Dense Component Research Articles

Effects of printing parameters on the properties of 316L stainless steel fabricated by fused filament fabrication

Purpose This study aims to explore the impact of printing parameters, specifically raster angle and layer thickness, on the microstructure and mechanical properties of green and sintered parts produced through filament-based fused filament fabrication (FFF) using a self-developed filament. The goal is to improve the quality and performance of the final sintered components. Design/methodology/approach A filament containing 92 Wt.% 316L stainless steel with polyoxymethylene (POM)-based binder was formulated and evaluated for flexibility through a buckling resistance test. Green parts were printed with varying raster angles (+45°/−45°, 0°/90°) and layer thicknesses (0.2 mm, 0.3 mm), followed by catalytic debinding and high-temperature sintering. Microstructure, dimensional accuracy and mechanical properties, including microhardness, tensile strength and elongation at break, were analyzed to identify optimal parameters. Findings A raster angle of (+45°/−45°) produced denser interlayer bonding and a more compact green part structure, whereas a thicker layer (0.3 mm) resulted in a looser structure with larger pores. The optimal combination of +45°/−45° raster angle and 0.2 mm layer thickness achieved the highest relative density (99.37%) and superior mechanical properties: microhardness (216.83 HV), tensile strength (467.59 MPa) and elongation at break (16.81%). Originality/value A 92 Wt.% 316L stainless-steel filament for FFF was independently developed and near dense steel components were successfully fabricated. This study provides new insight into developing a novel formula of filament and optimizing printing parameters for FFF technology.

Update on hazelnut allergy: Allergen characterization, epidemiology, food processing technique and detecting strategy.

Hazelnuts are popular among people due to their dense nutrient component. However, eating them may be quite dangerous for those who are allergic. To improve food safety, this research examines current developments in the characterization, processing, and detection of hazelnut allergens. The identification and molecular knowledge of certain proteins that cause allergic responses are necessary for the characterization of hazelnut allergens. Proteomics and genomics are two techniques that have helped to advance our knowledge of hazelnut allergens and facilitate the creation of more precise diagnostic instruments. One important factor to reduce but not to eliminate the exposure to hazelnut allergens is food processing. The extractability of hazelnut proteins with regard to food processing plays a crucial role in determining allergenicity. Innovative technologies have been created to lessen allergenicity in foods containing hazelnuts while preserving their flavor and quality. These technologies include thermal and nonthermal processing techniques. To further safeguard consumers with hazelnut allergies, innovations in ingredient labeling and cross-contamination avoidance techniques have been put into place. For the purpose of management, if foods contain hazelnut, they must label it. Technological developments in analytical methods, including mass spectrometry, polymerase chain reaction, and enzyme-linked immunosorbent assays, have made it possible to identify hazelnut allergens with high specificity and sensitivity in a range of dietary matrices. Moreover, the advancement of point-of-care testing instruments presents the possibility of prompt on site identification, hence enhancing food safety for people with hazelnut allergies. The multidisciplinary efforts of researchers, food technologists, and allergists to enhance the safety of products containing hazelnuts are highlighted in this study.

In Situ Neutron Diffraction Study of Strain Evolution and Load Partitioning During Elevated Temperature Tensile Test in HIP-Treated Electron Beam Powder Bed Fusion Manufactured Ti-6Al-4V

Abstract To manufacture almost fully dense components, electron beam powder bed fusion of Ti-6Al-4V is typically combined with post-heat treatment, such as hot isostatic pressing (HIP). The standard HIP treatment performed at 920°C and 100 MPa for 2 h results in coarsening of the microstructure and impacting the yield strength. A low-temperature HIP treatment performed at 800°C and 200 MPa for 2 h resulted in limited coarsening and comparable yield strength to as-built material. A coarser microstructure is detrimental to tensile properties. Tensile testing at different temperatures revealed that thermal activation of different slip systems could possibly affect the elongation behavior, demanding additional investigation. Performing in situ neutron time of flight diffraction during tensile testing provides data to analyze strain evolution and load partitioning in the crystal lattice, which includes the slip planes. A two-phase elastic–plastic self-consistent model has been used to analyze and compare the experimental results. The lattice strain evolution results indicated that the basal slip {0 0 0 2} was activated at 20°C while the pyramidal slip {1 0 $$\overline{1 }$$ 1 ¯ 1} was first activated during loading at 350°C. Load partitioning results showed that the β phase endures higher stresses than the α phase in the plastic regime.

HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation

Semantic segmentation of large-scale point clouds in 3D computer vision is a challenging problem. Existing feature extraction modules often emphasize learning local geometry while not giving adequate consideration to the integration of color information. This limitation prevents the network from thoroughly learning local features, thereby impacting segmentation accuracy. In this study, we propose three modules for robust feature extraction and aggregation, forming a novel point cloud segmentation network (HFA-Net) for large-scale point cloud semantic segmentation. First, we introduce the Hybrid Feature Extraction Component (HFEC) and the Hybrid Bilateral Enhancement Component (HBAC) to comprehensively extract and enhance the geometric, color, and semantic information of point clouds. Second, we incorporate the Ternary-Distance Attention Pooling (TDAP) module, which leverages trilateral distances to further refine the network’s focus on various features, enabling it to emphasize both locally important features and broader local neighborhoods. These modules are stacked into dense residual components to expand the network’s receptive field. Our experiments on several large-scale benchmark datasets, including Semantic3D, Toronto3D, S3DIS and LASDU demonstrate the effectiveness of HFA-Net when compared to state-of-the-art networks.

Experimental Investigation of Dispersant on Dynamics of Impact of Al2O3 Nanofluid Droplet.

Spray cooling, of which the essence is droplet impacting, is an efficient thermal management technique for dense electronic components in unmanned aerial vehicles (UAVs). Nanofluids are pointed as promising cooling dispersions. Since the nanofluids are unstable, a dispersant could be added to the fluid. However, the added dispersant may influence the droplet, thereby impacting behaviors. In this work, the effects of dispersant on the nanofluid droplet-impacting dynamics are studied experimentally. The base fluid is deionized water (DI water), and Al2O3 is the selected nanoparticle. Sodium dodecyl sulfate (SDS) is used as the dispersant. Five different concentrations of nanofluids are configured using a two-step method. Droplet impacting behaviors are observed by high-speed imaging techniques. The other effects, i.e., the nanofluid particle volume fraction and the Weber number on droplet impact dynamics, are also systematically investigated. The results illustrate that the surface tension of the Al2O3 nanofluid increases with increased nanofluid concentrations. The surface tension of Al2O3 nanofluid with SDS is lower than that of DI water. And the increase in droplet impact velocity increases the spreading morphology. Nanofluid droplets exhibit spreading and equilibrium process when SDS is added. Furthermore, as the concentration of the nanofluid increases, the spreading process is inhibited. Whereas without SDS, the droplets undergo spreading, receding, and equilibrium processes. Moreover, there is no appreciable change in the impacting process with concentration increase. The empirical models of maximum spreading factor should be established without SDS and with SDS, respectively. This study can provide theoretical basis and specific guidance for experimental characterization of UAVs' electronic devices based on the mechanism of nanofluid droplet impact on the wall.

Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma.

The assessment of lung adenocarcinoma significantly depends on the proportion of solid components in lung nodules. Traditional one-dimensional consolidation tumor ratio (1D CTR) based on ideal, uniformly dense solid components lacks precision. There is no consensus on the CT threshold for evaluating invasiveness using the threshold segmentation method. This study aimed to explore the effectiveness of the three-dimensional CTR (3D CTR) calculated by the artificial intelligence threshold segmentation method in predicting invasive stage T1 lung adenocarcinoma and to identify its optimal threshold and cut-off point. Data from 1,056 patients with 1,179 pulmonary nodules confirmed by postoperative pathology were collected retrospectively from two centers, Zhengzhou People's Hospital and Huadong Hospital of Fudan University. Patients were divided into non-invasive [atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA)] and invasive groups [invasive adenocarcinoma (IAC)]. Seven computed tomography (CT) threshold settings (-550 to 0 HU) were used to calculate the 3D CTR via the threshold segmentation method, and differences between the groups were analyzed. Receiver operating characteristic (ROC) curves were plotted to compare predictive performance for invasiveness of stage T1 lung adenocarcinoma, and the optimal threshold and corresponding cut-off value were determined. Subgroup analyses based on nodule size-T1a (≤10 mm), T1b (>10 to 20 mm), and T1c (>20 to 30 mm)-were also conducted. The CT threshold of -150 Housefield unit (HU) showed the highest predictive efficacy for invasiveness of stage T1 lung adenocarcinoma, with an area under the curve (AUC) of 0.901 [95% confidence interval (CI): 0.883-0.919], sensitivity of 86.878%, and specificity of 77.883%. The optimal cut-off point for 3D CTR was 2.75%. In subgroup analyses, -150 HU remained optimal, with predictive performance increasing with nodule size. For the T1a group, the AUC was 0.887 (95% CI: 0.885-0.919), cut-off value was 2.75%, sensitivity was 77.620%, and specificity was 85.714%. For the T1b group, values were 0.903 (95% CI: 0.875-0.931), cut-off value was 5.4%, sensitivity was 87.671%, and specificity was 80.296%. For the T1c group, values were 0.928 (95% CI: 0.893-0.963), cut-off value was 7.1%, sensitivity was 88.043%, and specificity was 81.176%. This study suggests that setting the CT threshold at -150 HU and using the AI-based threshold segmentation method to calculate the 3D CTR effectively distinguishes whether stage T1 lung adenocarcinoma is invasive, with an optimal cut-off point at 2.75%. Under this threshold, for varying nodule sizes, criteria are proposed: for nodules ≤10 mm with a 3D CTR <2.75%; >10 to 20 mm with a 3D CTR <5.4%; and >20 to 30 mm with a 3D CTR <7.1%, these partially solid nodules can be treated as non-IAC.

Constraining the Excitation of Molecular Gas in Two Quasar-starburst Systems at z ∼ 6

We present NOrthern Extended Millimeter Array observations of CO(8–7), (9–8), and (10–9) lines, as well as the underlying continuum for two far-infrared luminous quasars: SDSS J2054-0005 at z = 6.0389 and SDSS J0129-0035 at z = 5.7788. Both quasars were previously detected in CO (2–1) and (6–5) transitions, making them candidates for studying the CO spectral line energy distribution (SLED) of quasars at z ∼ 6. Utilizing the radiative transfer code CLOUDY, we fit the CO SLED with two heating mechanisms, including the photodissociation region (PDR) and X-ray-dominated region (XDR) for both objects. The CO SLEDs can be fitted by either a dense PDR component with an extremely strong far-ultraviolet radiation field (gas density n H ∼ 106 cm−3 and field strength G 0 ≳ 106) or a two-component model including a PDR and an XDR. However, the line ratios, including L TIR and previous [C ii]158 μm and [C i]369 μm measurements, argue against a very high PDR radiation field strength. Thus, the results prefer a PDR+XDR origin for the CO SLED. The excitation of the high-J CO lines in both objects is likely dominated by the central active galactic nucleus (AGN). We then check the CO (9–8)-to-(6–5) line luminosity ratio r 96 for all z ∼ 6 quasars with available CO SLEDs (seven in total) and find that there are no clear correlations between r 96 and both L FIR and the AGN UV luminosities. This further demonstrates the complexity of the CO excitation powered by both the AGN and nuclear star formation in these young quasar host galaxies.

Thermally Conductive Polydimethylsiloxane-Based Composite with Vertically Aligned Hexagonal Boron Nitride.

The considerable heat generated in electronic devices, resulting from their high-power consumption and dense component integration, underscores the importance of developing effective thermal interface materials. While composite materials are ideal for this application, the random distribution of filling materials leads to numerous interfaces, limiting improvements in thermal transfer capabilities. An effective method to improve the thermal conductivity of composites is the alignment of anisotropic fillers, such as hexagonal boron nitride (BN). In this study, the repeat blade coating method was employed to horizontally align BN within a polydimethylsiloxane (PDMS) matrix, followed by flipping and cutting to prepare BN/PDMS composites with vertically aligned BN (V-BP). The V-BP composite with 30 wt.% BN exhibited an enhanced out-of-plane thermal conductivity of up to 1.24 W/mK. Compared to the PDMS, the V-BP composite exhibited outstanding heat dissipation capacities. In addition, its low density and exceptional electrical insulation properties showcase its potential for being used in electronic devices. The impact of coating velocity on the performance of the composites was further studied through computational fluid dynamics simulation. The results showed that increasing the coating velocity enhanced the out-of-plane thermal conductivity of the V-BP composite by approximately 40% compared to those prepared at slower coating velocities. This study provides a promising approach for producing thermal interface materials on a large scale to effectively dissipate the accumulated heat in densely integrated electronic devices.

Short-Range Correlations and Urca Process in Neutron Stars.

Recent measurements of high-momentum correlated neutron-proton pairs at JLab suggest that the dense nucleonic component of the compact stars contains a fraction of high-momentum neutron-proton pairs that is not accounted for in the familiar Fermi-liquid theory of the neutron-proton fluid mixture. We compute the rate of the Urca process in compact stars taking into account the non-Fermi liquid contributions to the proton's spectral widths induced by short-range correlations. The Urca rate differs strongly from the Fermi-liquid prediction at low temperatures; in particular, the high threshold on the proton fraction precluding the Urca process in neutron stars is replaced by a smooth increase with the proton fraction. This observation may have a profound impact on the theories of cooling of compact stars.

Material extrusion additive manufacturing of bi-metal structures: A numerical and experimental study of interfacial microstructure

Bi-metal structures attract significant attention in research and industry due to their great performance and superior functionality. However, the formation of interface has not been comprehensively investigated due to the lack of methodologies to unveil the bonding mechanism between dissimilar materials. In this study, we developed a discrete element-based modeling and simulation to predict and understand the micro-scale bonding of stainless steel and nickel superalloys built by a printing, debinding, and sintering process. Two governing diffusion mechanisms were implemented to reveal the microstructural evolution from loose particles to a dense bi-metal component. The densification and grain growth of the interface were simulated and validated by experimental observations through scanning electron microscopy and electron backscatter diffraction analysis. Our simulation can accurately predict the interfacial microstructure, demonstrating a successful adoption of the modeling and simulation methodology to understand the interfacial formation of dissimilar materials shaped by AM and bonded by solid-state sintering.

Influence of Defects and Microstructure on the Thermal Expansion Behavior and the Mechanical Properties of Additively Manufactured Fe-36Ni.

Laser-based powder bed fusion of metals (PBF-LB/M) is a widely used additive manufacturing process characterized by a high degree of design freedom. As a result, near fully dense complex components can be produced in near-net shape by PBF-LB/M. Recently, the PBF-LB/M process was found to be a promising candidate to overcome challenges related to conventional machining of the Fe64Ni36 Invar alloy being well known for a low coefficient of thermal expansion (CTE). In this context, a correlation between process-induced porosity and the CTE was presumed in several studies. Therefore, the present study investigates whether the unique thermal properties of the PBF-LB/M-processed Fe64Ni36 Invar alloy can be tailored by the selective integration of defects. For this purpose, a full-factorial experimental design, representing by far the largest processing window in the literature, was considered, correlating the thermal expansion properties with porosity and hardness. Furthermore, the microstructure and mechanical properties were investigated by scanning electron microscopy and quasi-static tensile tests. Results by means of statistical analysis reveal that a systematic correlation between porosity and CTE properties could not be determined. However, by using specific process parameter combinations, the microstructure changed from a fine-grained fan-like structure to a coarse columnar structure.

Testing for sparse idiosyncratic components in factor-augmented regression models

We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative model augmented with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoretical validity. In simulation experiments, our procedure exhibits high power against sparse alternatives and low power against dense deviations from the null. Moreover, we apply our test to various datasets in macroeconomics and finance and often reject the null. This suggests the presence of sparsity — on top of a dense component — in commonly studied economic applications. The R package ‘FAS’ implements our approach.

Mechanism of thermal gravimetric difference between simulated and experimental of coal group components

Understanding the molecular mechanism underlying coal's thermal gravimetric behavior is crucial for advancing efficient and clean coal utilization technology, a task that remains challenging to address experimentally. ReaxFF molecular dynamic simulation has been widely applied across various systems and materials, particularly excelling in simulating combustion and pyrolysis processes with complex coal models. It is necessary to establish more connections between simulation and experiment, and to analyze and discuss the differences between them. This will lay the foundation for the reliability of simulation results in revealing experimental phenomena. In this study, coal was divided into four components using a full component classification method: the dense medium component, loose medium component, heavy component, and light component. The connections of the simulated and experimental thermal gravimetric behaviors of the coal group component skeletons were conducted, and founding the inconsistencies in the later stages of the curves. To understand these discrepancies, the test environments were analyzed, identifying the behavior of tar free radicals generated during pyrolysis as a key factor influencing the thermo-gravimetric curve. To mitigate the influence of tar free radicals and reduce the observed differences, a correction coefficient was introduced into the calculation formula for the yield of non-volatile products, achieving an improved alignment between the simulated and experimental thermo-gravimetric curves. This study significantly advances the application of ReaxFF molecular dynamics simulation in elucidating the mechanisms of coal pyrolysis.

Spark plasma sintering of TiC with TiAly as sintering aid: Mechanisms and microstructures

TiC features an interesting combination of mechanical properties, high-temperature resistance, and lightness, making it an excellent candidate for several applications in harsh environments. However, its sintering to obtain bulk components is extremely challenging. Herein, we show that titanium aluminide is a promising sintering aid for TiC (5, 10, and 20 vol% were investigated). The aluminide allows the formation of a nearly fully dense component at 1350 °C by spark plasma sintering under 80 MPa. The aluminide forms a grain boundary secondary phase that promotes the Ti diffusion: Ti from TiC can be dissolved within the TiAly at the neck center and precipitate at the neck surface, while C can easily diffuse through the TiC lattice. Higher temperatures cause the extrusion of the aluminide out of the SPS die and its reaction with oxygen impurities. The final microstructure is constituted by nearly pure TiC with isolated alumina pockets at the triple points.

To the ring-shaped nucleolus seen by microscopy using human lymphocytes of blood donors and chronic lymphocytic leukemia patients.

The present study was undertaken to provide more information on the peripheral RNA containing ring of ringshaped nucleoli (RSNo). Human lymphocytes of blood donors and patients suffering from B chronic lymphocytic leukemia mostly characterized by RSNo represented very convenient cell models for such study. According to the light microscopy the peripheral RNA ring possessed several highly condensed foci. Such regions represented accumulated dense RNA fibrillar components (DFCs) seen by the electron microscopy. In contrary, the incidence of dense granular RNA-containing components (GCs) in surrounding portions of the RNA ring was small. Thus, the structural and morphological organization of the peripheral RNA ring of RSNo apparently reflects sites of micro-segregated foci of DFCs and a small incidence of GCs. That structural organization of the peripheral RNA ring of RSNo appeared to be a prerequisite for further regressive nucleolar changes resulting in the development of micronucleoli in terminal lymphocytes.

Structural analysis of selective laser melted copper-tin alloy

Additively manufactured complex geometries from copper alloys with high thermal and mechanical properties have drawn the attention of researchers. The present contribution explores the additive manufacturing (AM) of copper-based alloys from powder particles intended for heat sink and heat exchange applications. Selective laser melting (SLM) parameters featuring low laser beam power (160 W), moderate scanning speed (320 mm/s), and high energy density (200 J/mm³) were employed to fabricate dense components from CuSn10 particles. The present work deal with structural analysis and precision investigation of microfabrication, particularly in Struts, Tubes, and Fins. Mechanical properties (compression and hardness) for Strut structure, differential pressure evaluations for Tube structure, and analyses of thermal and electrical conductivities for Fin structure were investigated. The results showed an improvement in strength compared to those of pure copper, facilitating ease of AM. The obtained results affirm the feasibility of AM, demonstrating the successful creation of complex and combined solid-porous structures using SLM process from Cu alloys. A comprehensive structural investigation and characterization of the Cu–Sn alloy is presented here, aiming to establish a standardized approach for analysing Cu alloys. The results indicate that small-scaled structures fabricated via CuSn10 alloy exhibits a thermal conductivity of 34.3 W·m⁻¹·K⁻¹, an electrical conductivity of 4.72×10⁶ S/m, a hardness of 119 HV-50, a uniform surface roughness of 6 µm, and can withstand a force loading of 1 kN.

Super-Resolution Estimation of UWB Channels Including the Dense Component—An SBL-Inspired Approach

Super-Resolution Estimation of UWB Channels Including the Dense Component—An SBL-Inspired Approach

FUSION3D: Multimodal data fusion for 3D shape reconstruction: A soft-sensing approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g., dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing three-dimensional (3D) shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Microstructural control of LPBF Inconel 718 through post processing of intentionally placed AM discontinuity distributions

A major untapped potential of laser powder bed fusion (LPBF) is the ability to additively manufacture (AM) parts with site-specific properties. However, robust methods of improving performance and manufacturing efficiency via spatial variations in microstructure are underexplored.This work investigates a method of creating several distinctive multi-modal microstructures in the nickel superalloy, Inconel 718, by performing a Hot Isostatic Pressing (HIP) procedure on characteristic LPBF discontinuities such as keyhole and lack of fusion (LOF) porosity, as well as large powder filled voids. Observed using electron backscattered diffraction, this approach leads to the formation of site-specific variations in microstructure and extreme bi-modal microstructures. Processing AM parts via this method facilitates the production of fully dense, equiaxed, and strain free material culminating in a substantial time saving per layer. Further optimisation is required in order to achieve an optimum volume fraction and distribution of γ″ precipitates in each heat treated microstructure.

Additive manufacturing of a low modulus biomedical Ti–Nb–Ta–Zr alloy by directed energy deposition

While β titanium alloys have garnered extensive attention as a new generation of biomedical materials designed to mitigate stress shielding due to their low modulus, the realm of additive manufacturing for these alloys is still in its nascent stages. This study focuses on the additive manufacturing of Ti–35Nb–5Ta–7Zr alloy powder via directed energy deposition (DED). The primary objectives were assessing the feasibility of employing DED for this alloy powder and identifying processing parameters to achieve nearly dense components. Systematic exploration of the effect of various processing parameters was performed, and the resultant impact on the densification of the produced specimens was studied. Comprehensive analysis of the microstructure, mechanical properties, electrochemical behavior, and cell studies of fully dense sample coupons were performed. These fully dense samples were found to exclusively comprise the β phase of titanium, resulting in a reduced modulus of elasticity (approximately 44–47 GPa) resulting in high yield strength to elastic modulus ratio. Microstructural examinations revealed the presence of both columnar and equiaxed dendrites, with grains transitioning from columnar to equiaxed (known as CET). Electrochemical testing of the coupons indicated exceptional corrosion resistance in the additively manufactured TNZT alloy. Pre-osteoblasts cultured on the alloys showed good attachment, viability, and growth to confirm cytocompatibility. These findings unveiled the attainment of high strength, favorable ductility, a low elastic modulus, excellent corrosion resistance, and cytocompatibility in dense samples created via DED of Ti–35Nb–5Ta–7Zr. These outcomes hold immense significance for the production of patient-specific medical implants manufactured from β-Ti alloys.