Search Results (544)

Six different twin boundary interface models were constructed by molecular dynamics simulations to investigate the effect of biaxial load ratio on the fracture behavior of titanium twin boundaries. Analysis of microstructural evolution indicates that twin boundaries exhibit a dual role during crack propagation. On one hand, they serve as preferential sites for void nucleation, promoting crack propagation along the twin boundary; on the other hand, they provide favorable sites for dislocation nucleation, inducing local plastic deformation at the crack tip, altering the crack path, and thereby hindering crack propagation. The crack propagation behavior in the ($\overline{1}0$11) and ($\overline{1}0$13) twin boundary models shows evident asymmetry: the crack on the left side mainly propagates through the void nucleation mechanism and exhibits a faster growth rate, while the right-side twin boundary inhibits crack propagation by favoring dislocation nucleation. In contrast, the crack propagation behavior in the ($\overline{1}0$12), ($\overline{2}1$11), ($\overline{2}1$12) and ($\overline{2}1$14) twin boundary models is largely symmetric on both sides, showing no significant difference in propagation rate. Stress field analysis further reveals that the differences in crack propagation behavior among the various twin boundary models mainly originate from the disparity in dislocation activity on both sides of the crack, resulting in different levels of stress concentration at the crack tip. When void nucleation occurs at the twin boundary interface, the stress concentration between the main crack and the void intensifies, promoting their coalescence and further propagation. Meanwhile, with an increase in biaxial load ratio, the stress concentration at the crack tip becomes more pronounced, further accelerating crack propagation. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

In this study, twin screw extruded Polyetherimide (PEI)/Poly(ethylene terephthalate) (PET) polymer blends (90/10, 70/30, 50/50 w/w%) were investigated to elucidate the composition–property relationship governed by morphological, structural, rheological, thermomechanical, mechanical, and electromagnetic shielding (EMI) performance behavior. Among other polymer blends, the 70/30 blend exhibits superior thermomechanical stability with a significant glass transition temperature of 132.7 °C, where a robust confinement effect effectively restricts the mobility of PET chains. This morphology, characterized by a domain size of 562 nm, provides proof of concept for interface-driven attenuation, reaching a maximum EMI shielding effectiveness of 2.54 dB within the investigated blends. This performance is primarily governed by Maxwell–Wagner–Sillars polarization at the immiscible boundaries, alongside an optimized dielectric loss of tan δ ≈ 0.065. The design of these high-temperature PEI blends provides a proof of concept for interface-driven attenuation and demonstrates their potential for developing advanced EMI shielding matrices. Full article

Magnesium (Mg) alloy sheets usually suffer from severe mechanical anisotropy and a trade-off between strength and ductility. In this work, the effects of rolling temperature (200 °C and 400 °C) and rolling speed (50–1000 r/min) on the microstructure and mechanical properties of a Mg-1Sm (samarium, Sm) (wt.%) alloy were systematically investigated. Low-temperature rolling (200 °C) results in high dislocation density and a double-peak basal texture in Mg-1Sm alloy, causing very limited plasticity and a pronounced anisotropy with a lower yield strength along the rolling direction (RD) than along the transverse direction (TD). A significantly improved mechanical property (yield strength of ~196 MPa, elongation of 18.4% and near-isotropy) can be achieved in the Mg-1Sm alloy by optimizing the rolling conditions (400 °C, 500 r/min). The findings indicate that increasing the temperature is beneficial for activating non-basal slip and multiple twinning modes, thereby weakening and dispersing the basal texture, which can efficiently improve the anisotropic properties. Increasing the rolling speed can promote the recrystallization process, resulting in the enhancement of plasticity. Quantitative analyses reveal that the reduction in dislocation density and the suppression of Sm segregation at grain boundaries under high-temperature high-speed rolling are responsible for the improved ductility and reduced anisotropy. Full article

Machine learning (ML) is highly effective for modeling the complex factors governing twinning in magnesium (Mg) alloys, but it is often limited by challenges in hyperparameter optimization and a lack of interpretability, which reduce predictive accuracy and hinder mechanistic understanding. In this work, we present an enhanced interpretable ML framework that integrates Optuna for automated hyperparameter tuning using tree-structured Parzen estimators and SHapley Additive exPlanations (SHAP) for quantitative feature attribution. This approach delivers significant performance improvements, including F1-score gains of 6.33–11.84% on dataset T and AUC increases of up to 16.31% on dataset Y, outperforming previous benchmarks. When applied to a custom dataset derived from in situ EBSD tensile tests on Mg alloys and complemented by molecular dynamics (MD) simulations, SHAP analysis reveals a previously unrecognized grain shape-orientation effect: elongated grains with long-axis orientations of 20–80° relative to the tensile direction facilitate twinning nucleation, whereas orientations of 0–20° or 80–90° suppress it. Combined EBSD observations and MD simulations indicate that this effect arises from changes in boundary-segment orientation combinations, which regulate local constraint conditions, stress-transfer paths, and effective boundary resistance. Full article

Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical continuum care, where patients traverse presurgical risk stratification, intraoperative monitoring, postsurgical ICU, ward care, and remote rehabilitation over days to weeks—a complexity no fixed-policy agent architecture can address without prohibitive rule engineering. Objective: We present the first agentic large language model (LLM) framework for autonomous end-to-end surgical continuum monitoring, superseding the prior rule-based MAS Digital Twin. Six ReAct-driven tool-use agents replace fixed-policy agents with dynamic reasoning, multi-hop evidence retrieval, and Reflexion self-correction while maintaining mandatory confidence-gated Human-in-the-Loop (HITL) gating at every care-pathway-modifying decision. Methods: The framework is grounded in the ReAct paradigm and Reflexion self-evaluation, embedded within the DETER Digital Twin state engine S(t). Each agent is specified by a ReAct loop signature, a ten-function clinical tool registry, and confidence-gated HITL escalation logic. Inter-agent coordination replaces the rule-based Priority Queue Manager with an LLM-mediated Coordination Supervisor Agent reasoning over competing resource requests. Results: The framework delivers: (i) six formally specified ReAct-loop agents with explicit tool registries and authorisation boundaries; (ii) a confidence-gated HITL architecture that reduces alert fatigue while preserving safety for ambiguous clinical scenarios; (iii) an extended conflict resolution function P(p,t,context) incorporating surgical phase and DETER deterioration trajectory gradient; (iv) Reflexion self-correction with a formal N_max = 2 termination condition and Clinical Factuality Verification Layer; and (v) a multi-phase Digital Twin state engine extending S(t) to the full surgical continuum. Conclusions: The proposed framework represents a fundamental architectural departure from rule-based clinical AI—from hardcoded policies to dynamic reasoning, from static retrieval to multi-hop tool-use chains, and from fixed escalation thresholds to confidence-gated self-evaluation—providing a formally specified, clinically deployable foundation for next-generation autonomous surgical care coordination. Full article

Achieving a favorable synergy of strength, ductility, and toughness is a critical challenge for expanding the engineering applications of titanium alloys. In this work, a medium-strength and high-toughness novel Ti-Al-Mo-V-Cr-Sn-Zr (named Ti62F) titanium alloy in the form of a Φ400 mm bar was adopted to systematically investigate the regulation behavior of double annealing on its microstructure and mechanical properties, and quantitative correlations between microstructural parameters and macroscopic properties were established. Increasing the cooling rate during the first annealing stage (air cooling, force air cooling and water quenching) significantly refined the secondary α (α_s) phase and reduced the volume fraction and size of the primary α (α_p) phase, leading to an increase in the ultimate tensile strength of the alloy from 1077 MPa to 1229 MPa. However, the impact-absorbed energy decreased from 51.5 J to 23.3 J. When the second annealing temperature was varied within the range of 625–675 °C, the ultimate tensile strength fluctuated slightly and the impact toughness increased moderately. Equiaxed α_p phase and relatively thick α_s can induce multiple crack deflections, prolong the crack propagation path and enhance energy absorption. Dislocations are mainly piled up at α/β phase boundaries, triggering void nucleation and growth, which dominate the ductility and toughness levels. Tensile twinning acts only as an auxiliary deformation mechanism and contributes limitedly to toughness. After heat treatment under the optimized schedule of 880 °C/2 h/AC + 650 °C/4 h/AC, the Ti62F alloy exhibits a superior strength–toughness balance compared with conventional medium-strength titanium alloys such as TA15, TC4, and TC4-DT. The findings can provide a heat treatment basis for microstructural regulation of large-size Ti62F bars and their engineering applications in aerospace structural components. Full article

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article

Finite element models of the mitral valve can be useful tools for physicians as a predictive tool for surgical planning, teaching, or observation. In order to seamlessly implement these tools in a clinical setting, the process for the creation of the models needs to take into account the diagnostic procedures and tools available to physicians. In this study, a rapid patient-specific model for clinical applications is developed, creating a parametric geometry from measurements routinely taken during the diagnostic process and maintaining a low computational cost through simplifications in material and boundary conditions. The healthy valve model is then validated against ultrasound images from peak diastole to peak systole, finding a good conformity despite simplifications. These results can serve as a stepping stone towards the development of a clinical digital twin of the mitral valve that combines engineering knowledge and medical process. Full article

Pipes conveying fluids are important fluid–structure interaction systems encountered in aerospace, energy, marine, and industrial applications. Their dynamic behavior is strongly influenced by the interaction between structural motion and internal or external flow, leading to complex phenomena such as divergence, flutter, and flow-induced vibration. This review presents a comprehensive assessment of the dynamics and stability of pipes conveying fluids by integrating classical theories with recent developments in modeling, computation, materials, and control. The review covers mathematical formulations based on Euler–Bernoulli, Rayleigh, Timoshenko, and shell theories, together with analytical and numerical solution methods used for stability and vibration analysis. The effects of geometry, boundary conditions, flow configuration, damping, and material properties on dynamic response and instability thresholds are discussed. Special attention is given to composite, viscoelastic, functionally graded, and smart materials, as well as micro- and nanoscale pipe systems. Recent advances in vibration suppression, reduced-order modeling, machine learning, and physics-informed computational approaches are also reviewed. Finally, the paper identifies current challenges and future research directions, including multiphysics coupling, experimental validation, digital twins, and AI-assisted predictive modeling for fluid-conveying pipe systems. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

High-Mn twinning-induced plasticity (TWIP) steels are renowned for their exceptional strength-ductility synergy. However, their practical applications are severely constrained by inadequate yield strength and poor corrosion resistance. In this study, an N-alloyed TWIP steel (Fe-25Mn-12Cr-0.3C-0.3N, wt.%, designated as TWIP-2) was developed, using an N-free counterpart (Fe-25Mn-12Cr-0.3C, TWIP-1) as a reference. Both steels underwent hot forging (HF) followed by solution treatment (ST). The synergistic effects of N alloying and thermomechanical processing on the microstructural evolution, mechanical properties, and corrosion behavior were systematically investigated. Results indicate that all samples retain a single-phase FCC austenitic structure. N alloying increased the yield strength of the hot-forged TWIP steel from 488.1 MPa to 802.9 MPa while maintaining an elongation after fracture around 40%. Solution treatment markedly improved corrosion resistance, changing the corrosion mode from intergranular attack to pitting. The TWIP-2-ST specimen exhibited the lowest corrosion current density of 2.88 × 10⁻⁵ A/cm² and demonstrated the best overall performance. This comprehensive improvement in mechanical and corrosion performance is primarily attributed to the elevated work-hardening capacity, a higher fraction of low-energy grain boundaries, and the beneficial role of interstitial N in suppressing pitting nucleation and propagation. Full article

To address the erosion-induced failure of large-caliber gun barrels under extreme thermochemical coupling, this study systematically investigates the microstructural evolution of multi-layered gradient regions along the radial direction of 32CrNi3MoV steel under extreme thermochemical cycling. Leveraging SEM, EBSD, TKD, and double-beam aberration-corrected TEM, combined with JMatPro thermodynamic simulations, the phase transitions, crystallographic characteristics, and substructural evolution spanning from the bore surface to the matrix are elucidated. The results demonstrate that a three-layer gradient structure forms along the radial direction. The topmost layer is a chemically stabilized metastable austenite diffusion layer with a thickness of 1.5–4.0 μm. which is attributed to the suppression of martensitic transformation due to C/N interstitial diffusion lowering the MS temperature. The observed high-density dislocation tangles and stacking faults within this austenite diffusion layer result from thermal mismatch stresses during rapid thermal cycling. The subsurface region is a martensitic transformation layer with a thickness of 70–97 μm, exhibiting a substructural gradient from nanostructured high-density twinned martensite to refined lath martensite. Thermodynamic analysis indicates that rapid heating (≈10⁵ °C/s) facilitates significant austenite nucleation and growth during the reverse phase transformation, subsequently forming nanostructured martensitic grains via non-equilibrium transformation during rapid cooling. Adjacent to this is a matrix tempering layer extending approximately 160 μm. Nanoindentation hardness profiling reveals that the peak radial hardness (≈1000 HV) occurs within the fine-grained martensitic zone approximately 40 μm from the surface. In contrast, the tempered layer exhibits reduced hardness (≈400 HV) compared to the original matrix (≈500 HV). This is primarily attributed to transient high-temperature over-tempering effects, which induces carbide coarsening and the loss of solid solution strengthening, alongside the softening of prior austenite grain boundaries. This study clarifies the micro-to-nanoscale evolution of the barrel microstructure, providing critical theoretical insights for understanding erosion mechanisms and improving lifetime predictions. Full article

Urban planning and design increasingly address systemic complexity, involving heterogeneous actors, multi-scalar interactions, and long-term uncertainty. Urban Digital Twins (UDTs) have emerged as instruments for data-driven urban analysis and decision support, yet their relationship to City Information Modelling (CIM) remains insufficiently articulated. This paper argues that UDTs should be understood not as self-contained technological artefacts, but as operative configurations within CIM, which provides the organisational and conceptual infrastructure for structuring urban information. To clarify this relationship, the paper introduces a distinction between imperative and declarative modes of Urban Digital Twinning. Imperative modes translate urban ambitions into indicators, thresholds, and evaluative metrics that support benchmarking, negotiation, and decision-making. Declarative modes use relational reduction strategies that preserve underlying configurations and support interpretive reasoning before evaluative closure. The argument is developed through a comparative conceptual–analytical reading of two practice-oriented applications in the Netherlands, Eindhoven and the Schiphol Area Development Corporation, and an exploratory research project centred on Graz, Austria. The comparison examines data sources, spatial units, transformation procedures, output forms, uncertainty treatment, and validation logic. The Dutch cases show how imperative UDTs support policy translation and multi-stakeholder coordination, while the Graz case demonstrates how declarative twinning can articulate structural tendencies for early-stage environmental interpretation. The paper contributes to CIM discourse by clarifying the role of UDTs within broader informational frameworks and positioning declarative twinning as a practical complement to performance-oriented approaches for engaging urban complexity beyond benchmarking alone. Full article

Cobalt-free Li-rich Mn-based layered oxides are promising cathode materials for next-generation lithium-ion batteries because of their high capacity and reduced reliance on cobalt resources. However, their practical application is still limited by low initial Coulombic efficiency, sluggish reaction kinetics, severe voltage decay, and progressive structural degradation during cycling. In this work, a Na-assisted sol–gel strategy was developed to construct a cobalt-free Li-rich Mn-based cathode with multiple twin boundaries, and the optimized sample with the composition of Li_1.13Na_0.06Mn_0.594Ni_0.219O₂ was denoted as SG-TB. Unlike conventional surface coating or elemental doping, this strategy focuses on regulating the bulk crystal framework through crystallographic defect engineering. Structural characterizations indicate that SG-TB contains repeatedly distributed twin-boundary-related interfaces, supporting the presence of multiple twin boundaries within the layered cathode. Benefiting from this structural feature, SG-TB delivers an initial Coulombic efficiency of 96%, an initial discharge capacity of 256 mAh/g, a discharge capacity of 167 mAh/g at 5 C, and a capacity retention of 77% after 200 cycles at 1 C. Further analyses suggest that the multiple twin boundaries help reduce electrochemical polarization, enhance Li⁺ diffusion kinetics, and improve structural retention during cycling. This work demonstrates that Na-assisted multiple twin-boundary engineering is an effective strategy for improving the reaction reversibility and structural stability of cobalt-free Li-rich Mn-based cathodes. Full article