Search Results (867)

This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector. Full article

Freeze–thaw cycles (FTCs) are a prevalent weathering process that threatens the stability of canal slopes in seasonally frozen regions. This study combines direct shear tests under multiple F-T cycles with coupled thermo-hydro-mechanical numerical modeling to investigate the failure mechanisms of slopes with different moisture contents (18%, 22%, 26%). The test results quantify a marked strength degradation, where the cohesion decreases to approximately 50% of its initial value and the internal friction angle is weakened by about 10% after 10 freeze–thaw cycles. The simulation reveals that temperature gradient-driven moisture migration is the core process, leading to a dynamic stress–strain concentration zone that propagates from the upper slope to the toe. The safety factors of the three soil specimens with different moisture contents fell below the critical threshold of 1.3. They registered values of 1.02, 0.99, and 0.78 within 44, 44, and 46 days, which subsequently induced shallow failure. The failure mechanism elucidated in this study enhances the understanding of freeze–thaw-induced slope instability in seasonally frozen regions. Full article

Assessing the stress–strain state around interacting mining excavations using the finite element method (FEM) is computationally expensive for parametric studies. This study evaluates tabular machine-learning surrogate models for the rapid prediction of full stress–strain fields in fractured rock masses treated as an equivalent continuum. A dataset of 1000 parametric FEM simulations using the elastoplastic generalized Hoek–Brown constitutive model was generated to train Random Forest, LightGBM, CatBoost, and Multilayer Perceptron (MLP) models based on geometric features. The results show that the best models achieve R² scores of 0.96–0.97 for stress components and 0.99 for total displacements. LightGBM and CatBoost provide the optimal balance between accuracy and computational cost, offering speed-ups of 15 to 70 times compared to FEM. While Random Forest yields slightly higher accuracy, it is resource-intensive. Conversely, MLP is the fastest but less accurate. These findings demonstrate that data-driven surrogates can effectively replace repeated FEM simulations, enabling efficient parametric analysis and intelligent design optimization for mine workings. Full article

This work presents a combined numerical and experimental investigation into the laser machining of aluminum alloy Al 1050 H14 using a high-power Continuous Wave (CW) fiber laser. Advanced three-dimensional, coupled thermal–structural Finite Element Method (FEM) simulations are developed to model key laser–material interaction processes, including laser-induced plastic deformation, laser etching, and engraving. Cases for both static single-shot and dynamic linear scanning laser beams are investigated. The developed numerical models incorporate a Gaussian heat source and the Johnson–Cook constitutive model to capture elastoplastic, damage, and thermal effects. The simulation results, which provide detailed insights into temperature gradients, displacement fields, and stress–strain evolution, are rigorously validated against experimental data. The experiments are conducted on an integrated setup comprising a 2 kW TRUMPF CW fiber laser hosted on a 3-axis CNC milling machine, with diagnostics including thermal imaging, thermocouples, white-light interferometry, and strain gauges. The strong agreement between simulations and measurements confirms the predictive capability of the developed FEM framework. Overall, this research establishes a reliable computational approach for optimizing laser parameters, such as power, dwell time, and scanning speed, to achieve precise control in metal surface treatment and modification applications. Full article

Understanding how strain is transferred across the interior of tectonic plates is fundamental to quantifying lithospheric deformation. The Central Anatolia Transition Zone (CATZ), situated between the North and East Anatolian fault systems, provides a unique natural laboratory for investigating how continental deformation evolves from localized faulting to distributed shear. In this study, we integrate InSAR analysis with Global Navigation Satellite System (GNSS) velocity data, and stress tensor inversion with supporting gravity and seismic datasets to characterize the geometry, kinematics, and geodynamic significance of the CATZ. The combined geodetic and geophysical observations reveal that the CATZ is a persistent, left-lateral deformation corridor (i.e., elongated zone of Earth’s crust that accommodates movement where the landmass on the opposite side of a fault system moves to the left relative to an observer) accommodating ~4 mm/yr of shear between the oppositely moving eastern and western sectors of the Anatolian Plate. Spatial coherence among LiCSAR-derived shear patterns, GNSS velocity gradients, and regional stress-field rotations defines the CATZ as a crustal- to lithospheric-scale transition zone linking the strike-slip domains of central Anatolia with the subduction zones of the Hellenic and Cyprus arcs. Stress inversion analyses delineate four subzones with systematic kinematic transitions: compressional regimes in the north, extensional fields in the central domain, and complex compressional–transtensional deformation toward the south. The CATZ coincides with zones of variable Moho depth, crustal thickness, and inferred lithospheric tearing within the retreating African slab, indicating a deep-seated origin. Its S-shaped curvature and long-term evolution since the late Miocene reflect progressive coupling between upper-crustal faulting and deeper lithospheric reorganization. Recognition of the CATZ as a lithospheric-scale transition zone, rather than a discrete active fault, refines the current understanding of Anatolia’s kinematic framework. This study demonstrates the capability of integrated satellite geodesy and stress modeling to resolve diffuse intra-plate deformation, offering a transferable approach for delineating similar transition zones in other continental regions. Full article

Energy harvesting systems face performance limitations, and existing optimizations are not always sufficient; this study addresses these gaps by enhancing piezoelectric energy systems. To improve the performance of piezoelectric energy harvesting systems, an optimization methodology is developed in this study. Since the mechanical strain distribution directly affects energy conversion efficiency, this issue is addressed through optimization of the thickness geometry of a common cantilever-type harvester elastic substrate element via a state-space gradient projection method combined with design sensitivity analysis. The gradient projection method is implemented in MATLAB R2024b software to determine the optimal elastic substrate design, after which the optimized design is simulated in COMSOL 6.3 Multiphysics for strain analysis in a transient study. The optimized cantilever designs are produced by 3D printing using a photopolymer and experimentally validated using piezo sensors and laser measurements for dynamic analysis. Theoretically compared with traditional uniform beams, the optimized cantilever designs maximize strain along the upper layer of the elastic substrate element, leading to a substantial increase in the energy conversion efficiency. This maximization is validated by experimental measurements showing a significant increase in strain in the elastic substrate (approximately 30% at the first eigenfrequency and 70% at the second). The correlation between the experimentally obtained data and the simulation results validates the optimization results. Deviation between the results did not exceed 3% and indicates that cantilever-type energy harvesters with optimized thickness profiles outperform traditional rectangular beams in energy conversion efficiency. Full article

This study examines the through-thickness microstructure and crystallographic texture evolution in friction-stir-welded (FSWed) AA5052-H32 and AA6061-T651 aluminum alloys using a tri-flats threaded pin tool. Optical microscopy and electron backscatter diffraction (EBSD) were employed to characterize grain morphology, boundary misorientation, and texture components across the weld thickness. Both alloys exhibited progressive grain refinement and increased high-angle grain boundary fractions from the top to the bottom of the stir zone due to combined thermal and strain gradients. The FSWed AA5052 displayed dominant {111}<110> and Y + γ fiber components at the upper and mid regions, whereas AA6061 showed more randomized textures. At the bottom region, both alloys developed rotated Goss {011}<01-1> and weak A ({112}<110>) and α fiber components. These results clarify how alloy strengthening mechanisms—solid-solution versus precipitation hardening—govern texture evolution under different strain-path and heat input conditions. The findings contribute to optimizing process parameters and material selection for structural-scale FSW aluminum joints in industrial applications such as bridge decks, transportation panels, and marine structures. Full article

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions. Full article

With the advancement of welding technology, the demand for 70S-6 welding wire steel has steadily increased in industries such as construction, automotive, pressure vessels, and line pipe manufacturing. To optimize the production process of the target material, this study utilized the finite-element software ABAQUS to numerically simulate the multi-pass finish rolling process of 70S-6 welding wire steel. The study investigates the effects of the key rolling parameters—pass distribution (8/10/12 passes), finish rolling temperature (860/880/900 °C), and rolling speed (0.5 Vp/1.0 Vp/1.5 Vp, here Vp denotes the reference industrial rolling speed) on the rolling force, temperature field, and equivalent stress/strain during finish rolling. The results show that the increased number of passes homogenizes deformation, reduces local stress concentration and enhances mechanical properties. Specifically, 12 passes reduce the peak rolling force from 250,972 N to 208,124 N, significantly enhancing stress and temperature uniformity across the section. Increasing the finish rolling temperature lowers the pass-averaged flow stress and attenuates rolling-force fluctuations. At 880 °C, the simulated core–surface temperature gradient is minimal (50 °C), whereas at 900 °C the gradient increases (80 °C) but the rolling-force histories exhibit a lower peak level and smaller low-frequency oscillations; thus 880 °C is preferable when through-thickness thermal uniformity is targeted, while 900 °C is more suitable when a smoother load response is required. Increasing the finish rolling speed from 0.5 Vp to 1.5 Vp reduces the peak rolling force from 233,165 N to 183,665 N and significantly damps low-frequency load oscillations. However, it concurrently intensifies stress localization at the outer-surface tracking points P3/P4, which are in direct contact with the rolls, where the local equivalent stress approaches 523 MPa, even though the overall strain distribution along the bar length becomes more uniform. Overall, the optimal processing window is identified as a 12-pass schedule, a finish rolling temperature of 880–900 °C, and a rolling speed of 1.0–1.5 Vp, which can improve both rolling quality and temperature and stress and strain uniformity. Full article

Modal decomposition, introduced by Fourier, expresses complex functions, such as sums of symmetric basis modes. However, convergence alone does not ensure structural fidelity. Discontinuities, sharp gradients, and localized features often lie outside the chosen basis’s symmetry class, producing artifacts such as the Gibbs overshoot. This study introduces a unified geometric framework for assessing when modal representations remain faithful by defining three symbolic invariants—curvature (κ), strain (τ), and compressibility (σ)—and their diagnostic ratio Γ = κ/τ. Together, these quantities measure how closely the geometry of a function aligns with the symmetry of its modal basis. The condition Γ < σ identifies the domain of structural closure: this is the region in which expansion preserves both accuracy and symmetry. Analytical demonstrations for Fourier, polynomial, and wavelet systems show that overshoot and ringing arise precisely where this inequality fails. Numerical illustrations confirm the predictive value of the invariants across discontinuous and continuous test functions. The framework reframes modal analysis as a problem of geometric compatibility rather than convergence alone, establishing quantitative criteria for closure-preserving transforms in mathematics, physics, and applied computation. It provides a general diagnostic for detecting when symmetry, curvature, and representation fall out of alignment, offering a new foundation for adaptive and structure-aware transform design. In practical terms, the invariants (κ, τ, σ) offer a diagnostic for identifying where modal systems preserve geometric structure and where they fail. Their link to symmetry arises because curvature measures structural deviation, strain measures representational effort within a given symmetry class, and compressibility quantifies efficiency. This geometric viewpoint complements classical convergence theory and clarifies why adaptive spectral methods, edge-aware transforms, multiscale PDE solvers, and learned operators benefit from locally increasing strain to restore the closure condition Γ < σ. These applications highlight the broader analytical and computational relevance of the closure framework. Full article

The study presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using recurrent neural networks, including SimpleRNN, LSTM, and GRU architectures. The experimental dataset was constructed from 100 to 250 loading–unloading cycles collected at seven loading frequencies (0.1, 0.3, 0.5, 1, 3, 5, and 10 Hz). The input features included the applied stress σ (MPa), the cycle number N (the Cycle parameter), and the indicator of the loading–unloading stage (UpDown). The output variable was the material strain ε (%). Data for training, validation, and testing were split according to the group-based principle using the Cycle parameter. Eighty percent of cycles were used for model training, while the remaining 20% were reserved for independent assessment of generalization performance. Additionally, 10% of the training portion was reserved for internal validation during training. Model accuracy was evaluated using MAE, MSE, MAPE, and the coefficient of determination R². All architectures achieved R² > 0.999 on the test sets. Generalization capability was further assessed on fully independent cycles 251, 260, 300, 350, 400, 450, and 500. Among all architectures, the LSTM network showed the highest accuracy and the most stable extrapolation, consistently reproducing hysteresis loops across frequencies 0.1–3 Hz and 10 Hz, whereas the GRU network showed the best performance at 5 Hz. Model interpretability using the Integrated Gradient (IG) method revealed that Stress is the dominant factor influencing the predicted strain, contributing the largest proportion to the overall feature importance. The UpDown parameter has a stable but secondary role, reflecting transitions between loading and unloading phases. The influence of the Cycle feature gradually increases with the cycle number, indicating the model’s ability to account for the accumulation of material fatigue effects. The obtained interpretability results confirm the physical plausibility of the model and enhance confidence in its predictions. Full article

In the context of evolving antifungal resistance and increasing reports of clinical outbreaks of non-albicans Candida spp. invasive infections, the rapid detection of resistant patterns is of the utmost importance. Currently, an azole-resistant Candida parapsilosis clinical outbreak is ongoing at Pisa University Hospital. Resistant isolates bear both Y132F and S862C amino acid substitutions. Based on the data and isolates retrieved during the clinical outbreak, mass spectrometry was used to investigate the differences between fluconazole-resistant and -susceptible clinical strains directly from yeast colonies isolated from agar culture media. A total of 39 isolates, 16 susceptible and 23 resistant, were included. Spectra were processed following a standardized pipeline. Several supervised machine learning classifiers such as Random Forest, Light Gradient Boosting Machine, and Support Vector Machine, with and without principal component analysis were implemented to discriminate resistant from susceptible isolates. Support Vector Machine with principal component analysis showed the highest sensitivity in detecting fluconazole resistance (100%). Despite these promising results, external prospective validation of the algorithm with a higher number of clinical isolates retrieved from multiple clinical centers is required. Full article

Improving the energy efficiency of advanced ultra-supercritical (USC) power plants by increasing steam operating temperature up to 700 °C can be achieved, at reduced cost, by using novel engineering design concepts, such as coated steam pipe systems manufactured from high temperature materials commonly used in current operational power plants. The durability of thermal barrier coatings (TBC) in advanced USC coal power systems is critically influenced by thermally grown oxide (TGO) evolution and interfacial stress under thermo-chemo-mechanical coupling. This study investigates a novel dual-pipe coating system comprising an inner P91 steel pipe with dual coatings and external cooling, designed to mitigate thermal mismatch stresses while operating at 700 °C. A finite element framework integrating thermo-chemo-mechanical coupling theory is developed to analyze TGO growth kinetics, oxygen diffusion, and interfacial stress evolution. Results reveal significant thermal gradients across the coating, reducing the inner pipe surface temperature to 560 °C under steady-state conditions. Oxygen diffusion and interfacial curvature drive non-uniform TGO thickening, with peak regions exhibiting 23% greater thickness than troughs after 500 h of oxidation. Stress analysis identifies axial stress dominance at top coat/TGO and TGO/bond coat interfaces, increasing from 570 MPa to 850 MPa due to constrained volumetric changes and incompatible growth strains. The parabolic TGO growth kinetics and stress redistribution mechanisms underscore the critical role of thermo-chemo-mechanical interactions in interfacial degradation. These research findings will facilitate the optimization of coating architectures and the enhancement of structural integrity in high-temperature energy systems. Meanwhile, clarifying the stress evolution within the coating can improve the ability to predict failures in USC coal power technology. Full article

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate. Full article

Joining high-strength, cold-drawn Cu-Mg alloy conductors is a critical challenge for ensuring the reliability of high-speed railway catenary systems. This study investigates the evolution of mechanical properties and microstructure in Cu-0.43 wt% Mg alloy wires joined by multi-pass butt-upset cold welding without special surface preparation. High-integrity joints were achieved, exhibiting a peak tensile strength of 624 MPa (~96% of the base material’s strength). After four upsetting processes, the tensile strength of the weld can reach 90% of the original strength, and the gains from subsequent upsetting processes are negligible. Microstructural analysis revealed the joining process is governed by localized severe shear deformation, which forges a distinct gradient microstructure. This includes a transition zone of fine, equiaxed-like grains formed by dynamic recrystallization/recovery, and a central zone featuring a nano-laminar structure, high dislocation density, and deformation twins. A multi-stage dynamic bonding mechanism is proposed. It progresses from initial contact via thin film theory to bond consolidation through a “mechanical self-cleaning” process, where extensive radial plastic flow effectively expels surface contaminants. This work clarifies the fundamental bonding principles for pre-strained, high-strength alloys under multi-pass cold welding, providing a scientific basis to optimize this heat-free joining technology for industrial applications. Full article