Search Results (2,521)

Metallic nanolaminates generally show ultra-high strength but low ductility due to their vulnerability to shear instability during deformation. Herein, we report the simultaneous enhancement in hardness (by 11.9%) and suppression of shear instability in a 10 nm Cu/Zr nanolaminate, achieved by introducing a nanoscale Cu₆₃Zr₃₇ amorphous interfacial layer (AIL) between the crystalline Cu and Zr layers via magnetron sputtering. The effect of AIL and its thickness (h) (h = 2, 5, and 10 nm) on the hardness and shear instability behavior was explored using nano- and micro-indentation tests. An abnormal increase in hardness occurs at h = 2 nm when h is decreased from 10 to 2 nm, deviating from the prediction of the rule of mixtures. This abnormal strengthening is attributed to thinner AIL, which induces an increased density of crystalline/amorphous interfaces, thereby generating a pronounced interface strengthening effect. The micro-indentation results show that shear banding was suppressed in the nanolaminate with AIL, as evidenced by fewer shear bands as compared to its homogeneous counterpart. This enhanced resistance to shear instability may originate from the crystalline/amorphous interface that provides more sites for dislocation nucleation, emission, and annihilation. Furthermore, two distinct shear banding modes were observed in the nanolaminate with AIL; i.e., a cutting-like shear banding emerged at h = 10 nm, whereas a kinking-like shear banding occurred at h = 2 nm. The potential mechanism of the AIL-thickness-dependent shear banding was analyzed based on the crack propagation model of the Griffith criterion. This study provides a comprehensive insight into the strengthening and tunable shear instability of super-nano metallic laminates by AIL. Full article

In modern industrial manufacturing, the mechanical properties of large bearing housing castings are critical to equipment reliability and lifespan. Traditional prediction methods relying on experimental testing and empirical formulas face challenges such as high costs, limited samples, and inadequate generalization capabilities. This study presents a machine learning approach for predicting mechanical properties of ZG270-500 cast steel, integrating multivariate data (chemical composition, process parameters) to establish an efficient predictive model. Utilizing real-world production data from a certain foundry and forging plant, the research implemented preprocessing steps including outlier handling, data balancing, and normalization. A systematic comparison was conducted on the performance of four algorithms: Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that under small-sample conditions, the SVR model outperforms other models, achieving a coefficient of determination (R²) between 0.85 and 0.95 on the test set for mechanical properties. The root mean square errors (RMSE) for yield strength, tensile strength, elongation, reduction in area, and impact energy are 7.59 MPa, 7.52 MPa, 0.68%, 1.47%, and 5.51 J, respectively. Experimental validation confirmed relative errors between predicted and measured values below 4%. SHAP value analysis elucidated the influence mechanisms of key process parameters (e.g., pouring speed, normalization holding time) and elemental composition on mechanical properties. This research establishes an efficient data-driven approach for large casting performance prediction and provides a theoretical foundation for guiding process optimization, thereby addressing the research gap in performance prediction for large bearing housing castings. Full article

T-bar penetrometers have been widely used to measure strength parameters of marine clay in laboratory and in situ tests. However, using the deep resistance factor derived from full-flow conditions to evaluate the undrained shear strength of shallow clay layers may lead to significant underestimation. Furthermore, the deep resistance factor is inherently influenced by the strain-softening behavior of clay rather than maintaining the constant value (typically 10.5) as conventionally assumed in practice. To address this issue, large-deformation finite element (LDFE) simulations incorporating an advanced exponential strain-softening constitutive model were performed to replicate the full T-bar penetration process—from shallow embedment to deeper depths below the mudline. A series of parametric studies were conducted to examine the influence of key parameters on the resistance factor and the associated failure mechanisms during penetration. Based on numerical results, empirical formulas were derived to predict critical penetration depths for both trapped cavity formation and full-flow mechanism initiation. For penetration depths shallower than the full-flow depth, an expression for the softening correction factor was developed to calibrate the shallow resistance factor. Finally, combined with global optimization algorithms, a computer-aided back-analysis procedure was established to estimate strain-softening parameters using resistance-penetration curves from initial penetration tests in marine clay. The reliability of the back-analysis procedure was validated through extensive comparisons with a series of numerical simulation results. This procedure can be applied to the interpretation of T-bar in situ test results in soft marine clay, enabling the evaluation of its strain-softening behavior. Full article

The combined influence of specimen size and strain rate on the mechanical behaviour of small-diameter bamboo culms remains insufficiently characterised. This study investigates the longitudinal compressive strength of Chimonobambusa utilis through axial compression tests on specimens measuring 15 × 15 × 5 mm, 18 × 18 × 6 mm, and 21 × 21 × 7 mm under strain rates of 10⁻⁴, 10⁻³, and 10⁻² s⁻¹. Coupling experimental data with theoretical analysis, this study develops a size–strain rate interaction model to quantitatively assess the effects of specimen size and strain rate on the compressive strength of small-diameter bamboo. Increasing specimen size reduced strength and shifted failure modes from shear to buckling and splitting. At a strain rate of 10⁻⁴ s⁻¹, strength decreased from 73.35 MPa for the 15 × 15 × 5 mm specimens to 62.84 MPa for the 21 × 21 × 7 mm specimens. Conversely, increasing the strain rate from 10⁻⁴ s⁻¹ to 10⁻² s⁻¹ for the 15 × 15 × 5 mm specimens increased strength from 73.35 MPa to 80.27 MPa, indicating suppressed crack propagation. The Type II Weibull model exhibited higher predictive accuracy and parameter stability than the Type I variant. Coupling the Type II Weibull function with a power-law strain rate term and an interaction exponent developed a predictive equation, achieving relative errors below 5%. The findings demonstrate that specimen size predominantly governs strength, whereas strain rate exerts a secondary but enhancing influence. The proposed coupling model enables reliable axial load prediction for small-diameter bamboo culms, supporting material selection and dimensional optimisation in structural applications. Full article

China’s liquor industry continues to steadily expand and develop. The industry is currently transforming, shifting its focus from scale to quality and efficiency. This transformation is significantly increasing the demand for quality and safety testing. Currently, the testing system relies mainly on manual operation or traditional mechanical equipment. Technical bottlenecks include low testing efficiency, a significant imbalance in the cost–benefit ratio, and difficulty meeting the modern industry’s dual technical index requirements of testing accuracy and systematicity. In this context, the innovative research and development of new detection technology is key to promoting technological upgrades in the liquor industry. Near-infrared (NIR) spectroscopy is a core, competitive analytical method for non-destructive wine quality testing due to its technical advantages, such as non-destructive analysis, real-time online detection, and the absence of sample pretreatment requirements. This study systematically elaborates on the optical principle and detection mechanism of NIR spectroscopy and explores the application paradigm of chemometrics in spectral data analysis. This study covers the quantitative analysis of alcoholic strength, the determination of main ingredient content (sugar, acidity, esters, etc.), the construction of trace flavor substance fingerprints, the authentication and origin tracing of alcoholic products, and the monitoring of wine aging quality dynamics, among other key technology areas. Additionally, we review the fusion and innovation trends of artificial intelligence and big data technology, the R&D progress of miniaturized testing equipment, and the technical bottlenecks of spectral modeling and algorithm optimization. We also make scientific predictions about the evolution path of this technology and its industrial application prospects. Full article

This study investigates the influence of particle morphology on two-dimensional (2D) single rock particle breakage using the combined finite-discrete element method (FDEM) coupled with fractal dimension analysis. Three key shape descriptors (elongation index EI, roundness index Rd, and roughness index Rg) were systematically varied to generate realistic particle geometries using the Fourier transform and inverse Monte Carlo. Numerical uniaxial compression tests revealed distinct morphological influences: EI showed negligible impact on crushing strength or fragmentation, and Rd significantly increased crushing strength and fragmentation due to improved energy absorption and stress distribution. While Rg reduced strength through stress concentration at asperities, suppressing fragmentation and elastic energy storage. Fractal dimension analysis demonstrated an inverse linear correlation with crushing strength, confirming its predictive value for mechanical performance. The validated FDEM framework provides critical insights for optimizing granular materials in engineering applications requiring morphology-controlled fracture behavior. Full article

Modern dentistry depends on polymer composite materials for a wide range of applications. These materials, mainly composed of polymer resins and reinforced with inorganic fillers, offer mechanical strength, wear resistance, and durability for restorations and prosthetics. This study concentrated on the density and surface tension of monomers often used in dental resins and employed Quantitative Structure–Property Relationship (QSPR) modeling to investigate the influence of monomers’ structural features on these properties. Two main and two auxiliary models to predict both density and surface tension were built and validated. Additionally, two models based on CircuS descriptors were built and analyzed. Molecular descriptors from the models were interpreted and structural characteristics of dental monomers influencing their physicochemical properties were identified. It was found that the presence of heteroatoms increases both of the analyzed properties, while all of the other identified structural features exert an opposite influence on density and surface tension. Furthermore, the study showed that the density of dental monomers can be reliably predicted using the database containing regular organic compounds, but the surface tension requires the database containing specific monomers in order to perform satisfactorily. Full article

Recent developments in machine learning (ML), deep learning (DL), and statistical signal processing have led to substantial improvements in the accuracy of time series forecasting, particularly for environmental parameters such as temperature. The accuracy of air temperature prediction is not only vital for meteorological forecasting but also critically impacts agriculture, energy management, and environmental monitoring. In this study, a comprehensive modeling approach is proposed by incorporating both data-driven learning methods and classical signal processing techniques. Specifically, statistical models such as Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) are evaluated alongside modern neural network architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based attention mechanisms. The implemented models utilize key atmospheric variables—humidity, pressure, and past temperature values—to predict ambient temperature for future time horizons, such as one week and six months ahead. The SARIMAX model, which is grounded in digital signal processing theory, is particularly examined for its ability to capture seasonality and trend components in structured data. Meanwhile, deep learning models excel in learning complex, nonlinear temporal dependencies. Experimental results show that while LSTM performs well in short-term predictions (mean absolute error (MAE): 2.27, mean squared error (MSE): 6.63), the attention-based Transformer model is superior in capturing the predictions in the long term (MAE: 2.99, MSE: 14.92). SARIMAX, on the other hand, demonstrates a reliable performance in the short term compared to LSTM. These findings provide valuable insights into the strengths and limitations of each modeling approach, guiding future efforts in temperature forecasting and time series analysis. Full article

During the processes of excavation, restoration, and conservation of archaeological sites, it is common practice to perform physical and chemical characterization of the site materials. This is carried out to determine the best methods and materials for conserving and preserving the site. For this reason, techniques such as infrared spectroscopy and elemental analysis by X-ray fluorescence (XRF) are primarily used for chemical characterization, while mechanical tests such as the uniaxial compression test and hardness tests are used for physical and mechanical characterization. However, a common limitation is obtaining samples for destructive physical tests, such as compression tests, due to their invaluable cultural value. To address this problem, this work proposes the mechanical characterization of the material through nanoindentation. This technique requires a smaller sample size and can be performed in a timely manner by observing the resistance of each mineralogical phase present in the material. Thus, a preliminary predictive model of mechanical resistance is proposed based on the composition observed in the samples from the archaeological site of Cerro de los Remedios, located in the municipality of Comonfort, Guanajuato, Mexico. The samples were characterized using infrared spectroscopy, XRF, XRD, and SEM-EDS. The results indicate that the stone (caliche) is formed from 95.6–93% micrite calcite; 2.51–0.42% aluminosilicate; 3.14–1.89% high-calcium aluminosilicate; and 3.43–2.39 quartz or amorphous SiO₂. The proposed correlation models were adjusted to a linear function, a second-order polynomial, and a logarithmic function. In the M2–linear model, the non-linear effects generated by variables such as texture, porosity, phase adhesion, cement type, and cracks or discontinuities were not considered. In this model the best prediction of the experimental data was obtained within a variation of ±15%. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

This study aims to investigate the mechanical behavior of cement mortar and concrete through a hybrid approach that integrates artificial intelligence (AI) techniques with finite element modeling (FEM). Support Vector Machine (SVM) models with Radial Basis Function (RBF) and polynomial kernels, along with Multilayer Perceptron (MLP) neural networks, were employed to predict the compressive strength (Fc) and flexural strength (Fs) of cement mortar incorporating nano-silica (NS) and micro-silica (MS). The dataset comprises 89 samples characterized by six input parameters: water-to-cement ratio (W/C), sand-to-cement ratio (S/C), nano-silica-to-cement ratio (NS/C), micro-silica-to-cement ratio (MS/C), and curing age. Simultaneously, the axial compressive behavior of C20-grade concrete was numerically simulated using the Concrete Damage Plasticity (CDP) model in ABAQUS, with stress–strain responses benchmarked against the analytical models proposed by Mander, Hognestad, and Kent–Park. Due to the inherent limitations of the finite element software, it was not possible to define material models incorporating NS and MS; therefore, the simulations were conducted using the mechanical properties of conventional concrete. The SVM-RBF model demonstrated the highest predictive accuracy with RMSE values of 0.163 (R² = 0.993) for Fs and 0.422 (R² = 0.999) for Fc, while the Mander model showed the best agreement with experimental results among the FEM approaches. The study demonstrates that both the SVM-RBF and CDP-based modeling approaches serve as robust and complementary tools for accurately predicting the mechanical performance of cementitious composites. Furthermore, this research addresses the limitations of conventional FEM in capturing the effects of NS and MS, as well as the existing gap in integrated AI-FEM frameworks for blended cement mortars. Full article

In this research work, five different types of post-consumer plastics were mechanically ground into fine aggregate, and each type was used to prepare 2 in. (50 mm) mortar cubes by partial volumetric replacement of the sand. The purpose is to evaluate the effect of the plastic type and its shape on the density and the compressive strength of concrete. The plastic products used in this study are usually not collected by curbside recycling facilities and are discarded in landfills or incinerated. The different types of plastics investigated were Polyethylene terephthalate (PET), High-Density Polyethylene (HDPE), Polypropylene (PP), Polystyrene (PS), and Acrylonitrile Butadiene Styrene (ABS). A total of 180 cubes with 5%, 10%, and 15% replacement were prepared and tested for their densities at the age of 28 days and their compressive strengths at the ages of 7 and 28 days. This work concluded by proposing general equations to predict the reduction in the density and compressive strength of the mortar with the increment in the plastic replacement. Full article

To promote the recycling and reuse of waste concrete, this study investigated the comprehensive impact of recycled aggregate (RA) content and particle size on the mechanical properties of concrete. A novel equivalence parameter (λ_eq) of RA was developed to consider the influence of RA content and size on the mechanical properties of concrete. Empirical equations were developed using linear regression to describe the test results and predict the impact of content and size of RA on the mechanical properties of concrete. The results showed that the comprehensive impact on the mechanical strength of recycled concrete shows a certain regularity when the content and particle size of RA change simultaneously. The measured mechanical properties and regression equations provided a reference and basis for engineering applications, such as the processing of RA in a crushing plant, the design of mix proportions in concrete using RA, and the rapid assessment of mechanical properties on-site. This study provides a design method and technical path for green construction. Full article

A longitudinal magnetic field provides a new method for regulating the plasma velocity, pressure field, and temperature field of the TIG welding arc. However, the mechanism of action of the longitudinal magnetic field remains poorly understood. In order to address this problem, this paper develops a numerical model based on continuum mechanics. The mechanism of how magnetic field strength affects temperature, pressure field, plasma velocity, and potential was investigated. The geometric shape, temperature, pressure, and plasma velocity of the TIG welding arc under different magnetic fields were predicted. The results indicate that as magnetic field strength increases, the arc shape is compressed under the influence of magnetic forces, with the degree of compression increasing with magnetic field strength; plasma velocity gradually increases from 74 m/s at 0 mT to 296 m/s at 150 mT, but the velocity along the arc’s central axis first decreases and then increases with increasing magnetic field strength. As the magnetic field strength increases, a negative pressure first appears near the cathode, then expands toward the cathode, and finally toward the anode. During the expansion of the negative pressure, the maximum absolute value of the arc pressure increases by 12.72 times. Full article

To reduce reflective cracking in asphalt pavements, gravel base layers are commonly employed to disperse stress and delay structural damage. However, the loose nature of gravel bases results in complex interlayer contact conditions, typically involving interlocking between gravel particles in the base and aggregates in the asphalt surface course. In order to accurately simulate this interaction and to improve the interlayer shear performance, a mesoscale finite element model was developed and combined with macroscopic tests. Effects due to the type and amount of binder material, type of asphalt surface layer, and external loading on shear strength were systematically analyzed. The results indicate that SBS (Styrene–Butadiene–Styrene)-modified asphalt provides the highest interlayer strength, followed by SBR (Styrene–Butadiene Rubber)-modified emulsified asphalt and unmodified base bitumen. SBS (Styrene–Butadiene–Styrene)-modified asphalt achieves optimal interlaminar shear strength at a coating rate of 0.9 L/m². Additionally, shear strength increases with applied load but decreases with increasing void ratio and the nominal maximum aggregate size of the surface course in the analyzed spectra. Based on simulation and experimental data, an equivalent macro–meso predictive model relating shear strength to key influencing factors was established. This model effectively bridges mesoscale mechanisms and practical engineering applications, providing theoretical support for the design and performance optimization of asphalt pavements with gravel bases. Full article