Search Results (1,467)

The design of modern railway vehicles is increasingly inseparable from advanced computation and programming. As engineering challenges become more complex, sophisticated numerical algorithms and powerful computational tools enable designers to solve problems that would otherwise be difficult, time-consuming, or even impossible to address using conventional methods. The global trend toward algorithm-based design and analysis is rapidly transforming the railway industry. Virtual prototypes and computer simulations have become essential elements of high-quality railway vehicle development. These simulations support a wide range of analyses, including statics, kinematics, dynamics, strength, durability, and reliability, covering both complete vehicles and their individual subsystems. Modern simulation tools can therefore provide comprehensive insight into the mechanical behavior of railway vehicles while also accounting for aspects related to technology, materials, operation, and other key engineering requirements. Against this background, the main objective of this study is to provide a comprehensive overview of computational optimization methods used in railway vehicle design. This study reviews the most widely applied numerical optimization procedures that have established a significant position in contemporary engineering practice and are particularly relevant to railway vehicle designers. The presented work describes the fundamental principles of the optimization process in railway vehicle design and provides a mathematical formulation of optimization problems. Particular attention is given to the optimization of the modal and spectral properties of railway vehicles, as well as to topology optimization, which offers new possibilities for developing lightweight and structurally efficient components. Three representative optimization problems are investigated to demonstrate the practical potential of these methods: the optimization of a corrugated sheet-metal structure, the main load-bearing rectangular profile of an open wagon, and a strut structure supporting the roof of a hopper wagon. In all three cases, the optimization objective was to minimize structural mass while preserving the required functional and mechanical properties. The results demonstrate the considerable potential of computational optimization in railway vehicle design. When an appropriate optimization method is selected and correctly applied, significant reductions in material consumption—and consequently in production costs—can be achieved without compromising the structural performance required for safe and reliable long-term operation. These findings highlight the important role of computational optimization as a powerful tool for developing lighter, more economical, and more efficient railway vehicles. Full article

Despite advances in digital engineering tools, early-stage sheet metal forming tooling design remains strongly dependent on the tacit knowledge of experienced engineers, making systematic knowledge reuse difficult. This study addresses the need for a structured method to capture, organize, retrieve, and reuse historical tooling knowledge during early design decision-making. A data-driven decision-support framework is proposed, in which previous tooling projects are represented as structured knowledge cases containing CAD-derived features and performance-related data. The framework is developed within a Case-Based Reasoning paradigm and consists of three modules: a Difficulty Assessment Module for estimating manufacturing difficulty, a Similarity Retrieval Module for identifying comparable historical cases, and a Performance Module for linking design decisions with maintenance and operational outcomes. A prototype implementation was developed, tested, and evaluated through expert validation workshops with industrial tooling partners. The results indicate that the proposed framework has the potential to support more interpretable and systematic early-stage tooling decisions by formalizing design knowledge and enabling the retrieval of relevant past cases. The study concludes that data-driven knowledge reuse can reduce reliance on individual experience, may potentially reduce the design lead time, and support early design stages. Full article

Changes in modal order alter the predictor dimension, pole-label swaps disrupt frequency continuity, and time-varying envelopes affect phasor magnitude and phase. Estimating these quantities independently can propagate errors across successive processing stages. A coordinated estimator is developed for the modal subspace, pole states, and regression parameters. An order confidence index combines the spectral gap, cumulative energy, and noise separation to select the model order and reconstruct the signal in one low-rank subspace. Variable-order recursive prediction and frequency–damping state association then form continuous pole trajectories, followed by adaptive smoothing and class-dependent unit-circle projection. The associated oscillatory and decaying direct-current (DC) poles update the Maclaurin regression atoms. Finite-window coupling is handled by either modal initialization followed by Gram iteration or a direct joint regularized solution, avoiding repeated leakage compensation. Tests with modal-order changes, frequency dynamics, modal crossings, amplitude modulation, and decaying DC show that the coordinated parameter chain preserves pole identity and improves dynamic phasor measurement. In the main dynamic test case, the mean and 95th-percentile total vector errors (TVEs) are 3.2082% and 6.0672%; the 95% paired confidence interval for the mean-TVE difference between the proposed method and estimation of signal parameters via rotational invariance techniques (ESPRIT) remains below zero. A separate RK3568 bare-metal test of the standalone three-tone Prony kernel completed 800 frames without a processing failure. Its mean processing time was 18.621 ms per frame, with observed values from 18.537 to 19.070 ms. Full article

Nowadays, the application of different materials is quite common to optimise the performance of structural components. In this context, bonding between metals and composites becomes a frequent strategy. However, the appropriate design requires special care owing to the complex stress profile in the adhesive layer, caused by the stiffness mismatch inherent to these solutions. A crucial aspect relies on fracture characterisation under mode I loading of bi-material bonded joints. Therefore, in this work, two different approaches are analysed regarding the attainment of an almost pure mode I of Carbon–Epoxy/Aluminium bonded joints using the asymmetric double cantilever beam test. One method relies on a global approach that matches the bending stiffness of the specimen arms, while the second, the longitudinal strain-based criterion, matches the longitudinal strain distributions of the two adherends at the bondline, making it a local method. The global process was validated against experimental results and subsequently compared to the local methodology. Additionally, several combinations of metallic and fibre-reinforced polymers were analysed numerically using the finite element method with a cohesive zone model to compare results from the two procedures. The consistently low presence of mode II loading across all cases demonstrates that both the local and global approaches accurately capture a predominant mode I loading condition under the investigated scenarios. Full article

Background: Helicobacter pylori infection is a common chronic bacterial infection and an established cause of gastritis, peptic ulcer disease, and gastric cancer. Environmental and host metal status may interact with gastric inflammatory and microbial processes, and infection-related changes in gastric acidity, mucosal permeability, and metal handling may in turn alter local tissue concentrations. This study compared systemic and gastric tissue concentrations of seven elements in adults with histopathologically confirmed H. pylori infection and uninfected controls. Methods: This single-center case–control study included 110 adults undergoing upper gastrointestinal endoscopy (55 H. pylori-positive and 55 H. pylori-negative). H. pylori status was established by hematoxylin–eosin and modified Giemsa staining. Whole blood collected in purple-top K₂EDTA tubes was analyzed for As, Pb, Cd, and Sn; serum obtained from yellow-top serum-separator tubes containing clot activator and separator gel was analyzed for Al, Co, and Ni. A separate fresh antral biopsy, not the formalin-fixed histology specimen, was used for ICP-MS; tissue concentrations were normalized to recorded dry weight. Between-group metal comparisons were corrected as one family of 14 tests using Benjamini–Hochberg FDR. Prespecified age- and sex-adjusted models and an exploratory multivariable model were supplemented by ROC, PCA, and internally cross-validated PLS-DA analyses. Results: The H. pylori-positive group was older than the negative group (55.65 ± 15.01 vs. 52.42 ± 12.53 years). Blood aluminum was nominally higher in the positive group (p = 0.032; q = 0.112). In gastric tissue, aluminum was higher [0.23 (0.13–13.40) vs. 0.13 (0.10–0.23) μg/g dry weight; p < 0.001; q = 0.002], whereas arsenic was lower [1.35 (0.54–2.02) vs. 1.92 (1.47–2.43) μg/g dry weight; p < 0.001; q = 0.003] in H. pylori-positive participants. Tissue cobalt and blood/tissue nickel differences did not remain significant after FDR correction. In prespecified age- and sex-adjusted models, tissue arsenic showed an inverse association (OR = 0.431, 95% CI 0.263–0.705; p = 0.0008), whereas tissue aluminum showed a positive association (OR = 4.957, 95% CI 1.629–15.085; p = 0.0048). In the exploratory joint model, only tissue aluminum remained significant (adjusted OR = 4.016, 95% CI 1.119–14.417; p = 0.033). The joint model had an apparent within-cohort AUC of 0.774; repeated five-fold cross-validated PLS-DA yielded an AUC of 0.707. Conclusions: Histopathologically confirmed H. pylori status was associated with compartment-specific metal concentration patterns, particularly higher tissue aluminum and lower tissue arsenic. Because the study was cross-sectional and measured total concentrations, these findings do not establish environmental exposure, temporal accumulation, toxicity, or a causal direction. The regression and multivariate findings are exploratory and require validation in independent cohorts with standardized tissue sampling, arsenic speciation, exposure assessment, and quantitative gastric pathology. Full article

Background: Aorto-iliac Trans-Atlantic Inter-Society Consensus II (TASC II) C and D lesions with involvement of the aortic bifurcation are complex, and their endovascular treatment with kissing stenting can be technically demanding. The aim of this study is to analyze the outcomes, risk factors and follow-up of aorto-iliac revascularization through kissing stenting. Methods: A single-center, retrospective, observational study was performed including patients treated with aorto-iliac kissing stenting from 2016 to 2025 for TASC II C and D lesions. Pre-/peri- and post-operative data were prospectively collected and retrospectively analyzed. Aorto-iliac calcification consisted of calcific lesions involving more than 70% of the aortic circumference. Technical and clinical success, primary patency and reintervention rate were analyzed. Results: Overall, 123 patients were included, 48 (39%) female; mean age was 68 ± 7 years. Sixty-nine patients (56%) had critical limb-threatening ischemia, 46 (37%) diabetes mellitus, 5 (4%) end-stage chronic kidney disease in hemodialysis and 68 (55%) aortic calcification. Femoral accesses were percutaneous in 18 (15%) and surgical in 105 (85%) cases; additional brachial access was used in 49 (40%) patients. Simultaneous femoral endarterectomy was performed in 48 (39%) cases. Stentgrafts and bare metal stents were used in 32 (26%) and 91 (74%) cases, respectively. Technical success was 100%. Primary patency at 1, 3 and 5 years was 100%, 98 ± 3% and 94 ± 5%, respectively. At a mean follow up of 39 months, 6 (5%) stent thromboses occurred. Freedom from reintervention at 1, 3 and 5 years was 99%, 93% and 81%, respectively. Calcification was associated with a higher 3-year reintervention rate: 13% vs. 0%, p = 0.037. Brachial access, additional femoral endarterectomy and use of covered stents did not affect the reintervention rate. Conclusions: Kissing stent is an effective and safe technique to provide revascularization in aorto-iliac TASC C-D lesions with involvement of the aortic bifurcation, with good outcomes in terms of technical, clinical success and primary patency. It often needs an upper limb access and/or adjunctive procedures such as femoral endarterectomy. Presence of severe calcification is associated with a higher reintervention rate. Full article

Today, the mechanical processing and treatment of metals are of great importance in the development of industrial sectors. Therefore, in the process of mechanical metalworking, a need arises to create new modern equipment or improve existing devices. In industrial sectors, there are several problems with devices and machine tools (such as the 1K62 lathe-screw cutting machine) used in the mechanical processing of metals, including the turning of cylindrical and conical parts, machining their external and internal surfaces, thread cutting, and surface polishing. Specifically, it is difficult to precisely control the spindle speed during the metal-cutting process; energy consumption is high due to the high starting current (5–7 times the nominal value) of asynchronous motors during machine operation; the vibration level is high during operation; and there is no torque control. These problems can be eliminated by using scalar (simple) and vector control methods of frequency converters. In this case, the vector control method was used in the research because it has several advantages as a solution to these problems. As a result, it was demonstrated during the research process that the spindle speed can be reduced to 12.5 rpm. This made it possible to precisely control the spindle speed, maintain torque at low speeds, increase energy efficiency, reduce the load on mechanical transmissions, and ensure the stability of the cutting process. In addition, by operating the asynchronous electric motor through a frequency converter, it is possible to increase the efficiency and service life of such machine tools. This is of significant importance in the process of optimizing the electrical power supply system of the machine tool. Full article

Friction stir welding (FSW) is a solid-state manufacturing process widely used in joining aluminum and other metal workpieces. The FSW process can be modeled by a coupled system of non-Newtonian Navier–Stokes and heat-transfer equations. However, solving this non-linear system with high accuracy requires significant computational power. This work refines the system by introducing corrected coefficients and new treatments for boundary conditions near the tool. Then, model order reduction, including the Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM), is applied to efficiently solve the FSW system in a low-dimensional space. To enhance accuracy and effectiveness, two novel treatments have been adopted for the POD and DEIM models: the introduction of preconditioner matrices to avoid large condition numbers and the use of indicator matrices to generate the non-linear data. For different cases regarding operating parameters, the results show that the DEIM model dramatically speeds up the computation (e.g., over 250 times faster compared with the full model) while maintaining accuracy. This makes simulations of the FSW process more accessible and easily combined with machine learning techniques in future study. Full article

Nanocrystalline (NC) alloys hold significant promise as structural alloys due to their superior mechanical properties over the traditional coarser grained microcrystalline alloys. Often, there is an optimal range of mean grain size for most metals about which maximum material strength can be realized. In the context of NC alloys, stabilization of the grain size in this optimal range is one of the primary synthesis challenges. A large volume fraction of NC alloy microstructure is occupied by grain boundaries (GBs). Since GBs increase the internal surface energy of the system, during solidification and grain growth phases, there is a tendency to minimize GBs through grain coarsening. However, in NC alloys, phenomena like GB–solute segregation and solute precipitation are active and mitigate grain growth and thus stabilize the desired small grains. Numerically modeling these phenomena of GB–solute interactions, and the evolution of these stabilized GBs under mechanical load, is of immense interest to the NC alloy community. To enrich the numerical modeling formulations available in this space, we present here a phase-field-method-based numerical framework to model GB segregation, solute precipitation and effect of external loading on NC alloys. While some of these effects have been modeled in isolation, a unified treatment of the solute–GB segregation-related effects and its coupling with mechanics has not be considered in the literature. We present a three-dimensional, finite element method (FEM)-based, finite-strain phase-field formulation for modeling grain evolution and microstructure stabilization in NC alloys. Beyond the formulation and its computational implementation, various case studies demonstrate the applicability of this framework. Further, thermodynamic and kinetic arguments are provided based on the evolution of GB energy to explain the effects of solute drag, GB pinning and mechanical deformation. Full article

Background and Objective: Laser marking has emerged as a promising technique for producing controlled surface modifications on metallic substrates. This preliminary material-level feasibility study investigates the Infrared (IR) direct pulsed-laser marking (DP-LM) of flat copper and brass (CuZn37) surrogate specimens for potential ammunition-related marking applications. Methods: Alphanumeric and quick-response code markings were produced using selected laser-processing conditions and evaluated in terms of subjective visual clarity, surface roughness, readability and functional scannability. The investigation was limited only to the flat surfaced copper and brass substrates, while excluding the aging effects, curve surfaced ammunition components, mechanical loading, deformation, abrasion, or handling conditions arising from firearm feeding, chambering, firing, and extraction mechanisms. Results: The findings demonstrate that enhanced direct laser marking conditions maintained the controlled surface roughness profile and produced visually distinguishable and functionally scannable markings on the studied material. These findings provide preliminary material-level proof that IR DP-LM justifies further investigations. Conclusions: Motivated by the need for optimized identifiers for law enforcement organizations, future investigations on actual cartridge cases and under representative service conditions together with standardized verification procedures are required before operational conclusion can be drawn. Full article

Material extrusion 3D printing has been widely used in industrial, educational and residential environments. However, the associated emissions and exposure health impacts need to be evaluated, especially for the new and emerging filament materials. This study characterized particle and chemical emissions from 3D printing using five different metal composite filaments, which contain over 90% (by weight) of metal powder blended with polymer binders. The emission characterization was conducted using an exposure chamber following a standard testing method. Real-time particle measurements showed that particle emission rates ranged from 8 × 10⁹ to 2 × 10¹¹ particles/h for particle number and 200 to 1400 µg/h for particle mass. Over 98% of the emitted particles were smaller than 1 µm, which poses an inhalation hazard. Inductively coupled plasma–mass spectrometry analysis detected manganese, copper, zinc, and selenium in emitted particles from all filaments. However, metal powder in raw filaments tended not to be transferred into particle emissions, resulting in the total metals (and metalloids) accounting for 0.07% to 0.95% of emitted particle mass. Sorbent tube sampling and analytical analyses showed various volatile organic compounds emitted during printing, including hydrocarbons, alcohols, aldehydes, and aromatic compounds. Overall, metal composite filaments generated lower levels of volatile organic compounds (VOCs) compared to thermoplastic polymer filaments; in addition, the emitted VOC compositions differed. Organic chemicals associated with metal composite filament emissions included formaldehyde, benzaldehyde, acetaldehyde, naphthalene, and trimethylbenzene, exposure to which may cause irritations or other adverse health impacts. This study estimated personal exposure to hazardous components assuming a person is close to the printer with low ventilation to represent an acute worst-case exposure scenario. Modeled personal exposure to emissions showed potential exceedances of exposure to fine (PM_2.5) and coarse (PM₁₀) particulate matter, arsenic, manganese, formaldehyde, caprolactam, acetaldehyde, and naphthalene compared to reference levels. A modeled office room with ventilation showed reduced exposure levels by up to two orders of magnitude, assuming the same emission source. Users can avoid close proximity to an operating printer and increase dilution through larger room volumes and higher air change rates to reduce potential inhalation exposures at given printing conditions. Full article

Accurate identification of complex crack networks with branching and intersecting topologies remains a challenge in aerospace and civil engineering. Conventional non-destructive testing techniques are constrained by limited coverage and equipment access requirements, while model-based inverse methods face the curse of dimensionality and high computational cost when characterizing intricate crack morphologies. To address these limitations, a Binary-encoded Transformable Modular Component (BTMC) method is proposed, which abstracts complex crack morphologies into combinations of modular components representing elementary crack topological operations and encodes their parameters into a unified binary genotype. This representation converts the high-dimensional continuous inverse problem into a discrete combinatorial optimization task over a bounded search space, and the extended finite element method is coupled with a genetic algorithm for forward analysis and parameter optimization. Numerical simulations covering non-intersecting cracks, intersecting networks, and irregular morphologies beyond the component library demonstrate that the method maintains stable identification accuracy under measurement noise up to 10%. Experimental verification on a metal tensile plate and a wing surface curved-shell structure confirms that the identified configurations are mechanically consistent with the measurements, with the strain-response error on the wing surface reduced from 8.67% for the traditional genetic algorithm to 2.27% for BTMC. Across all test cases, the BTMC method converges in fewer generations with a total identification time of approximately 16 min on average, which provides a computationally efficient framework for online structural health monitoring of aircraft structures. Full article

The building industry is the largest consumer of energy and the largest source of carbon emissions, and the sustainable development of building envelopes is thus inevitable. Among the facade systems, metal cladding materials offer a number of benefits such as high durability, architectural freedom, and recyclability; however, the choice of these materials needs to be based on several performance aspects. This research presents a novel comprehensive approach for analyzing metal cladding options in a health care building through the integration of building energy simulation and multi-attribute decision analysis (MADA) techniques. A primary health care facility in Istanbul, Türkiye, was used as a case example. Eight metal cladding materials—steel, aluminum, copper, zinc, titanium, stainless steel, Corten steel, and magnesium alloy—were evaluated against various wall and insulation combinations. The assessment combined energy with physical–mechanical, thermal, acoustic, and sustainability indicators such as density, thermal conductivity, Young’s modulus, damping capacity, traffic noise insulation, service life, and recyclability. A series of building energy simulations was performed to estimate the effect of facade design options on yearly energy consumption, and the resulting data set was analyzed based on the CRITIC, LOPCOW and ALPAS methods. This method allows the comprehensive evaluation of metal cladding material on energy efficiency, structural strength, acoustic performance, durability, and circularity simultaneously. The results provide a practical decision support framework for sustainable facade material selection in health care and other energy-intensive buildings. Titanium emerged as the optimal metal cladding material, distinguished by its superior combination of low thermal conductivity, damping capacity, and recyclability under both CRITIC and LOPCOW weighting schemes. Comparative analysis across nine MADA methods (ρ = 0.932) and sensitivity analysis over 70 scenarios confirmed the robustness of this finding, with titanium retaining first place in 64 out of 70 perturbation scenarios. These outcomes provide materials engineering insight into how the mechanical, thermal, and durability characteristics of structural metals and alloys translate into differentiated in-service performance, offering evidence-based guidance for metal selection in facade applications. The outcomes reported relate to one health care facility located in Istanbul and demonstrate the potential of the novel framework for the specific investigated case but are not intended to be generalized across all building types and climatic zones or to provide universally applicable material rankings. Full article

Background and Clinical Significance: Implant impressions can present inaccuracies, affected by many factors, such as impression technique, impression material, and parallelism or lack thereof among the implants. The use of a verification index can assess the accuracy of the mastercast and can be used in order to adjust the inaccuracy of the mastercast. This article presents a technical variation in established verification-index procedures, followed by implant analog repositioning, which can be used in case of an impression inaccuracy; Case Presentation: A patient with a moderate gag reflex received an implant. The impression of an implant and a prepared tooth was made. At the metal try-in, an inaccuracy of the mastercast was found, attributed to the final impression. A technique was illustrated, describing a polymethyl methacrylate (PMMA) verification index fabrication, followed by implant analog repositioning in the removable die of the master cast with the use of acrylic resin. This method may be used as an alternative to repeating the impression in selected clinical situations, but it is time-consuming, technique-sensitive, and needs meticulous handling; Conclusions: This is a viable technique and may represent an alternative to repeating the impression in selected clinical situations. Therefore, the clinical workflow can be reduced by one appointment. It can be helpful for patients with a moderate to severe gag reflex, since it can minimize the discomfort and stress for the patient and the dentist. Full article

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, while spatiotemporal matching methods based on volume scan data suffer from interpolation and matching inaccuracies. To address these issues, this study proposes a collaborative calibration method for X-band radar networks based on opposing Range–Height Indicator (RHI) scans. The method uses a rigorously calibrated reference radar as a benchmark and performs opposing RHI scans with the radar under calibration to obtain synchronized observations within the spatial overlap region. Precise spatial matching is achieved using the nearest-neighbor algorithm based on beam-broadening cross-coverage thresholds, and bias is extracted using both the midline 9-point averaging method (midline method) and spatially constrained regional Statistics method (regional method). Based on a total of 58 sets of opposing RHI scanning cases conducted under stratiform precipitation, scattered precipitation, and weak cloud conditions, the results show that under conditions where echo continuity is maintained near the midline of stratiform and scattered precipitation, both the midline method and the regional method can obtain stable matching data. The midline method achieves a median correlation coefficient (0.821–0.942) higher than that of the regional method (0.860–0.872), and its bias standard deviation remains relatively stable (midline method: 1.39–2.20 dB; regional method: 2.61–3.16 dB). Continuous RHI calibration tests confirm that within a 30-min window, the fluctuation of the data matching correlation coefficient is less than 0.05, and the fluctuation of the bias mean is controlled within ±0.3 dB. Under weak cloud conditions, although the midline method can still achieve a high correlation coefficient, the correctness of its results still requires auxiliary validation through other calibration means. This study provides a relatively efficient and effective technical approach for the automated collaborative calibration of dense X-band radar networks. Full article