Dense Layer Research Articles

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

ContextThe invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity.ObjectivesTo provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems.MethodsWe calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR).ResultsThe metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass.ConclusionsOur results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.

Effect of traps and conductive pathways on electron emission from copper broad-area composite emitters

This study investigates electron emission from copper broad-area emitters (CBAEs) and copper broad-area composite emitters (CBACEs) based on the principles of trapping and conductive pathways. Emission current measurements were conducted on two CBACEs, which consisted of copper coated with a 300–400 μm epoxy resin and subjected to high voltages up to 15 kV. The research specifically examines the ‘switch-on’ and collapse phenomena occurring within the epoxy layer. Field emission microscopy (FEM) was utilized in a high-vacuum environment ( 10−6 mbar) to observe these effects. A comprehensive model is developed to explain the formation of conductive pathways within the epoxy layer, allowing electrons transfer from traps to the surface. This model treats the composite emitter as a trap-rich capacitor. The study also clarifies the effects of trap density and epoxy layer thickness on the collapse process. To gain a deeper understanding of the model, changes in the I-V curve were examined. Simulations, scanning electron microscopy-energy dispersive x-ray spectroscopy (SEM-EDX) images, and Fourier transform infrared spectroscopy (FTIR) analysis were employed to understand the collapse mechanism of the epoxy collapse. Additionally, Nyquist and Cole–Cole plots were analyzed across frequencies ranging from 1 to 106 Hz before and after applying a high electric field on the samples, revealing changes in the capacitive component and the role of diodes in the formation of conductive channels.

Modeling the Electrical Stabilities of Organic Light‐Emitting Diodes

The ratio (R) of the molecular density of emissive layer to the carrier density in exciton formation zone has been provided to weigh the electrical stability of organic light‐emitting diode and simulated via the general mode of carrier device lifetimes. With current density increasing from 1 to 500 mA cm−2, the molecular density of emissive layer enabling the largest R, that is, the best electrical stability of device, gradually increases from 7.5 × 1019 to 1.5 × 1021 cm−3. At a given current density, R increases with the average bandgap of emissive layer increasing. There is a power law dependence of R on current density. For host–guest emissive layer, the electrical stability of device decreases with guest concentration increasing, mostly because the average bandgap of the host–guest emissive layer decreases. The current research provides some deep insights into developing the emissive layers of organic light‐emitting diodes toward high luminance applications.

Microstructure and wear characteristics of laser-clad Ni-based self-lubricating coating incorporating MoS2/Ag for use in high temperature

Nickel-based self-lubricating cladding containing Ag and MoS2 was prepared on Ti6Al4V alloy using laser cladding. The microstructure, microhardness and wear behaviour were measured and analysed. The coating was mainly composed of Ti2Ni and TiNi as the matrix, and TiC and TiB2 as the reinforcements. A new phase of Ti2SC was synthesized in situ in the coating, whereas Mo was consolidated in the matrix. The microhardness of the coating is greatly increased by in-situ generation of reinforcement phase and solution strengthening, with an average hardness of 823.6HV0.2, which is about 2–3 times that of the substrate. When the temperature increased to 600 °C, the coefficient of friction and wear rate of the coating decreased. The friction coefficient and the wear rate of the coating were greatly reduced at 800 °C. The wear rate of the coating decreased to 4.85 × 10−6 mm3, which decreased by nearly an order of magnitude compared with the coating's wear rate at room temperature. The anti-friction effect of the lubricating phase not only counteracted the effect of the decreased hardness on the wear rate, but also played a more obvious anti-friction effect. In the high-temperature stage, MoO3 and silver molybdate compounds produced by the tribological chemical reaction of wear surfaces could significantly improve the density of the enamel layer on the wear surface.

Post-mortem analysis of the deposit layers on the lower divertor after the 2023 high particle fluence campaign of WEST

We analysed deposits collected on the lower divertor of WEST after the first high fluence campaign performed in 2023. Deposits were collected on the high field side (thick deposition area) of 2 ITER-grade plasma facing units (PFUs), located on different toroidal positions. Using focused ion beam cross sectioning, the deposits were found to be very thick (12–55 µm). A significant difference was observed in the toroidal direction with thicker deposits for the PFU located at the maximal heat load in the inner side, showing a deposition pattern due to the toroidal magnetic field modulation. Deposits present a complex layer-by-layer structure with dense layers, some melted parts and porosities within the layers. The deposition is mainly composed of tungsten with oxygen, boron, carbon and traces of nitrogen whose composition vary along the radial direction. We identified W-rich deposits in the high plasma flux area near the inner strike point and deposits rich in O, B, C and N in the low plasma flux area further away from this strike point. W dense layers of about 5 up to 40 µm thick in the thick deposit area near the strike point were attributed to the high fluence campaign. Pure boron layers resulting from wall conditioning and processed by plasma wall interactions were also observed.

A novel insight into the enhanced electrochemical performance of Mg-air battery in mixed aqueous and organic electrolytes

A new strategy of utilizing mixtures of ethanol and complexing agents on Mg-air battery is investigated systematically. The presence of ethanol promotes formation of a dense inner layer with a high portion of oxide in the discharge film, leading to significantly enhanced utilization efficiency. The highest utilization efficiency of Mg anode has been increased from 53.6 % to 85.7 % and the discharge potential is decreased 300 mV, when discharges in mixed electrolyte. The adsorption/deposition of S-like species on metal surface is evidenced in ethanol and 5-sulfosalicylate acid containing solution, resulting in inhibition of the hydroxide-rich outer layer growth.

Achieving metallurgical bonding in steel faceplate/aluminum foam sandwich via hot pressing and foaming processes: interfacial microstructure evolution and tensile behavior

This study introduces an innovative approach to fabricating aluminum foam sandwich with aluminized steel faceplates through metallurgical bonding. The process involves the use of foamable precursor, prepared via melt stirring, and subsequent hot pressing and foaming, offering a cost-effective and industrially feasible method for producing lightweight structural materials and connection of steel/aluminum dissimilar metals. This study focuses on exploring the changes in the microstructure of the bonding interface before and after foaming, and revealing the impact of these changes on the tensile results. Foaming experiment shows that foamable sandwiches have superior foaming ability, and the core layer density after foaming is between 0.283 and 0.591 g/cm ³. Microstructural characterization results demonstrate that, during the hot pressing process, fine equiaxed grains are observed on the iron side of the interface, indicating dynamic recrystallization occurred. The formation of a small amount of η-Al5Fe2 at the interface is a primary factor causing the deflection of the fracture path. Subsequently, during the foaming process, intermetallic compounds (IMCs) θ-Al13Fe4, τ5-Al7Fe2Si, and β-Al4.5FeSi formed sequentially, mainly determined by the diffusion reaction of silicon elements. The formation of these IMCs led to an increase in microhardness at the interface and a decrease in shear strength. Digital image correlation was utilized to examine strain distribution under tensile loading. The result indicates that the damage accumulation is characterized by the formation and expansion of strain bands, with failure manifested as the interconnection of these strain bands.

Improved Charge Recombination Efficiency in Organic Light‐Emitting Transistors via Luminescent Radicals

AbstractLuminescent radicals are attracting attention as emitters in electroluminescent devices thanks to the exploitation of doublet excitons. Recent studies reveal that exciton formation in radical organic light‐emitting diodes (OLEDs) primarily occurs through a charge trapping mechanism. Although typically detrimental for OLEDs, this might be a key process to elucidate light emission in organic light‐emitting transistors (OLETs). Here, a unipolar n‐type architecture suitable for the implementation of radical emitters is introduced, designed based on computational calculations. The operation of the as‐realized devices incorporating the newly synthesized [2,6‐dichloro‐4‐(2,6‐dimethoxyphenyl)phenyl](3,5‐dichloro‐4‐pyridyl) (2,4,6‐trichlorophenyl)methyl radical is investigated via transient electroluminescence measurements to demonstrate the occurrence of long‐living emission ascribed to the charge trapping mechanisms. Moreover, a comprehensive understanding of the processes governing radical‐OLET is obtained by recording complete 2D maps of both optical and electrical response of the device as a function of applied voltages. Notably, the trapping of electrons by radical moieties is demonstrated to generate a negative charge density in the emissive layer that facilitates holes to be injected: increasing the balance of opposite charge carriers, a tenfold enhancement of the external quantum efficiency (EQE) at the proper source‐drain and source‐gate voltage conditions is reported to reach a maximum EQE value of 0.2%.

Multilayer Adsorption Characteristics of CO2 in Organic Nanopores with Different Pore Sizes: Molecular Simulation and Ono-Kondo Lattice Model.

Shale contains numerous organic micropores with significant potential for CO2 storage. To precisely evaluate the CO2 storage potential of shale reservoirs, it is essential to accurately quantify the adsorption of CO2 within these pores. This study used Grand Canonical Monte Carlo (GCMC) molecular simulations to analyze the CO2 adsorption behavior in organic micropores of varying sizes. The study clarified the number and width of the CO2 adsorption layers in micropores of various sizes and proposed a method for segmenting the multilayer adsorption structure. Additionally, the classic Ono-Kondo lattice (OK) model was extended to characterize pore-filling adsorption, incorporating solid-gas and gas-gas interactions. Accurate characterization of CO2 multilayer adsorption and precise calculation of CO2 absolute adsorption in micropores were achieved. Results indicate that CO2 exhibits pore-filling adsorption behavior in organic micropores, forming a multilayer adsorption structure governed by the pore size. Following symmetry principles, the adsorption layer structure in organic micropores can be simplified to a maximum of three layers. When only one adsorption layer forms, its width equals the gas-accessible pore size. For two or more layers, the width of the original layer stabilizes as additional layers form. The stable adsorption layer widths, from nearest to farthest from the pore wall, are 0.33, 0.45, and 0.39 nm. The improved OK model accurately describes CO2 excess and absolute adsorption isotherms across different pore sizes and calculates the CO2 density in each adsorption layer, showing high consistency with GCMC simulation results. These findings highlight the importance of understanding the CO2 multilayer adsorption structure for accurately estimating CO2 adsorption in organic micropores.

Experimental evidence and calculation of the penetration depth of acyl chloride reagent and molecular expansion in a dense layer of PVA coated on a paperboard after chromatogeny grafting

Experimental evidence and calculation of the penetration depth of acyl chloride reagent and molecular expansion in a dense layer of PVA coated on a paperboard after chromatogeny grafting

Factors Associated with Retinal Microvasculature Dropout Induced by Elevation of Intraocular Pressure in Primary Open-Angle Glaucoma.

To investigate the risk factors related to decrease in vessel density (VD) observed in primary open-angle glaucoma (POAG), due to acute increase in intraocular pressure (IOP) by an ophthalmodynamometer (OPD). This cross-sectional study involved 42 eyes of participants (22 Controls and 20 POAG patients) that underwent optical coherence tomography angiography (OCT-A) to assess VD in the peripapillary region in three examination sets: primary gaze position (1), 25-degree adduction (2) and 25-degree adduction with OPD compression (3). Individual relationships between IOP levels and changes in the superficial complex VD were evaluated after image processing and exclusion of large retinal vessels. Multivariable regression analysis was used to verify factors associated with differences in VD induced by IOP elevation. A significant increase in IOP was induced by OPD compression during adduction (mean ± SD, Control: + 13.8 ± 2.8; POAG: + 13.4 ± 2.1mmHg). Only during IOP elevation (set 3), a significant VD decrease was observed both in POAG eyes (p = 0.008) and controls (p = 0.022). Baseline IOP (p = 0.022), maximum IOP (p = 0.003), and scleral rigidity (p = 0.029) were significantly associated with VD decreases in eyes with POAG. No changes were observed in VD during adduction gaze exclusively. Acute IOP elevation induced with OPD, but not adduction gaze, decreased peripapillary VD measured with OCT-A imaging. IOP levels and scleral rigidity significantly affected VD reduction in POAG patients. Thus, high scleral rigidity may decrease the ability of the globe to dampen the well-known effects of IOP fluctuation on glaucoma onset and progression. What is known Decrease vascular density in the peripapillary retina was associated with POAG, but factors related to the vascular response to elevated IOP are unexplored. What is new OCT-A quantification shows decreases in vascular density of the superficial layers of the peripapillary retina during an acute elevation in IOP. High IOP levels and scleral rigidity significantly affected vascular density reduction in POAG patients.

Effect of grain size on high-temperature corrosion performance of laser cladding inconel 625 coating

As the calorific value of waste-to-energy incineration continues to rise, the corrosion damage to boilers becomes increasingly severe. The impact of the grain size on the high-temperature corrosion resistance of Inconel 625 coating was investigated. The results indicated that the corrosion performance of the coating in 1:1 NaCl–KCl salts at 700 °C for 120 h was highly dependent on the grain size of the coating. The coating fabricated with a laser power of 1800 W and an overlapping distance of 1.5 mm had an average grain size of 13.7 μm and highest hardness, exhibiting the best corrosion performance, with a corrosion weight loss of 1.4 mg/cm2. Coatings processed under different conditions all formed a three-layered structure, with a NiO outermost layer, a Cr2O3 intermediate layer, and an innermost Cr-depletion zone. Coatings with finer grains formed a continuous and dense protective oxide layer of NiO and Cr2O3 due to the rapid diffusion of alloy elements, effectively preventing the invasion of corrosive media and protecting the substrate from corrosion.

Impact of Vertical Vibration on Group Piles During Earthquake Loading: Experimental Findings

This study presents novel research on the impact of vertical vibration on the dynamic response of pile groups embedded in stratified soil under seismic loading conditions. An experimental setup was employed wherein the piled machine foundation was subjected to vertical vibrations at three operating frequencies (10, 20, and 30 Hz) and subsequently exposed to four levels of seismic acceleration (0.1 g, 0.34 g, 0.77 g, and 0.82 g). Piles with a length-to-diameter ratio of 25 were embedded in a stratified soil profile, with an upper loose layer (30% relative density) and a lower dense layer (80% relative density) acting as end-bearing. Measurements and analyses of horizontal and vertical accelerations, and amplification factors were conducted using the Fast Fourier Transform (FFT), spectral acceleration (Sa), and variation of acceleration with depth. The results demonstrated a significant reduction in horizontal acceleration, with a peak ground acceleration (PGA) reduction of up to 64% in average, particularly at higher frequencies such as 30 Hz. The mitigation efficiency at 30Hz improved with increasing PGA, showing reductions of 42, 68, 75, and 63% for seismic accelerations of 0.1 g, 0.34 g, 0.77 g, and 0.82 g, respectively. The analysis further revealed harmonic resonance and higher mode effects at lower frequencies, with nonlinear soil behavior affecting the resonance and amplification patterns. Additionally, the results demonstrate that the far-field accelerations exceeded the near-field accelerations within the pile group, particularly in the surface layer. The results indicated that the initial vibration amplitudes exceeded the safe operating limits outlined in ACI 351.3R-18 under seismic loading, particularly at higher seismic acceleration levels and lower frequencies. Additional modified charts were presented to account for these conditions. The results presented promising evidence for using vertical vibrations as an earthquake-mitigation strategy. However, avoiding operating at frequencies less than 10 Hz is recommended because of the potential resonance and interaction with horizontal accelerations during earthquakes. Doi: 10.28991/CEJ-SP2024-010-010 Full Text: PDF

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

Cyclosulfate additive inhibits Li dendrite disorder growth to achieve stable cycles of Li metal battery

ABSTRACT The high reactivity of lithium metal leads to the uncontrolled growth of lithium dendrites during the charging and discharging process of lithium metal batteries, which results in the poor cycle performance of batteries. In this paper, we innovatively utilized 1, 3-propanediol cyclic sulfate (PCS) as an additive to successfully construct a smooth and dense SEI layer containing sulfur element on the lithium metal anode surface. Under the action of PCS, the cells show excellent performance. After 200 cycles, cells with baseline electrolyte (BE) + 1 wt.% PCS still has a high capacity of of 148mAh g−1 and 81.38% capacity retention compared with cells with BE. In-situ optical microscopy is pioneeringly applied to showcase the generation and growth of lithium dendrites which showed that PCS participated in the formation of the SEI layer, indicating that this optimized SEI layer has better conductivity of Li+ and chemical stability, thus inhibiting malignant lithium deposition and electrolyte decomposition above the lithium metal anode.

A comparative study of early stage Alzheimer’s disease classification using various transfer learning CNN frameworks

ABSTRACT The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer’s disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model’s effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer’s data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer’s disease quickly and accurately.

Classification Of Plants By Their Fruits And Leaves Using Convolutional Neural Networks

The population growth of the world is exponential, this makes it imperative that we have an increase in food production. In this light, farmers, industries and researchers are struggling with identifying and classifying food plants. Over the years, there have been challenges that come with identifying fruits manually. It is time-consuming, labour intensive and requires experts to identify fruits because of the similarity in fruit’s leaves (citrus family), shapes, sizes and colour. A computerized detection technique is needed for the classification of fruits. Existing solutions to fruits classifications are majorly based on fruit or leave used as input. A new model using Convolutional Neural Network (CNN) is proposed for fruits classification. A dataset of 5 classes of fruits and fresh dry leaves plants (Mango, African almond, Guava, Avocado and Cashew) comprising of 1000 images each. The proposed model hyperparameters were: Conv2D layer, activation layer, dense layer, a learning and dropout rates of 0.001 and 0.5 respectively were used for the experiment. Various performances for accuracies of 91%, 97%, 78% and 97% were obtained for proposed model on local dataset, proposed model on benchmark dataset, benchmark model on local dataset and benchmark model on benchmark dataset. The proposed model is robust on both local and benchmark datasets and can be used for effective classification of plants

Changes in choroidal thickness and blood flow in response to form deprivation-induced myopia and repeated low-level red-light therapy in Guinea pigs.

To evaluate ocular refractive development, choroidal thickness (ChT) and changes in choroidal blood flow in form-deprived myopia (FDM) Guinea pigs treated with repeated low-level red-light (RLRL) therapy. Twenty-eight 3-week-old male tricolour Guinea pigs were randomised into three groups: normal controls (NC, n = 10), form-deprived (FD, n = 10) and red light treated with form-deprivation (RLFD, n = 8). Interocular refraction and axial length (AL) changes were monitored. Optical coherence tomography angiography (OCTA) measured choroidal thickness, vessel area density, vessel skeleton density and blood flow signal intensity (flux) in the choriocapillaris and medium-large vessel layers. The experimental intervention lasted 3 weeks. At week 3, the FD group had higher myopia and longer axial length than the NC group (all p < 0.001). The RLFD group had higher hyperopia and shorter axial length than the FD group (all p < 0.001). At week 1, the NC group had a thicker choroidal thickness than the FD group (p < 0.05). At weeks 2 and 3, the RLFD group had a thicker choroidal thickness than the FD group (p = 0.002, p < 0.001, respectively). Additionally, the NC group had higher vessel area density, vessel skeleton density and flux in the choriocapillaris layer than the FD group at the three follow-up time points (all p < 0.05). At week 3, the vessel skeleton density and flux were higher in the RLFD group than in the FD group (all p < 0.05). Correlation analysis results showed that weekly changes in refraction and choroidal thickness were negatively correlated with changes in axial length (all p < 0.05). Choroidal thickness changes were positively correlated with alterations in the vessel area density, vessel skeleton density and flux in the choriocapillaris layer, as well as vessel skeleton density and flux changes in the medium-large vessel layers (all p < 0.05). Repeated low-level red-light (RLRL) therapy retards FDM progression in Guinea pigs, potentially through increased choroidal blood flow in the choriocapillaris layer.

The comparative investigation of oxidation behavior and corrosion mechanisms of SiC matrix ceramics under different atmospheres

The oxidation experiments of SiC matrix ceramics were conducted at 1200 °C for 10 h under three different atmospheres: dry air, water vapor, and a mixture of H2O/air. The oxidation behaviors and corrosion mechanisms were elucidated through phase and valence bond analysis, morphology examination, as well as elemental distribution analysis of the oxidized surface and cross section. Comparing the oxidation conditions among the three atmospheres, it was found that dry air exhibited the least degree of oxidation, while water vapor showed a more pronounced increase in oxidation. Notably, the most severe oxidative corrosion occurred in the presence of H2O/air mixed gas conditions. The formation of a dense oxide layer, consisting of precipitated Y2Si2O7 crystals which result from YAG boundary phase on SiC matrix ceramics under dry air conditions, can effectively provide protection against further oxidation. However, introduction of water leads to additional corrosion on this oxide layer, particularly when combined with air.

Accelerating Laser Powder Bed Fusion: The Influence of Roller-Spreading Speed on Powder Spreading Performance

The powder spreading process is a fundamental element within the laser powder bed fusion (PBF-LP) framework given its pivotal role in configuring the powder bed. This configuration significantly influences subsequent processing steps and ultimately determines the quality of the final manufactured part. This research paper presents a comprehensive analysis of the impacts of varying spreading speeds, which are enabled by different roller configurations, on powder distribution in PBF-LP. By utilizing extensive Discrete Element Method (DEM) modelling, we systematically examine how spreading speed affects vital parameters within the spreading process, including packing density, mass fraction, and actual layer thickness. Our exploration of various roller configurations has revealed that increasing spreading speed generally decreases packing density and layer thickness for non-rotating, counter-rotating, and forward-rotating rollers with low clockwise rotational speeds (sub-rolling) due to powder dragging. However, a forward-rotating roller with a high clockwise rotational speed (super-rolling) balances momentum transfer, enhancing packing density and layer thickness while increasing surface roughness. This configuration significantly improves the uniformity and density of the powder bed, providing a technique to accelerate the spreading process while maintaining and not reducing packing density. Furthermore, this configuration offers crucial insights into optimizing additive manufacturing processes by considering the complex relationships between spreading speed, roller configuration, and powder spreading quality.