Search Results (1,010)

There is increasing interest in structured layered double hydroxide (LDH)-based catalysts because they combine tunable acid–base/redox chemistry with reactor architectures that can reduce diffusion lengths, improve heat management, and lower pressure-drop penalties. This review evaluates LDH, LDH-derived oxide (LDO/MMO), reduced metal/LDO, reconstructed hydroxide-rich, and mixed dynamic states integrated into honeycomb monoliths, open-cell foams, meshes/felts, thin films, washcoats, coated plates, microchannels, capillaries, and additively manufactured lattices. To move beyond descriptive comparison, the literature is assessed using unified evaluation dimensions: operative active state, support architecture, coating/integration route, active-phase loading, coating thickness and uniformity, reactor-volume-normalized productivity or STY, ΔP/L, axial/radial thermal gradients, time-on-stream, coating loss, regeneration recovery, and pilot-readiness. Representative benchmarks illustrate both the promise and reporting gaps of the field: NiFe-LDH-derived monoliths for CO₂ methanation have reached ~70% CO₂ conversion at 300 °C with >90% CH₄ selectivity and only 0.7% post-test mass loss; NiFe-LDH/iron-foam monoliths retained 85% ozone conversion after 168 h; high-entropy LDH-derived oxides showed T50/T90 values of 246/254 °C for toluene oxidation; and Au/LDH capillary films achieved 31.9% glycerol carbonate yield and 3.78 g h⁻¹ g⁻¹ productivity. The strongest current cases are pollution abatement and CO₂ methanation, whereas biomass upgrading, fine-chemical flow, high-entropy coatings, and photo/electrocatalytic films require deeper module-level validation. Overall, structured LDH catalysts should be treated as coupled chemistry–coating–reactor systems whose performance must be judged simultaneously by activity, accessible catalyst inventory, transport efficiency, pressure drop, thermal profile, durability, regeneration, and manufacturability. Full article

Tool wear significantly impacts the productivity and efficiency of sheet metal stamping operations, particularly in high-volume progressive die applications. This study presents an iterative calibration framework for Archard’s wear model, tailored to industrial stamping processes. The proposed methodology integrates finite element simulations with experimentally measured wear data obtained from production components, enabling data-driven calibration of the wear coefficient $K_{sim}$. The framework achieves high predictive accuracy, with deviations of 1.4–3.7% between simulated and optically measured wear depths and localization, after more than 15 million strokes. Rapid convergence is obtained within two to three calibration cycles, significantly reducing computational effort while maintaining physical fidelity. The simulation setup incorporates detailed modelling of contact pressure, sliding velocity, and stress distribution, validated using optical surface measurement systems and coordinate-based metrology. Beyond the specific industrial case, the framework demonstrates transferability to other sheet metal forming processes, such as bending, blanking, and coining, by leveraging physically based parameter adaptation across comparable pressure–velocity regimes. The approach enables predictive wear modeling in data-scarce environments and supports early-stage tool design workflows. Overall, the proposed methodology bridges the gap between empirical calibration and generalized simulation, contributing both methodological rigour and practical applicability to manufacturing science. Full article

The present study addresses the fabrication of porous gyroid architectures by additive manufacturing from preceramic polymer feedstocks. Photocurable emulsions were engineered by combining a silicone powder with acrylate monomers and dispersing an emulsified secondary phase of calcium nitrate. The formulations showed light-curing behaviour compatible with digital light processing vat photopolymerization (DLP-VPP), enabling high-fidelity replication of triply periodic minimal surface (TPMS) gyroids (designed porosity: 85 vol.%). After pyrolysis in nitrogen at 700 °C, the lattices converted into CaO–SiO₂-derived amorphous matrices embedding an in situ turbostratic/pyrolytic carbon fraction, as suggested by the photothermal response and preliminary impedance behaviour, although the latter was measured in liquid electrolyte and therefore does not isolate electronic transport. To improve robustness during polymer-to-ceramic conversion, pharmaceutical borosilicate waste glass (BASG) was added as a passive filler (30–70 wt.%). The waste-glass phase acts as a passive filler that improves processing robustness and can mitigate shrinkage-induced damage during pyrolysis, while remaining electrically insulating (dielectric) and therefore not directly contributing to electronic conduction. The resulting structures combine high surface-to-volume ratio, controlled open porosity, and structural integrity with electrochemical responsiveness under the adopted test conditions, making them promising architected platforms for electrochemical components where interconnected porosity is advantageous. Full article

This study proposes a condition-based maintenance monitoring method based on Geometry-based Optical Focus Metrology (GOFM) to detect wafer table edge deterioration early and enable proactive interventions before actual Critical Dimension (CD) bridge defects occur. In advanced Deep Ultraviolet (DUV) immersion photolithography, prolonged equipment operation mechanically wears the wafer table, inducing Edge-Roll-Off (ERO). Because conventional optical metrology struggles to separate this localized defocus from process noise, this work utilizes the existing GOFM technique to isolate the pure focus residual within the 140–147 mm radius region. To quantify this hardware-specific degradation, a mathematical dual-indicator system was constructed. This framework integrates a statistical threshold, the Range Percentile 97%, to reject baseline measurement noise, and a geometric variable, Slope × 3, to capture the topographical drop in the outermost 3 mm. Analysis of long-term time-series data from multiple High-Volume Manufacturing (HVM) scanners confirmed a strong correlation ($R^2=0.93$) between these indicators. Furthermore, we proved that the drift trajectory of Slope × 3 deterministically predicts mechanical failure prior to defect occurrence on production wafers. Based on these findings, an automated condition-based maintenance architecture was designed using an OR-logic decision gate. By triggering a preemptive table replacement at a quality-based critical warning threshold, this system converts routine time-based scheduling into a data-driven paradigm, maximizing both edge yield and equipment uptime. Furthermore, this proposed framework establishes a solid foundation for future extensions toward machine learning-based predictive maintenance. Full article

Inaccurate prediction of shrinkage in self-compacting concrete (SCC) may result in underestimated cracking risk, increased permeability, serviceability deterioration, and reduced long-term durability of concrete structures. Although conventional empirical shrinkage models are widely used in engineering practice, their accuracy is often limited when applied to SCC mixtures with high paste volume, mineral admixtures, manufactured sand, and high-range water-reducing admixtures. Recent machine-learning-based models provide an alternative approach, but single learning algorithms may show limited robustness for small and heterogeneous datasets. In addition, random sample-level data splitting may introduce information leakage when shrinkage measurements obtained at different curing ages from the same mixture are assigned to both training and testing sets. To address these issues, this study develops a stacking-based ensemble learning framework for SCC shrinkage prediction using mixture proportions and curing age as input variables. A multi-source database containing 61 mixture designs and 448 data samples was established from published experimental studies. To obtain a more realistic assessment of model generalization, a mixture-level validation strategy was adopted, in which all age-dependent samples from the same mixture were assigned exclusively to either the training set or the testing set. Under this strategy, 358 data samples were used for model training and 90 data samples were used for independent testing. Four base learners, including multilayer perceptron (MLP), support vector regression (SVR), decision tree (DT), and gradient boosting decision tree (GBDT), were constructed and integrated through different ensemble configurations. The Stacking-SVR model achieved the best overall performance on the independent testing set, with a mean absolute error (MAE) of 13.6 με and a mean absolute percentage error (MAPE) of 7.5%. Compared with GBDT, Stacking-GBDT, and DT models, the proposed Stacking-SVR model reduced the MAPE by approximately 10.7%, 11.8%, and 35.3%, respectively. Stability and applicability analyses further indicate that the proposed framework can provide reliable shrinkage predictions within the investigated mixture and curing-age ranges. However, because the model was developed from a compiled database and does not explicitly include environmental variables such as relative humidity and temperature, its use should be limited to parameter ranges represented in the database. Overall, the results demonstrate that stacking ensemble learning combined with mixture-level validation offers a leakage-controlled and engineering-oriented approach for SCC shrinkage prediction. Full article

To address the issues of slow operation and high variability in traditional electromagnetic trip devices, this paper proposes a magnetic trip device based on the principle of “permanent magnet holding, spring driving, and electric reset,” thereby reducing the circuit breaker tripping time. However, its high material and manufacturing costs have limited its widespread adoption. To address this issue, this paper employs a method combining orthogonal experiments with BP-NSGA-II. Using permanent magnet dimensions, coil wire gauge, moving component mass, and spring initial force as variables, the number of simulations is reduced through orthogonal experiments, and transient electromagnetic simulation is utilized to analyze the trip mechanism’s dynamic performance; a BP neural network surrogate model was constructed to replace finite element simulation, and the NSGA-II algorithm was employed to perform weightless Pareto optimization, with the volume of the permanent magnet and the amount of copper used in the coil as the cost optimization objectives. Under the constraint of a trip time ≤ 15 ms, the permanent magnet dimensions were reduced from 8 × 44 × 5 mm to 6.5 × 44 × 5 mm (a 18.7% reduction in volume), and the coil wire diameter was optimized from 0.18 mm (2000 turns) to 0.14 mm (1400 turns) (a 22.3% reduction in copper usage). Test results show that the optimized trip time was reduced from 16.0 ms to 14.1 ms, with the prototype measuring 14.56 ms in actual testing; the discrepancy between the simulation and experiment was less than 5%. This method provides a reference for the design of a permanent magnet trip device and holds significant engineering value. Full article

In spite of the intense research interest in the integration of Pressure Gain Combustion (PGC) systems with a turbomachinery module, limited studies have been conducted regarding the experimental investigation of the strong spatio-temporal perturbations of these unconventional machines’ outflow. This paper focuses on experimentally characterizing the perturbing exhaust flow of a Constant-Volume Combustor (CVC). Preceding numerical analysis offers a transition duct able to attenuate the CVC’s produced unsteadiness and connect this PGC with a turbomachinery module. In fact, the transition duct is manufactured, while a pair of windows are introduced allowing for high-frequency Particle Image Velocimetry (PIV) analysis. In addition, fast-response pressure sensors in the combustion chamber, upstream and downstream of the transition duct, are implemented. A parametric analysis of the rotational frequency of the inlet–outlet rotary valve pair is conducted. The perturbing outflow of this PGC is characterized and experimentally visualized for the first time. Moreover, the attenuation performance of the transition duct on the CVC’s produced unsteadiness is evaluated for different cycle frequencies. The transition duct is proved to be able to alleviate the spatial and time-dependent unsteadiness by CVC, offering crucial evidence and conclusions for the future industrial integration of the CVC with a High-Pressure Turbine stage. Full article

Additive manufacturing (AM) monitoring is fundamentally constrained by the severe scarcity of annotated data for layer-wise segmentation. This paper addresses this bottleneck by introducing a scalable, high-fidelity synthetic data generation pipeline built on the Slice-100K dataset, capable of producing large volumes of layer-wise semantic segmentation masks. Through analysis of this large-scale synthetic data, we identify a systemic foreground–background class imbalance (1:24 ratio) inherent to AM monitoring, which causes standard Dice loss formulations to diverge catastrophically into a phenomenon we formalize as the “Dice Crash.” To effectively leverage large amounts of unlabeled data, we adapt the Meta Pseudo-Labeling (MPL) framework for industrial segmentation. We evaluate MPL’s true marginal utility by integrating it with both a standard U-Net and a robust state-of-the-art nnU-Net architecture. Experimental outputs show that while MPL yields substantial performance gains (+15.2%) on weak baselines, integrating it with an optimally configured strong baseline consistently improves segmentation accuracy and suppresses false foreground detections, thereby mitigating confirmation bias. These findings demonstrate that semi-supervised learning via continuous bilevel optimization offers a practical and robust enhancement to data-scarce additive manufacturing monitoring. Because any hidden defects in the topmost layer will be permanently buried by subsequent extrusion, this foundational layer-wise segmentation step is the most critical primitive of the monitoring pipeline. Full article

This article focuses on the research and development of innovative polymer-concrete composites for the manufacture of precision machine tool frames and critical mechanical engineering components. The relevance of this work stems from the need to replace traditional cast iron and cement concrete with materials with superior damping properties and thermal stability. The polymer matrix used in this study was ED-20 epoxy-diane resin, modified with (FAM) furan resin and cured with polyethylenepolyamine (PEPA), which together ensured minimal linear shrinkage (less than 0.5–1%) during polymerization. The focus was on the effect of multimodal filler distribution, including quartz sand, gabbro, and basalt, as well as reinforcing additives such as silicon carbide and fiberglass, on the final performance characteristics of the material. Experimental studies determined the key physical and mechanical parameters of the obtained samples. The results showed that the optimized composition (Smp_001) exhibited compressive strength up to 92.3 MPa, significantly exceeding that of standard high-strength concrete. It was established that the use of silicon carbide and glass fiber promotes the formation of a dense heterogeneous microstructure characterized by extremely low porosity (1.2–2.5%) and record-low water absorption (less than 0.05%). These characteristics guarantee high dimensional stability of the frames during prolonged contact with process fluids and cutting fluids. The scanning electron microscopy (SEM) and (EDS) energy dispersive X-ray spectroscopy methods confirmed the dense packing and high degree of interaction of the polymer matrix with the crystalline phases of the filler. This condition of the interfacial boundaries guarantees stable stress transfer throughout the entire volume of the material, which minimizes the risk of local damage during operation. The study confirmed that the developed material has vibration damping properties 6–10 times more effective than gray cast iron, a critical factor in improving machining accuracy on modern metal-cutting machines. The scientific novelty of the study lies in its substantiation of the synergistic effect of the combined use of basalt fillers and silicon carbide to achieve the precision properties of a structural material. Its practical significance is confirmed by the possibility of producing large-scale parts by casting without the need for complex finishing, opening up new prospects for modernizing the machine tool industry. Full article

This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial contexts. The aim of this study is to develop and apply an integrated Lean–QMS–SPC analytical framework linking process performance improvement with sustainability-related outcomes. A case study was conducted in a high-volume manufacturing environment. The study combined process analysis, system-level assessment, and root cause identification to support targeted improvement actions. The results indicate that the implementation of Lean-oriented practices and supporting methods was associated with improved process stability, reduced variability, and decreased occurrence of nonconformities. These improvements translate into enhanced operational performance and reduced resource consumption associated with rework and defects. A scenario-based estimation model, based on observed defect reduction, is used to assess the potential impact on energy consumption and CO₂ emissions. The study contributes to the literature by operationally integrating SPC analysis, QMS assessment, root cause analysis, and Lean-oriented improvement activities within an industrial manufacturing context. The findings highlight that quality-driven process improvements may support operational efficiency while contributing to resource-efficiency performance. Full article

Wire Arc Additive Manufacturing (WAAM) has emerged as a cost-effective and high-deposition-rate technique for fabricating large-scale metallic components; however, the complex thermal history inherent to the process leads to heterogeneous microstructures that can significantly influence tribological performance. In this study, the dry sliding wear behavior of WAAM-fabricated austenitic stainless steel 308L (SS308L) was systematically investigated using a pin-on-disk configuration. The influence of applied normal load (1.5–15 N) and sliding speed (0.03–0.229 m/s) on wear volume, specific wear rate, coefficient of friction (COF), and tangential force was evaluated. Optical microstructural observations indicated features consistent with a ferritic–austenitic solidification structure, including regions resembling polygonal ferrite, Widmanstätten ferrite, and austenitic dendritic morphologies. Wear results showed that wear volume and cross-sectional area increased monotonically with increasing load, while the effect of sliding speed was comparatively less significant. The specific wear rate remained on the order of 10⁻⁴ mm³/N·m with minor variations across test conditions. The COF decreased with increasing load up to 10 N, followed by a speed-dependent response at higher loads. The findings demonstrate that load is the dominant factor governing wear behavior in WAAM SS308L, while microstructural heterogeneity may contribute to frictional stability and wear resistance. This study provides valuable insight into the structure–tribology relationship of WAAM stainless steels and supports the optimization of process parameters for wear-critical applications. Full article

Tomographic volumetric additive manufacturing (VAM) is an innovative 3D printing technology that polymerizes an entire volume of photopolymer resin simultaneously. VAM enables an increased printing speed and higher output compared with traditional stereolithography, layer-by-layer printing. We explore the operational implications of adopting VAM in an intelligent manufacturing context by considering process planning and production control issues exacerbated by the time bottlenecks introduced in downstream post-processing stages. Discrete Event Simulation (DES) was used to model production flow for two conceptual scenarios: a small-batch low-mix production environment and a high-mix variable-batch production environment. We simulated production, analyzed bottlenecks and tested intervention strategies that may be implemented: (1) increasing the availability of post-processing equipment, (2) modifying the number of available printers and (3) implementing improved workforce scheduling to reassign skilled operators during downtime of certain machines to reduce waiting time. VAM can speed up the creation of the primary part, but post-processing steps such as curing, washing and finishing the produced part might nullify those savings. Through the intervention methods we studied, the overall system utilization rate can be increased. VAM can achieve higher throughput rates in intelligent manufacturing settings only when it is incorporated into intelligent planning systems with high-speed post-processing. We provide some operational considerations in scaling up the VAM manufacturing capability, specifically focusing on planning challenges and gaps in adoption within manufacturing contexts. In this context, we find that coupling data-driven simulation methods with process planning algorithms may further improve workflow in smart manufacturing environments. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

For the Vacuum Induction Gas Atomization (VIGA) powder preparation process, a multi-scale coupled numerical simulation and experimental validation were employed to systematically reveal the influence mechanisms of process parameters on the primary atomization flow field structure, secondary atomization droplet breakup behavior, and powder particle size distribution Using Computational Fluid Dynamics (CFD) methods combined with the VOF (Volume of Fluid) multiphase flow model, the fragmentation morphology of the melt during primary atomization was simulated, capturing the dynamic characteristics of liquid film thinning and the reduction in initial droplet area. Concurrently, the DPM (Discrete Phase Model) coupled with the TAB (Taylor Analogy Breakup) model was applied to predict the droplet size distribution in secondary atomization. The results indicate that increasing atomization pressure (2.5–4.5 MPa) significantly enhances secondary fragmentation intensity, reducing the median particle size (D50) from 42.1 μm to 37.5 μm. Experimental studies on Ni-based superalloys, validated by laser particle size analysis, confirmed that higher atomization pressure improves gas velocity and gas–liquid energy conversion efficiency, optimizes turbulent flow structures, and refines powder particles. The study concludes that the multi-scale coupled model effectively predicts atomization dynamics. By optimizing atomization pressure, powder particle size can be significantly refined, providing a theoretical basis for process control of high-performance spherical powders used in additive manufacturing. Full article

Electrospun composites are desirable materials for drug delivery applications. Regarding microbial infections as a case study, the antibacterial effect is enhanced by physical attributes of electrospun meshes, namely, a high surface area-to-volume ratio and porosity, 3D topography, and customized surface functions. Beyond mimicking nanostructured fibers, the delivery of antibiotics from such composites enhances antibacterial efficacy, sustained release kinetics, and reduced wound infection while minimizing side effects. Concern over antibiotic resistance and the insufficient availability of pharmaceutical agents for effective infection treatment is increasing worldwide. A significant number of publications have reported the fabrication of electrospun composites to mitigate bacterial pathogenesis. However, from a structural and morphological perspective, the implications of electrospinning approaches for antibiotic delivery have not been reviewed. This proposal presents a comparative study of the different assemblies induced by electrospinning, enabling the development of platforms for administering antibacterial agents. The primary objective is to conduct a comprehensive examination of the considerations involved in electrospinning-based manufacturing of drug delivery systems and antibiotic loading, ensuring a thorough design process that accounts for composite processability, monitoring methods for kinetic behavior analysis and modeling, and biological considerations for pre-clinical in vitro characterization. Full article