Search Results (1,460)

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

High-Mn steels are commonly fabricated by hot rolling and on-line cooling for cryogenic applications, because there exists an aging embrittlement zone in most high-Mn steels, and this shortcoming makes it difficult to optimize their mechanical properties by heat treatments. Hence, 0.6C-18Mn-0/3/5Al (in wt.%) steels were designed to investigate the effects of Al on their strength and toughness. The addition of 5 wt.% Al can increase yield strength from 357 to 461 MPa and the Charpy impact absorbed energy from 56 to 119 J. Although there is still a cryogenic aging embrittlement zone in each steel, we found that the addition of Al can narrow this brittle zone. Moreover, the absorbed energy is lowered by around 89%, 48%, and 40% for the 0Al, 3Al, and 5Al steels at −196 °C, respectively. Additionally, impact plastic deformation mechanisms were also revealed in the steels with a heat-treating temperature of 600 °C, revealing that the main deformation mechanism shifts from numerous partial dislocation slip to twinning plus strong planar slip as the addition of Al increases. Full article

Aiming at the problem of poor autonomy and weak time performance of reentry trajectory planning for Reusable Launch Vehicle (RLV), an online reentry trajectory planning and guidance method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. In view of the advantage that the drag acceleration can be quickly measured by the airborne inertial navigation equipment, the reference profile adopts the design of the drag acceleration–velocity profile in the reentry corridor. In order to prevent the problem of trajectory angle jump caused by the unsmooth turning point of the section, the section form adopts the form of four multiple functions to ensure the smooth connection of the turning point. Secondly, considering the advantages of the TD3 dual Critic network structure and delay update mechanism to suppress strategy overestimation, the TD3 algorithm framework is used to train multiple strategy networks offline and output profile parameters. Finally, considering the reentry uncertainty and the guidance error caused by the limitation of the bank angle reversal amplitude during lateral guidance, the networks are invoked online many times to solve the profile parameters in real time and update the profile periodically to ensure the rapidity and autonomy of the guidance command generation. The TD3 strategy networks are trained offline and invoked online many times so that the cumulative error in the previous guidance period can be eliminated when the algorithm is called again each time, and the online rapid generation and update of the reentry trajectory is realized, which effectively improves the accuracy and computational efficiency of the landing point. Full article

Determining orientation relationships between different grains or phases via electron diffraction typically requires coincident zone axes, but it is difficult to achieve in most cases due to tilting angle limitations. To address this challenge, a straightforward method for determining the twinning relationship and twin variant in deformed metals is developed by interpreting the selected area electron diffraction (SAED) patterns and corresponding tilt angles in the transmission electron microscope (TEM). The transformation matrix from the sample coordinate system (SCS) to the crystal coordinate system (CCS) is derived to describe the orientation matrix of the observed target. This method is demonstrated by characterizing twins and corresponding Schmid factors in deformed Ti−15Mo alloy even when the zone axes are not coaxial. This method significantly facilitates the determination of multiple orientation relationships and the quantitative analysis of plastic deformation mechanisms in TEM. Full article

This study investigates the influence of Cu addition on the nanostructural evolution and mechanical performance of a heavily drawn non-equiatomic CoCu_1.71FeMnNi high-entropy alloy (HEA) wire. Through systematic microstructural and compositional analysis, we examine how Cu constituent affects phase separation behavior and promotes deformation-induced nano-twinning in another phase counterpart. The designed HEA wire exhibits an elongated ultrafine dual face-centered cubic (fcc) lamella structure (i.e., Co-Fe-rich and Cu-rich phases) that emerges through compositional segregation by spontaneous phase separation from the as-cast state. High-resolution electron microscopy reveals the dislocation wall boundaries stabilized by nanoscale phase interfaces. The cold-drawn CoCu_1.71FeMnNi wire features an impressive combination of strength and ductility, as well as an ultimate tensile strength of nearly ~2 GPa with an elongation of over ~6%. These findings highlight the critical role of compositional tuning in controlling the ultrafine lamella structure stabilized by spinodal-like phase decomposition, offering a pathway to engineering high-performance HEA wires for advanced structural applications. Full article

Crystal twinning, a common growth phenomenon, can substantially affect material performance in fields such as semiconductors, nonlinear optics, and drug development, yet its elimination during crystallization is challenging. This study presents a method for the controlled synthesis of ZSM-5 zeolite as either single crystals or twinned crystals using kaolin as the primary raw material. The method leverages the etching effect of ammonium fluoride (NH${⁠}_4$F) on the aluminosilicate structure derived from pre-treated kaolin. By adjusting the concentrations of NH${⁠}_4$F and the structure-directing agent tetrapropylammonium bromide (TPABr), pure ZSM-5 single crystals and twinned crystals were selectively synthesized. Conventionally, NH${⁠}_4$F is employed to introduce defects into zeolite structures. In contrast, this work demonstrates its utility in controlling crystal habit. The synthesis utilizes kaolin, an abundant and low-cost aluminosilicate mineral, to provide the entire aluminum source and a portion of the silicon source, offering an economical alternative to expensive precursors like aluminum isopropoxide. The resulting single and twinned crystals exhibited high crystallinity, demonstrating the viability of using natural minerals to produce high-quality zeolites. The physical and chemical properties of the kaolin-derived ZSM-5 were characterized and compared to those of ZSM-5 synthesized from conventional chemical reagents. A growth mechanism for the formation of single and twinned crystals is also proposed. Full article

This study addresses the critical challenges of interfacial stress mismatch, fiber degradation, and unstable clad geometry in manufacturing continuous carbon fiber-reinforced aluminum composites (Cf/Al) via laser cladding, driven by rapid thermal gradients. A dual-ellipsoid heat source-based thermoelastic–plastic finite element model was developed in Abaqus, integrating phase-dependent material properties and latent heat effects to simulate multi-physics interactions during single-track deposition, resolving transient temperature fields peaking at 1265 °C, and residual stresses across uncoated and Ni-coated fiber configurations. The work identifies an optimal parameter window characterized by laser power ranging from 700 to 800 W, scan speed of 2 mm/s, and spot radius of 3 mm that minimizes thermal distortion below 5% through gradient-controlled energy delivery, while quantitatively demonstrating nickel interlayers’ dual protective role in achieving 42% reduction in fiber degradation at 1200 °C compared to uncoated systems and enhancing interfacial load transfer efficiency by 34.7%, thereby reducing matrix tensile stresses to 159 MPa at fiber interfaces. Experimental validation confirms the model’s predictive capability, revealing nickel-coated systems exhibit superior thermal stability with temperature differentials below 12.6 °C across interfaces and mechanical interlocking, achieving interfacial void fractions under 8%. These results establish a process–structure linkage framework, advancing defect-controlled composite fabrication and providing a digital twin methodology for aerospace-grade manufacturing. Full article

In the context of Industry 4.0 and intelligent manufacturing, the scale and complexity of complex equipment systems are continuously increasing, making effective high-precision modeling, simulation, and prediction in the engineering field significant challenges. Digital twin technology, by establishing real-time connections between virtual models and physical systems, provides strong support for the real-time monitoring, optimization, and prediction of complex systems. However, traditional digital twin models face significant limitations when synchronizing with high-dimensional nonlinear and non-stationary dynamical systems due to the latter’s dynamic characteristics. To address this issue, we propose a multi-layer deep Koopman operator-based (MDK) credibility-based self-evolution algorithm for equipment digital twins. By constructing multiple time-scale embedding layers and combining deep neural networks for observability function learning, the algorithm effectively captures the dynamic features of complex nonlinear systems at different time scales, enabling their global dynamic modeling and precise analysis. Furthermore, to enhance the model’s adaptability, a trustworthiness-based evolution-triggering mechanism and an adaptive model fine-tuning algorithm are designed. When the digital twin model’s trustworthiness assessment indicates a decline in prediction accuracy, the evolution mechanism is automatically triggered to optimize and update the model with the fine-tuning algorithm to maintain its stability and robustness during dynamic evolution. The experimental results demonstrate that the proposed method achieves significant improvements in prediction accuracy within unmanned aerial vehicle (UAV) systems, showcasing its broad application potential in intelligent manufacturing and complex equipment systems. Full article

Discrete manufacturing environments face increasing challenges in managing work-in-process (WIP) inventory due to growing product customization and demand volatility. While Value Stream Mapping (VSM) has been widely used for process improvement, traditional approaches lack the ability to dynamically control WIP levels while optimizing multiple performance dimensions simultaneously. This research addresses this gap by developing an integrated framework that synergizes Multi-Dimensional Value Stream Mapping (MD-VSM) with multi-objective optimization, functioning as a specialized digital twin for dynamic WIP control. The framework employs a four-layer architecture that connects real-time data collection, multi-dimensional modeling, dynamic WIP monitoring, and execution control through closed-loop feedback mechanisms. A mixed-integer optimization model is used to balance time, cost, and quality objectives. Validation using a high-fidelity simulation, parameterized with real-world industrial data, demonstrates that the proposed approach yielded up to a 31% reduction in inventory costs while maintaining production throughput and showed a 42% faster recovery from equipment failures compared to traditional methods. Furthermore, a comprehensive sensitivity analysis confirms the framework’s robustness. The system demonstrated stable performance even when key operational parameters, such as WIP upper limits and buffer capacity coefficients, were varied by up to ±30%, underscoring its reliability for real-world deployment. These findings provide manufacturers with a validated methodology for enhancing operational efficiency and production flexibility, advancing the integration of lean principles with data-driven, digital twin-based control systems. Full article

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies. Full article

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

This study aims to explore the role and impact of digital twin technology in enhancing the sustainable development of the energy industry so as to analyze how digital twin technology facilitates urban sustainability. Using data from 281 prefecture-level cities in China over the twelve-year period from 2013 to 2024, the study employs methods such as the entropy method, kernel density analysis, and spatial econometric models to conduct an in-depth analysis of improvements in energy efficiency. The findings indicate that digital twin technology plays a significant role in promoting the sustainable development of the energy industry. Furthermore, China is divided into four regions—eastern, central, western, and northeastern—for a comparative analysis, revealing regional differences in the relationship between the application level of digital twin technology and sustainable development of the energy industry. To effectively apply digital twin technology in this context, it is recommended to establish comprehensive digital twin models and intelligent decision-making systems for accurate energy monitoring and efficient management decisions. The results reveal that while digital twin technology enhances energy efficiency and promotes sustainable development overall, significant regional imbalances persist. The eastern region shows the highest integration level and performance, while the western and northeastern regions lag behind. In response, the study proposes tailored regional strategies, including the development of scalable digital twin technology, integrated data platforms, and strengthened governance mechanisms to enhance digital coordination and ensure data security. This research provides new empirical evidence and strategic guidance for leveraging digital twin technology in promoting low-carbon and sustainable urban energy systems. Full article

The synergistic advancement of “Digital China” and “Beautiful China” represents a pivotal national strategy for achieving high-quality economic development and a low-carbon transition. To illuminate the intrinsic mechanisms linking the digital economy (DE) to urban carbon emission performance (CEP), this study develops a novel two-sector theoretical framework. Leveraging panel data from 278 Chinese prefecture-level cities (2011–2023), we employ a comprehensive evaluation method to gauge DE development and utilize calibrated nighttime light data with downscaling inversion techniques to estimate city-level CEP. Our empirical analysis integrates static panel fixed effects, panel threshold, and moderating effects models. Key findings reveal that the digital economy demonstrably enhances urban carbon emission performance, although this positive effect exhibits a threshold characteristic linked to the maturity of digital infrastructure; beyond a specific developmental stage, the marginal benefits diminish. Crucially, this enhancement operates primarily through the twin engines of fostering technological innovation and driving industrial structure upgrading, with the former playing a dominant role. The impact of DE on CEP displays significant heterogeneity, proving stronger in northern cities, resource-dependent cities, and those characterized by higher levels of inclusive finance or lower fiscal expenditure intensities. Furthermore, the effectiveness of DE in reducing carbon emissions is dynamically moderated by policy environments: flexible economic growth targets amplify its carbon reduction efficacy, while environmental target constraints, particularly direct binding mandates, exert a more pronounced moderating influence. This research provides crucial theoretical insights and actionable policy pathways for harmonizing the “Dual Carbon” goals with the overarching Digital China strategy. Full article