Search Results (477)

This study presents multi-objective geometric optimization of a modular friction disk cutter for spur gear thermo-friction processing within the ANSYS Workbench 2024 R1. The integrated workflow—Geometry → Steady-State Thermal → Static Structural → Design of Experiments → Response Surface → Response Surface Optimization—enables selection of a rational tool geometry within a single parametric model. Variable dimensions (a, b, c) describe the load-bearing part: a characterizes the transitional thin profile zone, b is the massive supporting part, and c is the intermediate disk thickness controlling thermo-mechanical load transmission. Dimension c most significantly influences equivalent stresses and directional deformation, while maximum temperature depends on combined a and c effects. Based on Response Surface Optimization, the rational solution domain is concentrated near $a\approx 3 \mathrm{m}\mathrm{m}$, $b\approx 10 \mathrm{m}\mathrm{m}$, and $c\approx 4 \mathrm{m}\mathrm{m}$, yielding $P4\approx 33.306 \mathrm{M}\mathrm{P}\mathrm{a}$, $P5\approx 295.93 °\mathrm{C}$, and $P6\approx -5.7488·{10}^{-5} \mathrm{m}$. These values demonstrate a sufficient calculated safety margin within the finite element framework, providing a technically justified direction for prototype manufacturing. Although currently evaluated as purely computational without direct full-scale physical measurements, these results establish a foundation for subsequent experimental validation using thermal imaging and optical deformation analysis. Future research will focus on transient thermo-mechanical modeling with impulse cooling and experimental verification. Full article

The shrinkage strain, occurring upon UV curing and aging, leads to non-uniform dimensional changes that can compromise the part’s final geometry. This study investigates the deformation of green parts during the stereolithography process. Based on experimental measurements, a finite element model (FEM) is developed to account for different phenomena contributing to the structural distortion of the part, like polymerization shrinkage and the adhesion between the part and the build platform during printing. In addition, the time dependency of the degree of conversion is also considered to integrate the aging of green parts, and elastoplastic material behavior is also considered to include non-reversible deformations. This novel model makes it possible to predict stress generation during the stereolithography process and simulate part warping over time. The resulting simulations provided a numerical validation for part shapes observed experimentally, as well as insights to better understand the deformation mechanisms and optimize the dimensional fidelity of stereolithography-manufactured components. Full article

Due to the low stiffness characteristics, thin-walled parts are prone to force-induced deformation during the peripheral milling process, which severely restricts machining accuracy and efficiency. In existing studies, for curved thin-walled parts, the Finite Element Method (FEM) is usually adopted for deformation prediction. However, the traditional FEM usually requires a considerable amount of computing time, owing to the high model complexity and batch parameter evaluations. Therefore, this study proposes a method of constructing a surrogate model based on a small amount of FEM simulation data. Firstly, a peripheral milling cutting force model is established to obtain the instantaneous milling force. Secondly, a finite element model considering the material removal effect is constructed, and an iterative solution strategy is introduced to calculate the force-induced deformation. Finally, an Enhanced Latin Hypercube Sampling (ELHS) method is used to generate training samples, and the Elliptic Basis Function Neural Network (EBFNN) is selected as the surrogate model to establish a nonlinear mapping relationship between machining parameter combinations and force-induced deformation. This method enables rapid prediction of deformation at any machining position on curved thin-walled parts, reducing the computation time from hours to seconds while maintaining prediction accuracy. Full article

Background/Objectives: Pes planus and pes cavus are common foot deformities, which may lead to pain, functional limitations, and impairment of foot biomechanics. While calcaneal pitch, talar declination, and Meary angles, commonly used in diagnosis, provide objective information, their lack of a gold standard and the observer’s dependence on manual measurements limit their reliability. Therefore, in this study, these angles obtained from weight-bearing lateral foot radiographs were evaluated according to literature references, and the aim was to determine the model that provides the most accurate prediction in the diagnosis of pes planus using machine learning algorithms. It should be emphasized that, because the diagnostic labels were derived from literature-based thresholds of these same angles, the machine-learning task addressed here is the automated reproduction and standardization of expert, angle-threshold-based classification, rather than an independent clinical diagnosis from raw images. Methods: This retrospective study was conducted using weight-bearing lateral foot radiographs of 697 male patients obtained from the archives of public hospitals in Gaziantep. Calcaneal pitch, Meary angle, and talar declination angles were evaluated in both feet, and the data were labeled as normal, pes planus, and pes cavus. The dataset, consisting of a total of 1394 feet, was divided into training and test groups and analyzed using Random Forest, XGBoost, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms; the diagnostic performance of the models was compared using measures such as accuracy, F1 score, sensitivity, and specificity. Results: A total of 1394 feet from 697 male patients (mean age 24.8 ± 5.57 years) were analyzed using five machine learning algorithms with calcaneal pitch angle (CPA), Meary angle (MA), and talar declination angle (TDA) as reference labels. Ensemble-based methods showed superior performance, with XGBoost achieving perfect classification (Accuracy = 1.000) under all three labels for the left foot and 0.996–1.000 for the right foot, while Random Forest reached 0.986–1.000 across all experiments. Logistic Regression and SVM yielded moderate accuracies (0.905–0.973), whereas KNN consistently performed the weakest (0.905–0.964), particularly in the pes cavus subgroup. The near-perfect accuracy obtained when the labeling angle was itself included among the predictors reflects, at least in part, the algebraic reconstruction of the threshold rule from a same-source variable rather than genuine diagnostic generalization; results should therefore be interpreted with this in mind. Conclusions: This study demonstrates that machine learning, particularly ensemble methods such as XGBoost and Random Forest, provides high accuracy and consistency in diagnosing foot arch deformities based on radiographic angle measurements. Traditional models, such as Logistic Regression, still hold value in terms of clinical interpretability despite their lower performance. The findings suggest that machine learning-based approaches can offer objective, rapid, and reliable decision support tools for diagnosing pes planus and pes cavus, but external validation studies are necessary for clinical generalizability. Full article

Geometric monitoring of slender support structures, particularly steel lattice transmission towers, is a critical component of power infrastructure diagnostics. Such structures are susceptible to environmental influences and long-term deformation processes, which necessitates precise assessment of their geometric axis. The aim of this study was to evaluate the influence of the terrestrial laser scanning (TLS) scanner class and point cloud registration strategy on the determination of the geometric axis of a steel high-voltage lattice transmission tower (hereafter LTT). Unlike previous studies focused primarily on TLS-based axis reconstruction, this work introduces a comparative assessment of registration strategies, an error propagation model, and the proposed Axis Drift Index (ADI) as quantitative indicators of axis stability. The analysis was based on data obtained using a tachymetric method (reference), a compact scanner (Leica BLK360), and a survey-grade scanner (Riegl VZ-400i). The comparison included planimetric axis deviation, consistency of deformation direction, variation in results with height, and the influence of registration quality. The results show that TLS measurements performed using a survey-grade scanner and target-based registration exhibit high agreement with tachymetric results. In contrast, cloud-to-cloud registration without a stable reference framework leads to cumulative errors and instability of the reconstructed axis, particularly in the upper parts of the structure. The observed deviations in the BLK360 dataset were dominated by registration-related geometric instability rather than unequivocal structural deformation signals. The findings indicate that the accuracy of geometric axis determination in slender structures is governed more by the adopted point cloud registration strategy than by the scanner class itself. The proposed ADI parameter and linear error propagation model additionally enabled a quantitative assessment of geometric consistency with height. From an engineering perspective, this highlights the importance of stable reference systems and appropriate survey design in high-precision TLS applications. Although the study was conducted on a single lattice tower, the results provide practical insight into the reliability of TLS workflows for slender structures characterized by discontinuous geometry and high sensitivity to registration errors. Full article

Five modifications of polytetrafluoroethylene (PTFE) are considered as a modern alternative to PTFE as sliding layers of bridge bearing parts. Radiation-modified PTFE without additives and with nano-additives as well as composites based on PTFE with bronze inclusions and nanomodified carbon fiber fillers were investigated. Ultra-high-molecular-weight polyethylene (UHMWPE) and classic pure PTFE were considered as control samples. The thermomechanical properties of the materials were studied within the framework of dynamic mechanical analysis in the operating temperature range of bridge structures [−40; +80] °C. The exit zones from the linear theory of viscoelasticity were established for all the materials considered. Temperature dependencies of the storage modulus and the loss modulus were determined. Thermoviscoelastic models of material behavior were constructed using a numerical identification procedure, experimental data, and simulation models. The thermomechanics of materials during the deformation of the spherical support part of the bridge were analyzed. Temperature dependencies of the parameters of the contact stress-strain state were determined with an average coefficient of determination R² = 0.97 and an average error size RMSE = 0.092. Full article

Foam injection moulding (FIM) enables lightweight thermoplastic parts, but current process simulations do not resolve microstructure formation. This work presents a micro-scale CFD framework for FIM that captures gas–melt interaction and bubble morphology. A two-phase, compressible volume-of-fluid solver (OpenFOAM) with surface tension and viscoelastic Phan–Thien–Tanner rheology is coupled to a nucleation pre-processor based on classical nucleation theory, which places bubbles stochastically using macro-scale pressure and temperature histories. The approach was demonstrated on a plate geometry using a 2D through-thickness section to investigate bubble nucleation, deformation, coalescence, and interaction under realistic process conditions. The simulations reproduced characteristic morphology trends across the thickness. In particular, the predicted aspect ratio and orientation show the expected skin–core behaviour and agree qualitatively with experimental observations. These results demonstrate that the framework can describe morphology development beyond simplified spherical-cell assumptions and provides a proof of concept for multiscale coupling between macro-scale process conditions and micro-scale foam structure evolution. A simplified surrogate growth representation was used to enable bubble expansion; however, a physically based mass-transfer model is required for quantitatively accurate growth kinetics. Full article

Cat behavior recognition in unconstrained videos is important for animal welfare monitoring and veterinary assessment, yet remains challenging because behavior cues are often carried by highly deformable and intermittently visible parts such as the head and tail. This study aims to improve clip-level cat behavior recognition under unstable part visibility in real-world videos. We propose PMTNet, a part-centric temporal network for cat behavior recognition under unstable part visibility. The framework first detects the cat body, head, and tail using a DEIM-based detector, then selects a detector according to video-domain continuity and stability, and finally models behavior from ROI appearance features and explicit geometric motion cues. The framework was developed and evaluated using a part-detection dataset of 4000 training images and 500 validation images, together with a cat behavior dataset of 1283 video clips across five categories. In the best-performing setting, PMTNet achieved 93.1% Top-1 Accuracy and 90.9% Macro-F1. Ablation studies further suggest that detector choice in the video domain, complementary part cues, and missing-aware fusion all contribute to the final recognition performance. On the present dataset, PMTNet also outperformed representative end-to-end video recognition baselines. These results support the use of part-centric temporal modeling for cat behavior recognition in unconstrained real-world videos. Full article

This work addresses the quasi-static behavior of fiber-reinforced materials whose response is based on a hyperelastic formulation augmented by viscous and damage-like effects. A transversely isotropic constitutive model is developed within the framework of an internal scalar variable, enabling the reversible description of material damage while ensuring objectivity, thermodynamic admissibility and polyconvexity of the stored-energy function. The isotropic contribution is constructed from a generalized Ciarlet model, whereas the anisotropic part accounts for a family of elastic fibers embedded in a viscoelastic matrix, interpreted through a simple mixture theory. The resulting constitutive equations are implemented in Abaqus/Standard via a UMAT subroutine, and their rate form is derived consistently with the Zaremba–Jaumann objective stress rate. The performance of the model is examined by means of finite element simulations, including homogeneous tests in uniaxial strain and simple shear, relaxation and creep problems, and an inflation-like problem. The results demonstrate the capability of the model to capture strain-rate sensitivity, creep, stress relaxation and energy dissipation, as well as nonuniform deformation patterns, while highlighting its current limitation in representing permanent deformations. Full article

Dispensing-based in situ casting offers a practical route for introducing dense or mechanically distinct polymer regions into fused deposition modeling (FDM) parts during fabrication. This study investigates the curing-dependent process constraints governing stable integration of in situ casting within an FDM workflow. In the proposed process, FDM is used to fabricate thermoplastic confinement geometries, after which liquid polymer is dispensed into selected cavities and cured before printing resumes. Two representative curing systems were examined: a UV-curable photopolymer and a two-component epoxy resin. The experimental program included UV curing characterization under perpendicular 405 nm exposure, infrared thermal imaging of curing-induced heat generation and dissipation, confined curing of epoxy resin, layer-wise integration within an FDM-printed cavity, and a representative mechanical linkage demonstration. The results show that UV-based in situ casting is constrained by the coupled effects of curing depth, peak temperature, and visible deformation, making staged curing with intermediate thermal relaxation necessary for stable operation. In contrast, the epoxy system enabled bulk cavity filling with lower peak temperature, but required substantially longer curing time, introducing a different process limitation. A layer-wise UV curing strategy enabled successful stacking of four cast layers within an FDM-printed confinement without visible leakage or shell collapse. Mechanical testing of hybrid linkage specimens further showed that localized casting can modify structural stiffness through selective reinforcement. These findings demonstrate that dispensing-based in situ casting can be integrated into FDM when thermal, temporal, and filling constraints are explicitly managed, and they provide practical process guidance for hybrid polymer fabrication involving confined casting during printing. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

The selection of deformation mechanisms in hexagonal close-packed (HCP) metals is strongly influenced by both crystallographic orientation and macroscopic deformation constraints. In commercially pure titanium, plastic deformation under constrained loading conditions involves a complex interplay between dislocation slip and deformation twinning, whose respective activation cannot be fully described by classical stress-based criteria. In this study, the mechanical origin of slip and twinning variant selection in commercially pure titanium subjected to plane strain compression is investigated experimentally. Plane strain compression is used as a canonical loading condition representative of constrained deformation paths encountered in sheet metal forming. Interrupted in-situ electron backscatter diffraction is combined with slip trace and twin variant analyses to identify the active deformation mechanisms at the grain scale. Resolved shear stress calculations show that stress-based criteria provide a necessary first-order condition for the activation of both slip and twinning systems. While the Schmid factor reasonably predicts part of the observed slip activity, it fails to uniquely determine the selection of active twinning variants. A kinematic analysis reveals that twinning variant selection is governed by the compatibility between the deformation induced by twinning and the macroscopic strain constraints imposed by plane strain compression. Only variants whose deformation accommodates compression along the loading axis, extension along the free in-plane direction, and minimal strain along the constrained in-plane direction are preferentially activated. These results demonstrate that deformation mechanism selection in HCP titanium under constrained loading conditions results from a combined effect of resolved shear stress and kinematic compatibility. The proposed framework provides a physically grounded basis for interpreting deformation-induced texture evolution and offers clear perspectives for the development of crystal plasticity models incorporating twinning under complex strain paths. Full article

As autonomous driving technology progresses, efficient and accurate object detectors are able to detect pedestrians, vehicles, road signs, and obstacles in real time, thereby enhancing driving safety and serving as a part of autonomous driving. However, the performance of such object detectors is limited and cannot be leveraged to satisfy modern autonomous driving systems. To address this issue, we develop an object detection network for autonomous driving scenarios, SST-YOLO, which is based on YOLOv8. First, we propose a Sobel Convolution & Convolution (SCC) module to enhance the backbone, which incorporates a SobelConv branch to explicitly model gradient-based edge information and improve structural feature representation. In addition, we replace the original path aggregation feature pyramid network (PAFPN) with a Small Object Augmentation Pyramid Network (SOAPN), which integrates SPDConv and CSP-OmniKernel modules to strengthen multi-scale feature fusion and enhance small object representation. Finally, a Task-Adaptive Decomposition & Alignment Head (TADAHead) is designed, which employs task decomposition, dynamic deformable convolution, and classification-aware modulation to decouple tasks and achieve adaptive spatial alignment, thereby improving detection accuracy and robustness in complex scenarios. Experiments on the public autonomous driving dataset KITTI show that our proposed method outperforms the baseline YOLOv8 model. Compared with the baseline results, mAP@0.5:0.95 ranges from 65.1% to 69.2%, which indicates that the proposed SST-YOLO network can achieve object detection for autonomous cars. Full article

To accurately control the hot workability of 7050 aluminum alloy and determine the optimal process window, systematic hot compression experiments were carried out on the Gleeble-3500 thermal simulation test machine under the multi-group process conditions of deformation temperature 300~450 °C, strain rate 0.001~1 s⁻¹, and maximum deformation of 60%. The high-temperature rheological curve data were collected, and the key hot deformation parameters, such as deformation activation energy Q, Zener–Hollomon (Z) parameter, and power dissipation efficiency η, were calculated based on the experimental results. The random forest prediction model between process parameters and thermal deformation parameters was innovatively constructed to realize the accurate quantification of the parameter relationship. On this basis, the multi-objective process optimization was further carried out by coupling the TOPSIS and EWMs. Finally, the optimal hot deformation process parameters of 7050 aluminum alloy were determined as 410~450 °C and 0.001~1 s⁻¹. The microstructure analysis showed that the main deformation mechanism of the material in the optimized region was dynamic recrystallization, which could effectively ensure the microstructure uniformity and mechanical property stability of the formed parts. Full article

Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process. Full article