthy meniscus and for menisci with radial, horizontal and complex tears. Stress, penetration, gap, pressure, and sliding distance were calculated, and a mathematical model describing their relationships was established. Results: All torn menisci exhibited significantly higher stresses than the healthy meniscus (p < 0.001). Radial tears generated the highest stress concentrations (p < 0.001). Pressure was mainly influenced by meniscal geometry, while the gap remained nearly constant. Penetration increased slightly (p < 0.05). The predictive model demonstrated a strong correlation between meniscal stress and interface parameters (R² > 0.9). In a healthy meniscus, stress distribution is homogeneous (≈26 MPa). Stress concentration increases depending on the tear type: limited in a horizontal tear (≈26.5 MPa), significant in a vertical tear (≈30.8 MPa), and highest in a radial tear (≈40.6 MPa). These results indicate that as the tear progresses, the load-bearing capacity of the meniscus decreases, and stresses concentrate at the tear edges. Conclusions: Meniscal tears, especially radial ones, substantially alter knee biomechanics and elevate tissue stress. These biomechanical insights highlight the importance of early diagnosis and targeted rehabilitation strategies to prevent further cartilage damage and osteoarthritis progression. Full article

Rolling bearing simulations are often too computationally expensive for early design decisions, because many simulations are required in a large design of experiments. Therefore, the aim of this systematic literature review is to provide an overview of how machine learning (ML) is used to integrate engineering knowledge in advance when simulations are the primary data source for supervised learning. In the 11 included studies, ML is mainly applied as regression models trained on simulation data to replace repeated solver calls. The applications can be classified into three domains—contact mechanics, lubrication, and dynamics—mostly linked to their domain specific outputs. In most cases, ML models replace the simulation once the model is trained and validated, followed by optimization, which is often performed on the surrogate using evolutionary algorithms. Surrogates have the potential to enable design-space exploration, sensitivity analysis, and uncertainty propagation, but this capability is not yet fully exploited in current practice. The purpose of this review article is to provide a summary of methodological building blocks and practical guidelines to assist researchers and engineers in selecting appropriate ML workflows for simulation-based analysis of rolling bearings in the areas of tribology, dynamics, service life, load capacity, and system-level investigations. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

In response to the problem that the gears for offshore wind power are prone to cyclic stress and pitting damage under specific conditions, a finite element analysis method was adopted to establish numerical models for the distribution of cyclic stress on the gears and the dynamic expansion of pitting. Based on the material properties of ASTM5140 alloy structural steel, simulations were conducted using ANSYS 2024 R1 for contact stress analysis during gear meshing and COMSOL 6.3 for the evolution of pitting in a corrosive environment over a 120-h period. The results showed significant stress concentration in the tooth root fillet area under cyclic loads, with a maximum equivalent contact stress of 2.838 × 10⁸ Pa, which was identified as the key region for fatigue damage. Based on the simulated stress amplitude and material fatigue parameters, the predicted fatigue life of the gear under typical offshore operating conditions was approximately 13.3 years. In the corrosive environment, pitting pits exhibited an accelerating expansion trend, with pit volume increasing by approximately 125% and internal surface area by approximately 54% over 120 h. The volume growth followed a cubic polynomial, and the surface area growth followed a quadratic polynomial over time. These research results provide a quantitative basis for fatigue life assessment and corrosion protection design of offshore wind power gears. Full article

Traditional homogeneous materials often face an inherent trade-off between strength and toughness, restricting their application in high-performance impact protection. Mechanical metamaterials overcome this fundamental limitation by integrating structure and material. The 3D-printed Bouligand laminates (3DPBLs), a type of mechanical metamaterial, are renowned for their exceptional impact resistance. While the 3DPBLs have been proven to provide superior resistance under normal impact, actual service conditions inevitably involve complex, multi-directional loading. We aimed to investigate the 3DPBLs’ oblique impact resistance here. To this purpose, samples of 3DPBLs with varying helical angles (0°, 7°, 15°, 60°, 90°) were fabricated and subjected to low-velocity drop-weight impact tests at impact angles of 0°, 30°, 45°, and 60° to evaluate their damage evolution and energy dissipation. The experimental investigation exhibited distinct temporal evolutions of contact forces, with the 15° helical configuration identified as the optimal design. Further numerical analysis using a finite element model (validated with a deviation < 10%) is conducted to simulate performance under diverse impact angles in order to validate the reasonability of the experimental investigation. Mechanistically, 3DPBLs enhance impact resistance by increasing fracture tortuosity through their periodically rotated layered structure. These findings establish a theoretical foundation for developing high-performance, lightweight, and toughened protective materials. Full article

Rotator cuff tendon injury (RCTI) is aggravated by the pro-inflammatory milieu elicited by TLR4 and TREM1 signaling. Hence, tendon tissue engineering approaches require considerations that address these inflammatory episodes to benefit active regenerative responses. The objective of this study was to engineer and evaluate the immunocompatibility of a tendon-mimetic hydrogel composed of a chitosan–polyvinyl alcohol (PVA) blend incorporated with Collagen-I and to assess LR12 delivery for addressing TREM1-driven inflammation in RCTI management. A chitosan–PVA-HEMA-Acrylic acid (CPHA) hydrogel was synthesized by blending the linear natural polysaccharide chitosan and linear synthetic polymer PVA in an aqueous phase, followed by incorporation and redox chain growth with HEMA using acrylic acid (AA). Interpenetration of Collagen-I in CPHA yielded the CPHA-C hydrogel. CPHA and CPHA-C hydrogels displayed ample surface functional moieties provided by the co-polymers, exhibited excellent porosity as revealed by SEM imaging (28.65 ± 6.85 and 41.56 ± 18.00, respectively, for CPHA and CPHA-C), and were amphiphilic, as evident by contact angle analysis (~70 for CPHA and CPHA-C). Both hydrogels displayed a progressive release profile for the TREM1-inhibitory peptide LR12 for 7 days, whereas the LR12-loaded CPHA hydrogel exhibited increased TREM1 inhibition in LPS-challenged RAW264.7 macrophages. CPHA and CPHA-C hydrogels were immunocompatible and masked the oxidative damage in RAW264.7 macrophages, as evident by decreased levels of mitochondrial superoxide and ROS. Additionally, the CPHA hydrogel displayed an increased TGFβ/TLR4 ratio (0.24), whereas the CPHA-C (−0.52) system showed a decreased ratio upon exposure to tenocytes and macrophages. Overall, the findings highlight the potential of CPHA and CPHA-C hydrogels as candidates for tendon regenerative applications. Full article

Three-dimensional printed polymers produced using Fused Deposition Modelling (FDM) exhibit directional microstructures resulting from filament paths, layer interfaces, and cellular infill, leading to mechanical and tribological responses distinct from those of homogeneous bulk materials. This study presents a comparative tribomechanical evaluation of polypropylene (PP) bulk inserts and 3D-printed polyethylene terephthalate glycol (PETG) inserts with a 30% hexagonal infill, relevant for robotic gripping applications. Progressive scratch tests were performed under loads from 5 to 100 N (150 N for PP), and profilometry was applied to quantify groove morphology, ridge formation, and displaced-volume ratios. An elasto-plastic conical indentation model was used to derive indentation pressures and elastic–plastic transition radii from groove geometry. The PETG inserts exhibited heterogeneous groove depth, intermittent ridge tearing, and friction fluctuations associated with the internal infill structure, consistent with previous findings on anisotropy and architecture-dependent behaviour in additively manufactured polymers. In contrast, bulk PP demonstrated smoother friction profiles and more stable plastic flow under increasing loads. Two functional indices—specific frictional work and ridge-to-trace volumetric ratio—are introduced to support material selection for robotic gripping systems. The results show that local contact mechanics in 3D-printed inserts are governed by print-induced structural features and can be effectively evaluated through a scratch-based elasto-plastic analysis. The methods and results presented in this work support the rational selection and design of polymer inserts for robotic gripper fingertips. The proposed scratch-based elasto-plastic evaluation framework enables manufacturers and automation engineers to compare 3D-printed and conventional materials based on friction stability, wear response, and deformation resistance. This approach can be directly applied to optimise gripping performance in industrial handling, packaging, and collaborative robotics. Full article

Starved lubrication poses a critical challenge to hybrid ceramic bearings operating under severe conditions. This study investigates the tribological behavior of carburized 20CrMo steel sliding against Al₂O₃ ceramic balls and GCr15 steel balls under dry sliding, with oil-lubricated tests as a reference. Under oil lubrication, the 20CrMo/Al₂O₃ pair exhibits superior wear resistance, attributed to the high hardness of the ceramic counterpart. Under dry sliding, however, this pair shows a slightly lower friction coefficient but a wear rate approximately three times that of the 20CrMo/GCr15 pair. This counterintuitive behavior stems from two mechanisms: lower contact stress and friction-induced work hardening in the GCr15 pair, which together suppress wear. Further analysis reveals that secondary carbides in the carburized layer detach under repeated high shear stress, acting as hard third-body abrasives and accelerating surface damage. These findings highlight that hybrid ceramic bearings are more susceptible to lubrication failure than all-steel bearings. Under heavy loads and poor lubrication, residual compressive stress plays a key role in governing the tribological behavior of carbides on carburized surfaces. Full article

As the most prevalent type of existing building in China, masonry structures are susceptible to cracking due to the low tensile strength of the masonry material. In the event of a sudden, strong earthquake, they are highly prone to brittle collapse, leaving occupants little time and space to escape. Based on this, combining the advantages of the elastoplastic mechanical theory and the nonlinear finite element (FE) method, this study adopts different modeling methods: integral modeling (IM), contact element discrete modeling (CEDM), spring element discrete modeling (SEDM), and co-node discrete modeling (CNDM). FE models of unreinforced masonry walls (UMWs) are established, respectively, and a monotonic pushover mechanical performance analysis is carried out. The accuracy of the adopted modeling methods is verified against existing test results for UMW specimens. Through parametric analysis of aspect ratios (0.5, 0.75, 1.0, and 1.25), axial compression ratios (0.1, 0.3, 0.5, 0.7, and 0.8), and mortar strengths (M5, M7.5, and M10), the characteristic mechanical performance factors of UMWs are determined. A novel strength index is proposed to discriminate between failure modes and elucidate the damage mechanism of UMWs. The results indicate that the ultimate load and its corresponding displacement change systematically with variations in aspect ratios, axial compression ratios, and mortar strengths. Furthermore, integrating stress cloud maps with the proposed strength index provides a quantitative basis for discriminating between flexural and shear failure modes in UMWs. All four modeling methods can, to varying degrees, capture the pushover behavior of UMWs, and quantifiable selection schemes are provided to balance analysis accuracy and computational cost. The analytical methods and findings presented in this work can be applied to performance assessment, seismic design, and engineering practice of UMWs. Full article