Search Results (3,335)

Titanium alloys exhibit exceptional strength-to-density ratios, high hardness, and outstanding resistance to elevated temperatures, making them indispensable structural materials in aerospace engineering, marine construction, and biomedical applications. In aerospace systems specifically, fatigue failure represents the predominant failure mode for titanium alloy components. This review systematically examines prevalent surface treatment techniques for titanium alloys—including shot peening, ultrasonic rolling treatment, hot isostatic pressing (HIP), physical vapor deposition (PVD), micro-arc oxidation (MAO), and thermal spray processes—and critically evaluates their respective effects on fatigue performance. The underlying mechanisms of each technique are concisely outlined, with emphasis on stress state evolution, near-surface microstructural refinement, and interfacial integrity. Building upon the characteristic surface-dominated fatigue fracture behavior of titanium alloys, this work focuses on how coating composition, architecture (e.g., graded, multilayer, or nanocomposite designs), and interfacial bonding strength govern fatigue resistance. A unified analysis is presented on the distinct yet complementary roles of substrate deformation strengthening (e.g., residual compression, grain refinement) and coating-mediated protection (e.g., barrier function, crack deflection, stress redistribution) during fatigue crack initiation and propagation. Key determinants of fatigue performance, including residual stress distribution, coating/substrate adhesion, thermal mismatch, and environmental degradation susceptibility, are rigorously assessed. Finally, emerging research frontiers are identified, including intelligent process–structure–property mapping, in situ monitoring of fatigue damage at coated interfaces, and design of multifunctional gradient coatings that synergistically enhance strength, wear resistance, and fatigue endurance of titanium alloy components. Full article

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads to the precipitation of dispersed M₂₃C₆ carbides (M = Cr, W), primarily along grain and deformation twin boundaries. After HTMT, these particles are mainly localized at grain and low-angle boundaries. With increasing aging time, both the size and volume fraction of the particles increase. In both states, the microtwin and substructure are partially retained after aging. Local regions corresponding to the early stages of recrystallization were identified after both treatments. These regions were associated with intense decomposition of the supersaturated solid solution and the coarsening of carbide particles. The mechanical properties were evaluated by tensile testing at 20, 650, and 700 °C. Aging reduced average ductility after both treatments and at all test temperatures, with this trend persisting with increasing aging time. After CR and aging, a significant scatter in elongation to failure was observed, with minimum values of ≈2–3%. This behavior is attributed to the high density of plate-like M₂₃C₆ carbides at grain and microtwin boundaries. Microcrack formation and intercrystalline fracture features were observed, directly linked to the high density of boundary carbides. These effects were less pronounced in the HTMT condition after aging. In this paper, strategies for suppressing carbide precipitation in high-manganese low-activation austenitic steels via chemical composition and thermomechanical processing optimization are discussed. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Background: Wearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data. Methods: Wearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance. Results: Axis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (p ≤ 0.01 for each metric). Composite variability was strongly associated with Mullen gross motor outcomes at 9 and 12 months of age (r = 0.55, p < 0.001). Conclusions: Overall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life. Full article

Service Level Agreement (SLA) compliance in hospital transport processes is essential in terms of patient safety, service continuity, and resource efficiency. However, transport requests occur as irregular events, limiting the applicability of equally spaced time-series assumptions. The presented study jointly addresses two complementary objectives in an event-based framework: predicting the interarrival time between consecutive transport requests (next-event forecasting) and forecasting the total request count within forward SLA horizons (forward-count forecasting). Machine learning methods such as Ridge Regression, Extra Trees, and Histogram-based Gradient Boosting, as well as deep learning architectures such as Long Short-Term Memory and Gated Recurrent Unit, were compared under different time horizons and adaptive history windows on time-stamped transport request records from the operational system supporting a private hospital in Turkey, including patient, specimen, and material transport requests. Results indicate that deep learning methods yield lower errors in demand count prediction at short time horizons; as the horizon lengthens, machine learning performs similarly and even outperforms in some cases; and as the history window increases, the prediction error for the next request occurrence systematically decreases. The lowest mean absolute error values in request counts were obtained for demand forecasting within a 30 min time window; 2.10 for material transport, 3.88 for patient transport, and 2.84 for specimen transport. Additionally, R² value reached 0.98 for next-event forecasting with a rolling-memory window of 20 events. Overall, the findings suggest that hospital transport demand is substantially predictable and that event-based forecasting can support SLA-oriented staffing, task dispatching, and delay mitigation. Full article

This study systematically investigates the hot deformation behavior and microstructure evolution of Ti-3Al-2.5V-0.5Ni alloy under compression at temperatures ranging from 800 °C to 1010 °C and strain rates ranging from 0.1 s⁻¹ to 10 s⁻¹, with a maximum deformation of 75% (with a corresponding true strain of 1.4). An Arrhenius-type constitutive equation was developed, and a hot processing map was established using a dynamic material model (DMM). Microstructural evolution was characterized using electron backscatter diffraction (EBSD). A hot processing map delineated stable and unstable regions. Regions with high power dissipation efficiency (η) were identified at deformation temperatures of 850–880 °C with strain rates of 0.1–10 s⁻¹, and at 940–960 °C with strain rates of 1.5–10 s⁻¹. These regions show high recrystallization fraction and good processing performance. The instability zone was observed at about 900 °C and high strain rate, which should be avoided during processing. The microstructure analysis of different power dissipation efficiency regions was carried out in detail. The results show that the power dissipation efficiency is about 0.38 at the deformation temperature of 950 °C and the strain rate of 0.1 s⁻¹, accompanied by high dynamic recrystallization. However, when the deformation condition is 800 °C and 10 s⁻¹, the power dissipation efficiency is lower than 0.18, the degree of recrystallization is limited, and a large number of dislocations accumulate. In summary, the large strain rolling of Ti-3Al-2.5V-0.5Ni alloy should be processed in the high-temperature α + β phase region (850–900 °C) and low-to-medium strain rate range of 0.1–5 s⁻¹. The process conditions can promote high recrystallization fraction, good processability, and weakened crystallographic texture, thereby minimizing the anisotropy of the final sheet. This study provides theoretical guidance for the optimization of industrial hot processing parameters of the alloy. Full article

A structured optimization approach was applied to ultrasonic surface rolling process (USRP) parameters, aiming to enhance the material surface characteristics of TC4 titanium alloy. To overcome the premature convergence and limited exploration capability of the standard Whale Optimization Algorithm (WOA), three enhancement strategies were introduced, including population initialization based on an optimal point set, a sinusoidal nonlinear convergence factor, and an adaptive inertia-based position update strategy. By optimizing the structural parameters of the RBF neural network with the improved WOA, an IWOA–RBF predictive model for surface performance evaluation was developed and rigorously validated in terms of prediction accuracy. Using the developed IWOA–RBF model, a multi-criteria decision-making framework integrating the CRITIC weighting method and the TOPSIS ranking approach was constructed to evaluate surface quality. This framework was further combined with a multi-objective Grey Wolf Optimization (MOGWO) algorithm to perform Pareto-based optimization and determine the optimal USRP parameter set. Experimental validation showed that the optimized parameters resulted in a significant reduction in surface roughness, while enhancing both surface hardness and residual compressive stress. The results confirm the robustness and effectiveness of the proposed IWOA–RBF and MOGWO optimization framework, providing a reliable strategy for high-precision parameter optimization and coordinated enhancement of surface properties in the TC4 titanium alloy USRP. Full article

The limited ductility of conventional titanium alloys significantly limits their application in critical load-bearing components. To overcome this limitation, a Ti-6Al-2Mo-2Nb-2Zr-2Sn titanium alloy (TC21) was subjected to warm rolling at 500 and 600 °C and aging treatment. Subsequently, microstructural characterization was conducted using scanning electron microscopy, electron backscatter diffraction and transmission electron microscopy, while the mechanical properties were tested by uniaxial tensile tests and nanoindentation tests. The sample warm rolled at 600 °C exhibited an optimal combination of strength and ductility, with an ultrahigh yield strength of 1138 MPa and an elongation-to-fracture of 7.3%. Aging treatment further enhanced the yield strength to 1263 MPa, while retaining a good ductility of 9.6%. The improved mechanical properties are mainly associated with the formation of nanoscale secondary α phase (α_s) lamellae caused by the aging treatment. Interface strengthening is identified as the primary strengthening mechanism. In particular, the optimal volume fraction and decreasing texture intensity of the soft phase contribute to the enhanced ductility. This work provides a method for viable thermo-mechanical processing for achieving an excellent strength–ductility combination in titanium alloys. Full article

Hydrological memory reflects the persistence of hydrological processes and plays an important role in understanding basin regime dynamics under changing climatic conditions. This study investigates the temporal stability of hydrological memory in the ten largest European basins: Volga, Danube, Dnieper, Don, Northern Dvina, Pechora, Neva, Rhine, Vistula, and Elbe. The analysis used rolling cross-correlation (CCF) and auto-correlation (ACF) functions calculated with a 50-month moving window to assess temporal changes in hydrological dependence structures. Additionally, an Instability Index was applied to quantify the variability of hydrological memory over time. The results indicate that the strongest correlations occur mainly at lag 0 and ±1, suggesting a relatively short hydrological memory in most basins. The lowest Instability Index was observed in the Volga basin, whereas the highest values were recorded in the Danube and Rhine basins. Full article

Accurate shape control is paramount for ensuring the quality of hot-rolled strip products, which is significantly challenged by the high dimensionality, inherent nonlinearity, and strong coupling of process parameters. While machine learning (ML) methods have demonstrated superior predictive performance in product quality modeling, the inherent “black-box” nature and lack of transparency severely undermine system reliability and hinder practical deployment. Existing explainable artificial intelligence (XAI) approaches predominantly rely on statistical correlations while overlooking the underlying causal mechanisms among coupled variables, which severely limits the validity of explanations. To address these limitations, a causal XAI diagnosis and optimization framework for hot-rolled strip shape is proposed. Initially, a hybrid causal structure learning module is established, which integrates domain knowledge with the NOTEARS-MLP algorithm to accurately reconstruct the causal topology and decode the complex coupling mechanisms among process parameters. Subsequently, a high-performance quality prediction module utilizing AutoML techniques is constructed to establish a robust predictive baseline. Furthermore, a causal XAI and quality optimization module is introduced, which incorporates causal constraints into standard Shapley additive explanation (SHAP) analysis for transparent diagnosis, and employs piecewise linear analysis (PLR) to generate sample-specific optimization strategies. Comprehensive experimental validation demonstrates that the prediction module significantly outperforms state-of-the-art ML approaches across multiple performance metrics. Additionally, comparative analysis reveals that the optimization strategy based on causal feature attribution exhibits 14.7% defect rate reduction over the associational baseline, which is effective, efficient and establishes a new benchmark for causal explainability in industrial process optimization applications. Full article

Lack of lubrication in rolling-element bearings is a leading root cause of premature failure in induction motors and other electromechanical drives. This study proposes novel vibration-based technologies for diagnosing a lack of lubrication in bearings of induction motors. Two technologies are proposed: the Filter-less spectral kurtosis (FLSK), which quantifies impulsive energy generated by a lack of bearing lubrication, and the fundamental rotational harmonic technology, which captures an increase in the fundamental rotational harmonic magnitude, also induced by a lack of bearing lubrication. Comprehensive experimental trials are performed on a Siemens induction gearmotor, used in airport baggage handling conveyor systems. The experimental results show that both technologies exhibit effective diagnostics. Full article

The increasing demands of application conditions urgently call for process innovations in high-performance 7xxx aluminum alloys. This study investigated the effect of differential speed rolling (DSR) on the microstructural evolution and mechanical properties of 7075 aluminum alloy subjected to DSR with a total reduction of 60%, followed by isothermal aging at 120 °C for 24 h. The results show that DSR promotes the development of grain refinement, defect accumulation, and deformation texture, while the corresponding strengthening effect exhibits a non-monotonic dependence on speed ratio. Among all conditions, the DSR2.0 sample exhibits the most favorable microstructure, characterized by the highest kernel average misorientation (KAM) value, the strongest deformation texture, and the finest as well as most densely distributed intragranular η′ precipitates. Accordingly, the DSR2.0 sample achieves the optimal strength–ductility balance, with a yield strength, ultimate tensile strength, elongation, and hardness of 582.26 MPa, 648.43 MPa, 10.75%, and 199.8 HV, respectively. Specifically, the deterioration in the properties of the DSR2.5 sample is attributed to localized recovery, shear inhomogeneity and coarsening of precipitates. The differential speed ratio enables effective optimization of the 7075 aluminum alloy by regulating the evolution of grains, dislocations, precipitate phases, and texture, among which precipitation strengthening is the dominant calculated contribution. Therefore, an appropriate differential speed ratio is key to achieving performance optimization. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

The quality of ground surfaces depends largely on the topography of the active surface of the grinding wheel, which, in turn, is determined both by the structure of the grinding wheel and by the conditions of the dressing process. This article proposes a new approach to improving the quality of ground surfaces by optimizing the dressing conditions with diamond rollers, based on the correlation between the roughness of the ground surfaces, the roughness of the cutting surface of the grinding wheel, and the parameters of the dressing process. A comprehensive theoretical–experimental study and modeling of the microgeometry of electrocorundum grinding wheels and the roughness of ground surfaces, depending on the dressing conditions with diamond dressing rolls made of medium- and high-strength synthetic diamonds with a mixed grit size, has been carried out. A complex quality indicator has been defined, determined as the ratio between the roughness of the ground surfaces and the roughness of the cutting surface of the grinding wheels, and models have been constructed for its determination, depending on the dressing conditions. By applying a genetic algorithm, optimal conditions for uni-directional and counter-directional dressing (dressing speed ratio, radial feed rate, the dress-out time and the ratio between the grit sizes of the diamond roller dresser and grinding wheel) have been determined, which ensure a minimum value of the complex quality indicator in combination with minimum roughness of the ground surfaces. Full article

The rapid advancement and widespread application of telecommunication technologies have significantly increased human exposure to electromagnetic waves, thereby intensifying the demand for effective electromagnetic shielding materials. Beyond potential health concerns, ensuring the stable performance of highly integrated electronic devices also necessitates protection against electromagnetic interference (EMI). In this study, the effects of processing conditions on the EMI shielding effectiveness (SE) of AZ61 magnesium alloy sheets were systematically investigated. Aging treatment of rolled AZ61 alloy promoted the formation of Mg₁₇Al₁₂ lamellae. Transmission Kikuchi diffraction analysis revealed that plate-like Mg₁₇Al₁₂ precipitates preferentially formed on the (0001) planes of the Mg matrix, contributing to improved EMI shielding. The rolled AZ61 sheet exhibited the highest SE in both the as-rolled state (83.1 dB at 900 MHz) and after aging for 131 h at 250 °C (76.2 dB at 900 MHz). The superior shielding performance of the as-rolled sheet is attributed to its high density of deformation-induced defects such as dislocations and twins, which induce lattice distortions and impede wave propagation. Meanwhile, the enhanced SE from the 131 h-aged condition results from multiple reflections of incident electromagnetic waves facilitated by the matrix–precipitate lamellar microstructure. Full article