Fusion Process Research Articles

Breaking through clouds: A hierarchical fusion network empowered by dual-domain cross-modality interactive attention for cloud-free image reconstruction

Cloud obscuration undermines the availability of optical images for continuous monitoring in earth observation. Fusing features from synthetic aperture radar (SAR) has been recognized as a feasible strategy to guide the reconstruction of corrupted signals in cloud-contaminated regions. However, due to the different imaging mechanisms and reflection characteristics, the substantial domain gap between SAR images and optical images makes it a challenging problem to effectively perform cross-modality feature fusion. Although several SAR-assisted cloud removal methods have been proposed, most of them are often unable to achieve adequate information interaction between different modalities, which greatly limits the effectiveness and reliability of cross-modality fusion. In this paper, we proposed a novel hierarchical framework for cloud-free multispectral image reconstruction, which effectively integrates SAR and optical data by a dual-domain interactive attention mechanism. The overall encoder–decoder network is a W-shaped asymmetric structure with a two-branch encoder and one decoder. The encoder branches extract features from SAR images and optical images separately, while the decoder exploits multiscale residual block groups to expand the receptive field and multi-output strategy for reducing the training difficulty. The core cross-modality feature fusion module at the bottleneck adopts the dual-domain interactive attention (DDIA) mechanism which enhances the reciprocal infusion of SAR and optical features to encourage the reconstruction of spectral and structural information. Furthermore, features in the spatial and frequency domains are integrated to improve the effectiveness of the fusion process. To echo the overall network structure, the loss function is designed as a multiscale loss in dual domains. The proposed method can realize sufficient information communication and effective cross-modality fusion between SAR features and optical features. Extensive experiments on the SMILE-CR dataset and SEN12MS-CR dataset demonstrated that the proposed method can outperform the seven representative deep-learning comparative methods in terms of visual performance and quantitative accuracy.

Application of U-Net Network Utilizing Multiattention Gate for MRI Segmentation of Brain Tumors.

Studies have shown that the type of low-grade glioma is associated with its shape. The traditional diagnostic method involves extraction of the tumor shape from MRIs and diagnosing the type of glioma based on corresponding relationship between the glioma shape and type. This method is affected by the MRI background, tumor pixel size, and doctors' professional level, leading to misdiagnoses and missed diagnoses. With the help of deep learning algorithms, the shape of a glioma can be automatically segmented, thereby assisting doctors to focus more on the diagnosis of glioma and improving diagnostic efficiency. The segmentation of glioma MRIs using traditional deep learning algorithms exhibits limited accuracy, thereby impeding the effectiveness of assisting doctors in the diagnosis. The primary objective of our research is to facilitate the segmentation of low-grade glioma MRIs for medical practitioners through the utilization of deep learning algorithms. In this study, a UNet glioma segmentation network that incorporates multiattention gates was proposed to address this limitation. The UNet-based algorithm in the coding part integrated the attention gate into the hierarchical structure of the network to suppress the features of irrelevant regions and reduce the feature redundancy. In the decoding part, by adding attention gates in the fusion process of low- and high-level features, important feature information was highlighted, model parameters were reduced, and model sensitivity and accuracy were improved. The network model performed image segmentation on the glioma MRI dataset, and the accuracy and average intersection ratio (mIoU) of the algorithm segmentation reached 99.7%, 87.3%, 99.7%, and 87.6%. Compared with the UNet, PSPNet, and Attention UNet network models, this network model has obvious advantages in accuracy, mIoU, and loss convergence. It can serve as a standard for assisting doctors in diagnosis.

Powder Bed Fusion Techniques in Metal 3D Printing: A Review

The use of 3D printing (additive manufacturing) with metal has grown significantly in demand recently, greatly reducing the time and expense required to produce complex interconnected metal components. This method minimizes material wastage, facilitates material recycling, and eliminates the need for support materials. Among the various Metal Additive Manufacturing techniques, Powder Bed Fusion (PBF) processes stands out as the most prevalent for manufacturing parts. Within the realm of PBF, electron beam melting technique, selective laser sintering technique, and selective laser melting technique are the primary methods employed. Selective laser melting and selective laser sintering operate without the need for any special conditions, unlike EBM, which necessitates a vacuum environment. Regarding the choice of materials, laser melting/sintering processes are suitable for almost all types of metals except those which surpasses beam melting capabilities. While electron beam melting is constrained to a few materials such as titanium alloys, cobalt and chromium alloys, and nickel alloys, whereas selective laser melting and sintering allows for a broad range of materials, including iron and steel alloys. However, electron beam melting exhibit the ability to process brittle materials that would typically be challenging for melting and sintering through laser. Nevertheless, the ductility, yield testing, and ultimate testing of materials created through EBM are inferior to those processed by laser methods. Although all PBF techniques excel at creating complex structures, finishing products to have a smooth surface directly over a rough surface remains a subject of ongoing research. To attain suitable mechanical properties such as hardness, tensile strength, and endurance, critical process factors include power of laser or beam, speed for scanning, density for powder bed, thickness of laser or beam, and material characteristics. Inadequate material selection coupled with incorrect process settings can lead to issues such as porosity, slag formation, and other flaws.

Containment Control-Guided Boundary Information for Semantic Segmentation

Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the concept of containment control in a pioneering way, which treats image interior elements as well as image boundary elements as followers and leaders in containment control, respectively. Based on this, we utilize two learnable feature fusion matrices in the high-level semantic information stage of the model to quantify the fusion process of internal and boundary features. Further, we design a dedicated loss function to update the parameters of the feature fusion matrices based on the criterion of containment control, which enables fine-grained communication between target features. In addition, our model incorporates a Feature Enhancement Unit (FEU) to tackle the challenge of maximizing the utility of multi-scale features essential for semantic segmentation tasks through the meticulous reconstruction of these features. The proposed model proves effective on the publicly available Cityscapes and CamVid datasets, achieving a trade-off between effectiveness and speed.

Feature size specific processing parameters for additively manufactured Ti-6Al-4V micro-strut lattices

The material properties of individual micro-struts are critical to the overall success of lattice structures. These properties can be significantly compromised by defects inherited from powder bed fusion processes. Among these defects, porous inclusions are well understood to have a detrimental effect on mechanical properties; posing a high risk to the implant under loading. While the majority of these defects can be avoided through optimisation of printing parameters, this has generally only been done for traditional bulk components with no in-designed porosity. Furthermore, a number of studies have observed changes in the frequency of such porous inclusions as feature size is reduced, indicating a size effect. This also suggests that the optimal parameters for bulk material are not necessarily translatable to the individual micro-struts which build the lattice. In this study, the relationship between parameter optimisation and feature size was investigated. Here, a higher energy density input was required for processing micro-strut lattices with an optimised relative density, than it was for bulk components. This could be attributed to faster rates of heat loss in micro-strut samples on account of their increased surface-to-volume ratio. The consequential improvement in mechanical properties was also assessed. An increase in both strength and stiffness could be largely attributed to an increase in the percentage volume of load bearing material, while improvements in failure strain were largely driven by minimisation of stress concentrations around the irregular pore morphologies. Fatigue properties did not improve beyond the effects of yielding. Rather, crack initiation was dominated by surface defects; which on account of their surface free energy, experience a much higher stress intensity factor.

High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation)

BackgroundHyperspectral imaging techniques have emerged as powerful tools for non-invasive investigation of artworks. This paper employs either reflectance imaging spectroscopy (RIS) or macroscopic X-ray fluorescence (MA-XRF) imaging in combination with macroscopic X-ray powder diffraction (MA-XRPD) for state-of-the-art chemical imaging of painted cultural heritage artefacts. While RIS can provide molecular information and MA-XRF can offer elemental distribution maps of paintings of high lateral resolution, the unique advantage of MA-XRPD lies in its ability to visualize the distributions of specific pigments and estimate in a quantitative manner the relative concentrations of the crystalline phases at the surface of artworks. However, MA-XRPD is more time-consuming and offers a lower lateral resolution than RIS and MA-XRF. ResultsThis study introduces a machine learning (ML) approach to obtain the distribution of specific compounds on the surface of artworks with a resolution that is comparable to that of RIS and MA-XRF data but with the compound specificity of MA-XRPD. The general aim is to expedite non-destructive artwork imaging analysis by fusing data from different imaging modalities via machine learning models. The effect of preprocessing techniques to enhance the predictive accuracy of the models is explored. The paper demonstrates the method's efficacy on a 16th-century illuminated manuscript, showcasing the feasibility of predicting compound-specific distribution maps. Three evaluation methods—visual examination of the predicted distribution, root mean square errors (RMSE), and feature permutation importance (FPI)—are employed to assess model performance. Fusing MA-XRF with MA-XRPD led to the best RMSE scores overall. However, fusing the RIS and MA-XRPD data blocks also yield very satisfactory and easily interpretable high-resolution compound maps. SignificanceWhile MA-XRPD allows for highly specific imaging of artworks, its time-consuming nature and limited resolution presents a bottleneck during non-invasive imaging of painted works of art. By integrating data from more time-efficient hyperspectral techniques such as MA-XRF and RIS, and employing machine learning, we expedite the process without compromising accuracy. The fusion process can also denoise the distribution maps, improving their readability for heritage professionals and art historical scholars.

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Novel hybrid waves solutions of Sawada–Kotera like integrable model arising in fluid mechanics

The purpose of the paper is to uncover more advanced soliton solutions for the three-component Sawada–Kotera (SK)-like equation. The Hirota bilinear (HB) method is exploited to obtain a bilinear form and general solution to the three-component SK like equation. The study explores various soliton solutions by considering multiple dispersion coefficients. The focus is on multiple bifurcated solitons, hybrid breathers, periodic lumps, and periodic rogue waves, as well as the resonance in soliton solutions. The fission or fusion processes of solitons are analyzed for specific values of parameters. From the literature, real Y-shaped and X-shaped solitons in water waves are provided. The results are presented through graphs generated using MATLAB 2021. The important feature of the proposed study is to show different behavior of the soliton in each component. The behavior of solitons, their interactions, and their transformations are all governed by the fundamental concept of energy conservation in all three cases. The amplitudes and forms of the solitons are impacted by the energy redistribution that occurs during collisions, fission, or other interactions. Understanding these energy dynamics aids in predicting how these nonlinear wave events would behave in complex systems and sheds light on the fundamental physics of these phenomena.

Digital twin–driven optimization of laser powder bed fusion processes: a focus on lack-of-fusion defects

Purpose The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion. Design/methodology/approach This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone. Findings This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach. Originality/value Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

Additive Fertigung von Zahnimplantaten

Abstract A novel additive-subtractive process chain based on the Laser Powder Bed Fusion process (PBF-LB) is being developed for the manufacturing of patient-specific dental implants. The study includes finite element analyses to evaluate implant materials and geometries. Investigations are performed with pure titanium. Dental requirements for the corresponding process chain are also described.

Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection.

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS.

Experimental study on the pressure drop of helium purge gas in particle crushing pebble beds

The solid breeder blanket is a critical component in fusion reactor, where helium purge gas flows through the solid breeder pebble bed to carry out the tritium generated during the fusion process. The flow pressure drop of helium purge gas within the breeder pebble bed is a significant parameter affecting the design of the tritium extraction system. Previous studies have indicated that the helium flow in the breeder pebble bed conforms to the theory of porous media flow. However, due to potential pebble breakage during the plasma operation, the pressure drop characteristics of the helium flow in breeder pebble bed may change as the void structure changes. The objective of this study is to measure the variation in flow pressure drop of the breeder pebble bed under different pebble crushing conditions. The flow pressure drops of intact beds (Alumina, diameter 1-1.2 mm) and four groups with different pebble breakage rates (3%, 5%, 7%, 9%) are measured using the pebble bed pressure drop test facility. The following results are obtained through experimental research: (1) The Ergun equation, Foumeny equation, and Reichelt equation can all reasonably match the experimental results of intact pebble beds; (2) The pressure drop across the pebble bed increases with the increase in pebble breakage rate, reaching approximately 1.6 times that of the intact bed at a 9% breakage rate; (3) A correlation for predicting the pressure drop of the broken pebble bed is established by introducing the pebble breakage rate (η) into the Ergun equation, which can be used to determine the pressure drop variation within a conservative range of breakage rates.

Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection

The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model’s compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing advanced feature extraction and attention mechanisms. The model meets portability requirements and provides a more efficient solution for underwater detection.

Meldonium, as a potential neuroprotective agent, promotes neuronal survival by protecting mitochondria in cerebral ischemia–reperfusion injury

BackgroundStroke is a globally dangerous disease capable of causing irreversible neuronal damage with limited therapeutic options. Meldonium, an inhibitor of carnitine-dependent metabolism, is considered an anti-ischemic drug. However, the mechanisms through which meldonium improves ischemic injury and its potential to protect neurons remain largely unknown.MethodsA rat model with middle cerebral artery occlusion (MCAO) was used to investigate meldonium’s neuroprotective efficacy in vivo. Infarct volume, neurological deficit score, histopathology, neuronal apoptosis, motor function, morphological alteration and antioxidant capacity were explored via 2,3,5-Triphenyltetrazolium chloride staining, Longa scoring method, hematoxylin and eosin staining, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay, rotarod test, transmission electron microscopy and Oxidative stress index related kit. A primary rat hippocampal neuron model subjected to oxygen–glucose deprivation reperfusion was used to study meldonium’s protective ability in vitro. Neuronal viability, mitochondrial membrane potential, mitochondrial morphology, respiratory function, ATP production, and its potential mechanism were assayed by MTT cell proliferation and cytotoxicity assay kit, cell-permeant MitoTracker® probes, mitochondrial stress, real-time ATP rate and western blotting.ResultsMeldonium markedly reduced the infarct size, improved neurological function and motor ability, and inhibited neuronal apoptosis in vivo. Meldonium enhanced the morphology, antioxidant capacity, and ATP production of mitochondria and inhibited the opening of the mitochondrial permeability transition pore in the cerebral cortex and hippocampus during cerebral ischemia–reperfusion injury (CIRI) in rats. Additionally, meldonium improved the damaged fusion process and respiratory function of neuronal mitochondria in vitro. Further investigation revealed that meldonium activated the Akt/GSK-3β signaling pathway to inhibit mitochondria-dependent neuronal apoptosis.ConclusionOur study demonstrated that meldonium shows a neuroprotective function during CIRI by preserving the mitochondrial function, thus prevented neurons from apoptosis.

Satellite cell-derived TRIM28 is pivotal for mechanical load- and injury-induced myogenesis

Satellite cells are skeletal muscle stem cells that contribute to postnatal muscle growth, and they endow skeletal muscle with the ability to regenerate after a severe injury. Here we discover that this myogenic potential of satellite cells requires a protein called tripartite motif-containing 28 (TRIM28). Interestingly, different from the role reported in a previous study based on C2C12 myoblasts, multiple lines of both in vitro and in vivo evidence reveal that the myogenic function of TRIM28 is not dependent on changes in the phosphorylation of its serine 473 residue. Moreover, the functions of TRIM28 are not mediated through the regulation of satellite cell proliferation or differentiation. Instead, our findings indicate that TRIM28 regulates the ability of satellite cells to progress through the process of fusion. Specifically, we discover that TRIM28 controls the expression of a fusogenic protein called myomixer and concomitant fusion pore formation. Collectively, the outcomes of this study expose the framework of a novel regulatory pathway that is essential for myogenesis.

A Qualitative Comparison of Dimensional Deviation of Laser Powder Bed Fusion Processed Multi Morphology Lattice Structures Based on Thermomechanical Simulations

Multi morphology lattices are composite lattice structures formed by different types of lattice structures in different configurations based on engineering application needs. These types of lattices can be manufactured with additive manufacturing modalities, specifically laser powder bed fusion process. However, due to the high cost and time spent on manufacturing these components, it is necessary to predict the properties of manufactured parts before build with numerical methods. This study focused on prediction of dimensional deviation of laser powder bed fusion produced multi morphology lattice structures composed of Schoen Gyroid, Schwarz Diamond, Schwarz Primitive, Schoen FRD and Neovius topologies via thermomechanical simulations qualitatively. Since the comparisons were made based on numerical study results, only qualitative assessments were performed. Lattices were generated by using a novel, in-house developed tool. Among multi morphology lattices investigated in the present study, Schoen FRD topology at the center and Schwarz Primitive topology at the outer region showed the lowest deviations and Schoen FRD topology at the center and Schwarz Diamond topology at the outer region showed the highest deviations. It was also shown that adding Schoen FRD or Schwarz Primitive topologies at the outer region reduces the max. deviations, and Schoen Gyroid or Schwarz Diamond topologies increases the max. deviations.

Measures of solid-state material and powder bed absorptivity in the visible and near IR spectrum with new small homemade integrative spheres

Abstract The absorptivity is a major parameter in laser-matter interaction. It can be measured by different methods (thermal or optical). The already known integrating sphere method is generally used with spheres whose choice of dimensions and architecture is not discussed in the literature. The influence of the parameters of the sphere (size of the sphere, position of the photodiode on the sphere, distance of the photodiode from the sphere, opening of the passage of the laser, distance from sample to the sphere, presence of a baffle in front of the photodiode, sample angle, coating materials) was tested with a mirror of known reflectivity. This study has demonstrated that it is possible to use small integrating spheres for absorptivity measurements. These spheres are home-made by additive manufacturing from a polymer, with the inner walls coated with BaSO4. The optimum experimental conditions for these small spheres are defined, particularly the angle sample. The absorptivity of various materials in the solid state and powder bed was measured for four wavelengths. These measurements were carried out for three sizes of spheres and confirmed the literature results (for example, the absorptivity is measured at 80% for copper at blue wavelength). This small sphere will be adapted to in-situ measurement, particularly in the Laser Powder Bed Fusion process whatever wavelength, particularly new green or blue laser sources, and material.

Heterojunction of natural clay minerals and carbon nanotubes as robust moisture electric generator

Among various sustainable energy harvesting devices, moisture electric generators (MEG) possess distinct advantages like portability, minimum human intervention, absence of moving parts, unnecessity of fuels, and potential for off-grid power generation. However, maturing this emerging technology requires the exploration of a diverse range of materials and device configurations. Here, we report the fabrication of heterojunction-based robust MEGs by using reconstructed layers of natural vermiculite clay (VM) and HNO3-treated oxidized multi-walled carbon nanotubes (o-CNT). The heterojunction of negatively charged VM and o-CNT membranes (o-CNT-VM) prepared through a facile water-assisted fusion process yielded voltages of ∼ 790 mV for more than 2000 s (at 75 % RH). Assembling multiple o-CNT-VM devices in series connection output potentials up to 98 V were achieved. The mechanistic studies revealed the junction potential between VM and o-CNT to be the major contributor to the observed potential, with minor contributions arising from evaporation-driven streaming of ions through the 2D nanofluidic channels. The o-CNT-VM devices can function in extreme conditions such as temperatures ranging from 0 to 80 °C. The practical applicability of the o-CNT-VM-based MEG devices is demonstrated by lighting LED, and powering calculators by assembling o-CNT-VM devices in simultaneous series and parallel connection.

Evolution of self-assembled amphiphilic colloidal particles in strong-flavor Chinese baijiu

This study proposed the evolution of self-assembled amphiphilic colloidal particles in Strong-Flavor (SF) Baijiu based on Ostwald ripening for the first time. The evolution process occurs in two stages: disordered amphiphilic molecules self-assemble into small colloidal particles and subsequently undergo Oswald ripening to form larger hydrophobic particles. Microscopic observations revealed the average size of oil-like spherical colloidal particles in Baijiu increased from 1.86 μm to 2.96 μm while the number of particles decreased by 39.50% during the 16-year cellaring process of SF Baijiu, consistent with the particle size trend observed via laser scattering. During fusion process, the charge-to-mass ratio of positively charged colloidal particles decreased, leading ζ-potential decreased from 23.7 mV to 4.66 mV within 16 years of storage. The electrochemical impedance spectroscopy approach tracked the unidirectional variation in the dielectric constant during evolution of SF Baijiu, reflecting the gradual expansion of colloidal particles, which aligns with the evolution trend observed in molecular dynamics simulations. By integrating direct microscopic observations of amphiphilic colloidal particles with electrochemical techniques, the evolution of Baijiu samples is capable to be evaluated in-situ, laying the foundation for intelligent Baijiu aging monitoring technology.

A novel approach on artificial aging of nylon 12 powder for laser powder bed fusion

PurposeThis study aims to address challenges in the Laser Powder Bed Fusion process of polymers, focusing on the considerable amount of unsintered powder left post-printing. The objective is to understand the altered properties of this powder and find solutions to improve the process, reduce waste and explore reusing reprocessed powder.Design/methodology/approachA novel methodology is used to generate reprocessed powder without traditional printing, reducing time, cost and waste. The approach mimics the ageing effects during the printing process, providing insights into particle size distribution and thermal behaviour.FindingsResults reveal insights into artificial ageing, showing an 8.2% decrease in particle size (60.256–69.183 µm) and a 9.1% increase in particle size (17.378–19.953 µm) compared to unsintered powder. Thermal behaviour closely mirrors used powders, with variations in enthalpy of fusion (−0.55% to 2.69%) and degree of crystallinity (0.19% to 2.64%). The proposed methodology produces results that differ from those due to printing under 3% from a thermal point of view. The new process reduces the time needed for aged powder, contributing to cost savings and waste reduction.Originality/valueThe study introduces a novel method for reprocessed powder generation, deviating from traditional printing. The originality lies in artificially ageing powders, providing comparable results to actual printing. This approach offers efficiency, time savings and waste reduction in the Laser Powder Bed Fusion process, presenting a valuable avenue for further research.