Search Results (409)

Weld quality plays a critical role in ensuring the structural integrity and long-term performance of critical piping systems used across petrochemical, oil and gas, marine, and healthcare sectors. Although gas tungsten arc welding, shielded metal arc welding, and gas metal arc welding are widely applied in pipe fabrication, existing studies often examine these processes independently and provide limited insight into the comparative influence of process parameters on weld morphology, microstructure, and mechanical performance. This review consolidates findings from recent research to evaluate how welding current, arc voltage, heat input, travel speed, shielding gas composition, and joint preparation interact to affect weld bead geometry, heat-affected zone evolution, tensile properties, hardness, and overall weld integrity in piping systems. The primary objective of this review is to critically compare fusion welding process parameter optimization strategies and to identify unresolved challenges in achieving controlled weld root geometry for high-integrity piping applications. Recent industrial failure investigations, particularly in ethylene oxide service piping, further underscore the importance of weld root control. Several documented leak events were traced to excessive root protrusion and inadequate interpretation of non-destructive testing data, where elevated reinforcement disrupted internal flow and promoted turbulence-induced degradation. These recurring issues highlight a broader industry challenge and strengthen the need for improved root-height optimization in critical piping applications. A significant research gap is identified in the limited optimization of weld root reinforcement, particularly in gas tungsten arc welding processes, where most reported studies document root heights exceeding 3 mm. Achieving a root height below 2 mm, which is an important requirement for reducing flow-induced turbulence and meeting industry acceptance criteria, remains insufficiently addressed. This review highlights this gap and outlines future research opportunities involving advanced parameter optimization and improved process monitoring techniques. The synthesis presented here provides a comprehensive reference for enhancing weld quality in critical piping systems and establishes a pathway for next-generation welding strategies aimed at producing high-integrity weld joints compliant with the American Society of Mechanical Engineers B31.3 requirements. Full article

This paper presents the results of a numerical study on the influence of the tooth root fillet manufacturing method on the bending strength of spur gears with straight teeth. A mathematical model describing the gear tooth geometry was developed, in which the transition curve at the tooth root was directly related to the applied machining process—either rack-type gear shaping or pinion-type gear shaping. Based on this model, a numerical procedure for calculating the bending stresses at the tooth root was formulated and verified using the finite element method (FEM). The results demonstrated high consistency between the proposed approach and FEM analysis, confirming the accuracy of the developed mathematical model and numerical methodology. The study also examined the effect of the tool fillet radius on the stress distribution in the root region. It was found that increasing the tool radius leads to a reduction in bending stresses, while the differences between the two machining methods gradually diminish. The proposed methodology offers a reliable numerical framework for assessing the strength of spur gears and can be effectively used in the design of lightweight, high-performance gear transmissions for aerospace and automotive applications. Full article

Accurately measuring the three-dimensional green quantity (3DGQ) of urban trees is crucial for quantifying carbon sequestration benefits (CSB) in high-density cities. In this study, 540 street trees across 18 species (30 per species) in Shanghai were analyzed to evaluate an Improved Simpson Model (ISM) for UAV-derived crown volume estimation against a traditional Approximate Geometry Model (AGM) and a LiDAR-based point cloud method (PCM). The ISM integrates UAV imagery, edge-based canopy profiling, and Simpson’s numerical integration to account for irregular crown shapes and internal leaf-stem gaps. Results show that ISM achieved consistently lower estimation errors than the benchmark methods. Overall, ISM’s 3DGQ estimates had a root mean square error (RMSE) of approximately 5.2 m³ and a mean absolute error (MAE) of about 4.1 m³, indicating a close match with PCM reference values. This represents a dramatic error reduction, on the order of 90%–95% improvement in RMSE, compared to the conventional AGM approach. Broadleaf species with dense, regular canopies (e.g., Cinnamomum camphora and Platanus × acerifolia) exhibited the highest accuracy, with ISM-predicted volumes deviating only ~1%–2% from field measurements. Even for species with more irregular or porous crowns, the ISM maintained robust performance, yielding smaller errors than AGM and nearly matching the LiDAR-based PCM “ground truth.” These findings demonstrate that the proposed ISM can provide highly accurate 3D crown volume and carbon sequestration estimates in complex urban environments, outperforming existing geometric models and offering a practical, efficient alternative to labor-intensive LiDAR surveys. Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

Graphene oxide (GO) is a carbon-based nanomaterial explored for agricultural and forestry uses, but plant responses are strongly subject to both the dose and the route of exposure. We summarized recent studies with defined graphene oxide (GO) exposures by seed priming, foliar delivery, and root or soil exposure, while comparing annual crops with woody forest plants. Mechanistic progress points to a shared physicochemical basis: surface oxygen groups and sheet geometry reshape water and ion microenvironments at the soil–seed and soil–rhizosphere interfaces, and many reported shifts in antioxidant enzymes and hormone pathways likely represent downstream stress responses. In crops, low-to-moderate doses most consistently improve germination, root architecture, and tolerance to salinity or drought stress, whereas high doses or prolonged root exposure can cause root surface coating, oxidative injury, and photosynthetic inhibition. In forest plants, evidence remains limited and often relies on seedlings or tissue culture. For forest plants with long life cycles, processes such as soil persistence, aging, and multi-seasonal carry-over become key factors, especially in nurseries and restoration substrates. The available data indicate predominant root retention with generally limited root-to-shoot translocation, so residues in edible and medicinal organs remain insufficiently quantified under realistic-use patterns. This review provides a scenario-based framework for crop- and forestry-specific safe-dose windows and proposes standardized endpoints for long-term fate and ecological risk assessment. Full article

In the oil and gas pipeline industry, numerous small-diameter branch pipe fillet welds exist, which are prone to stress concentration because of diverse geometric shapes. The internal welding defects within these welds pose severe hazards to safe production. Specifically, the irregular geometry often leads to internal root defects where the weld metal fails to fully penetrate the joint or fuse with the base material (referred to as incomplete penetration and incomplete fusion). This study developed a GF-CF-GF (CF is carbon fiber, GF is glass fiber) sandwich composite reinforcement structure for pipe fittings with these specific internal defects (main pipe: Φ323.9 × 12.5 mm; branch pipe: Φ76 × 5 mm) through a combination of finite element analysis (FEA) and burst test verification. The inherent correlation between structural factors and pressure-bearing capacity was revealed by analyzing the influence of defect sizes. Based on FEA, the repair layer coverage should be designed to be within 400 mm from the defect along the main pipe wall direction and within 100 mm from the defect along the branch pipe wall direction, with required thicknesses of 5.6 mm for incomplete penetration and 3.2 mm for incomplete fusion. Analysis of the actual burst test pressure curve showed that the elastic-plastic transition interval of the repaired pipes increased by approximately 2 MPa compared to normal undamaged pipes, and their pressure-bearing capacities rose by 1.57 MPa (incomplete penetration) and 1.76 MPa (incomplete fusion). These results demonstrate the feasibility of the proposed reinforcement design, which has potential applications in the safety and integrity of oil and gas transportation. Full article

In medical 3D printing, accuracy is critical for fabricating patient-specific implants and anatomical models. Although printer performance has been widely examined, the influence of slicing software on geometric fidelity is less frequently quantified. The slicing step, which converts STL files into printer-readable G-code, may introduce deviations that affect the final printed object. To quantify slicer-induced G-code deviations by comparing G-code-derived geometries with their reference STL modelsTwenty mandibular models were processed using five slicers (PrusaSlicer (version 2.9.1.), Cura (version 5.2.2.), Simplify3D (version 4.1.2.), Slic3r (version 1.3.0.) and Fusion 360 (version 2.0.19725)). A custom Python workflow converted the G-code into point clouds and reconstructed STL meshes through XY and Z corrections, marching cubes surface extraction, and volumetric extrusion. A calibration object enabled coordinate normalization across slicers. Accuracy was assessed using Mean Surface Distance (MSD), Root Mean Square (RMS) deviation, and Volume Difference. MSD ranged from 0.071 to 0.095 mm, and RMS deviation from 0.084 to 0.113 mm, depending on the slicer. Volumetric differences were slicer-dependent. PrusaSlicer yielded the highest surface accuracy; Simplify3D and Slic3r showed best repeatability. Fusion 360 produced the largest deviations. The slicers introduced geometric deviations below 0.1 mm that represent a substantial proportion of the overall error in the FDM workflow. Full article

Artstein-Avidan and Milman [Annals of mathematics (2009), (169):661–674] characterized invertible reverse-ordering transforms in the space of lower, semi-continuous, extended, real-valued convex functions as affine deformations of the ordinary Legendre transform. In this work, we first prove that all those generalized Legendre transforms of functions correspond to the ordinary Legendre transform of dually corresponding affine-deformed functions. In short, generalized convex conjugates are ordinary convex conjugates of dually affine-deformed functions. Second, we explain how these generalized Legendre transforms can be derived from the dual Hessian structures of information geometry. Full article

Shield thrust is a key control parameter for ensuring the safety and efficiency of tunnel construction. Under complex geological conditions and strong data nonlinearity, conventional prediction methods often fail to achieve sufficient accuracy. This study proposes a hybrid prediction model in which a bidirectional long short-term memory (BiLSTM) network is optimized by intelligent algorithms. A multidimensional input dataset comprising tunnel geometry, geomechanical parameters and tunnelling parameters is constructed, and BiLSTM is used to capture bidirectional temporal dependencies in the tunnelling data. To adaptively determine key BiLSTM hyperparameters (number of neurons, dropout rate and learning rate), four intelligent optimization algorithms—genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA) and Hunger Games Search (HGS)—are employed for hyperparameter tuning, using the root-mean-square error (RMSE) between predicted and measured values as the fitness function. Four hybrid models, GA–BiLSTM, PSO–BiLSTM, SSA–BiLSTM and HGS–BiLSTM, are validated using real engineering data from Beijing Metro Line 22, Guanzhuang–Yongshun shield-driven section. The results show that HGS–BiLSTM outperforms the other models in terms of RMSE, mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R²) and exhibits faster convergence, supporting real-time prediction and decision-making in shield thrust control. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

Mesh generation is a critical preprocessing step in Computational Fluid Dynamics. In ship hydrodynamics, existing mesh generation methods lack adaptability to complex hull surface geometries, necessitating repeated optimization. To address these issues, a new hybrid mesh generation strategy was proposed, integrating Non-Uniform Rational B-Spline surface interpolation, advancing front technique, hull surface curvature features, and mesh quality evaluation parameters. Firstly, the ship hull surface was partitioned into multiple regions, and each region was assigned a specific mesh type. Subsequently, the adaptively sized mesh was generated based on local curvature variations. Finally, the angle skewness was employed as an objective function to improve the mesh quality. In addition, considering the actual ship model as an example, the mesh generated by our method and conventional Laplacian smoothing method were used to perform first-order potential flow simulations, and the results were compared against the convergence values. The results indicated that our method has lower root mean square errors in computing the total non-viscous force, steady drift force and ship hull free floating Response Amplitude Operator. This method is applicable to numerical simulations of the ship potential flow, providing high-quality hull meshes for hydrodynamic analysis. Full article

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may lead to difficulties in solving using traditional form-modeling methods. Traditional geometric solutions such as ellipsoid approximations, diameter–height estimation, and shape-from-silhouette reconstruction often fail because the irregular calyx generates asymmetric protrusions that violate their basic form assumptions. We offer a novel study framework employing both multi-view instance segmentation and hybrid geometrical feature modeling to quantitatively model mangosteen volume with traditional 2D imaging. A You Only Look Once (YOLO)-based segmentation model was employed to explicitly separate the fruit body from the calyx. Calyx inclusion resulted in dense geometric noise and reduced model performance ($R^2<0.40$). We trained eight regression models on a curated and augmented 900 image dataset ($N=720$, test $N=180$). The models used single-view and multi-view geometric regressors ($V\propto A^{1.5}$), polynomial hybrid configurations, ellipsoid-based approximations, as well as hybrid feature formulations. Multi-view models consistently outperformed single-view models, and the average predictive accuracy improved from $R^2=0.6493$ to $R^2=0.7290$. The best model is indeed a hybrid linear regression model with side- and bottom-area features—($A_s^{1.5}$, $A_b^{1.5}$)—combined with ellipsoid-derived volume estimation—($V_{\mathrm{ellipsoid}}$)—which resulted in $R^2=0.7290$, a Mean Absolute Percentage Error (MAPE) of 16.04%, and a Root Mean Square Error (RMSE) of 31.9 ${\mathrm{cm}}^3$ on the test set. These results confirm the proposed model as a low-cost, interpretable, and flexible model for real-time fruit volume estimation, ready for incorporation into automated sorting and grading systems integrated in post-harvest processing pipelines. Full article

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively. Full article

The reliability of electric vehicle (EV) batteries requires detecting surface defects but also precisely locating them on the physical module for automated inspection, repair, and process optimisation. Conventional 2D computer vision methods, though accurate in image-space, do not provide traceable, real-world defect coordinates on complex or curved battery surfaces, limiting utility for digital twins, root cause analysis, and automated quality control. This work proposes a hybrid inspection pipeline that produces millimetre-level three-dimensional (3D) defect maps for EV battery modules. The approach integrates (i) calibrated dual-view multi-view geometry to project defect points onto the CAD geometry and triangulate them where dual-view coverage is available, (ii) single-image neural 3D shape inference calibrated to the module geometry to complement regions with limited multi-view coverage, and (iii) generative, physically informed augmentation of rare or complex defect types. Defects are first detected in 2D images using a convolutional neural network (CNN), then projected onto a dense 3D CAD model of each module, complemented by a single-image depth prediction in regions with limited dual-view coverage, yielding true as-built localisation on the battery’s surface. GenAI methods are employed to expand the dataset with synthetic defect variations. Synthetic, physically informed defect examples are incorporated during training to mitigate the scarcity of rare defect types. Evaluation on a pilot industrial dataset, with a physically measured reference subset, demonstrates that the hybrid 3D approach achieves millimetre-scale localisation accuracy and outperforms a per-view CNN baseline in both segmentation and 3D continuity. Full article

Periapical radiographs remain the first-line imaging modality in endodontics due to accessibility and low radiation dose, whereas cone-beam computed tomography (CBCT) is reserved for inconclusive cases or suspected anatomical complexity. We propose a physics- and geometry-aware preprocessing pipeline coupled with sliding-window Viterbi tracking to enhance canal visibility and recover plausible root canal trajectories directly from routine periapical images. The pipeline standardizes row-wise brightness, compensates for the cone-like tooth density profile (Tukey window), and suppresses noise prior to dynamic-programming inference, requiring only minimal operator input (two-point orientation and region of interest). In a retrospective evaluation against micro-computed tomography (micro-CT)/CBCT reference anatomy, the approach accurately localized canals on periapicals under study conditions, suggesting potential as a rapid, chairside aid when 3D imaging is unavailable or deferred. Full article