Search Results (105)

Powder bed fusion of copper has been extensively investigated using both laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) additive manufacturing technologies. Each technique offers unique benefits as well as specific limitations. Near-infrared (NIR) laser-based LPBF is widely accessible; however, the high reflectivity of copper limits energy absorption, thereby resulting in a narrow processing window. Although optimized parameters can yield relative densities above 97%, issues such as keyhole porosity, incomplete melting, and anisotropy remain concerns. Green lasers, with higher absorptivity in copper, offer broader process windows and enable more consistent fabrication of high-density parts with superior electrical conductivity, often reaching or exceeding 99% relative density and 100% International Annealed Copper Standard (IACS). Mechanical properties, including tensile and yield strength, are also improved, though challenges remain in surface finish and geometrical resolution. In contrast, Electron Beam Powder Bed Fusion (EB-PBF) uses high-energy electron beams in a vacuum, eliminating oxidation and leveraging copper’s high conductivity to achieve high energy absorption at lower volumetric energy densities (~80 J/mm³). This results in consistently high relative densities (>99.5%) and excellent electrical and thermal conductivity, with additional benefits including faster scanning speeds and in situ monitoring capabilities. However, EB-PBF faces its own limitations, such as surface roughness and powder smoking. This paper provides a comprehensive review of the current state of laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) powder bed fusion processes for the additive manufacturing of copper, summarizing key trends, material properties, and process innovations. Both approaches continue to evolve, with ongoing research aimed at refining these technologies to enable the reliable and efficient additive manufacturing of high-performance copper components. Full article

Limited recovery of the viscoelastic properties of aged asphalt on RAP surfaces at ambient temperature reduces interface fusion and bonding with new emulsified asphalt, degrading pavement performance and limiting large-scale promotion and high-value applications of the emulsified asphalt cold recycled mixture (EACRM). Therefore, a cold regenerant was independently prepared to rapidly penetrate, soften, and activate aged asphalt at ambient temperature in this paper, and its effects on the volumetric composition, mechanical strength, and pavement performance of EACRM were systematically investigated. The results showed that as the cold regenerant content increased, the air voids, indirect tensile strength (ITS), and high-temperature deformation resistance of EACRM decreased, while the dry–wet ITS ratio, cracking resistance, and fatigue resistance increased. Considering the comprehensive pavement performance requirements of cold recycled pavements, the optimal content of the activated cold regenerant for EACRM was determined to be approximately 0.6%. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs to be solved. In this paper, a three-phase transient model based on the level-set method is established to examine the heat transfer and melt pool behavior in PBF-LB/M. Surface tension, the Marangoni effect, and recoil pressure are implemented in the model, and evaporation-induced mass and thermal loss are fully considered in the computing element. The results show that the surface roughness and density of metal parts induced by heat transfer and melt pool behavior are closely related to process parameters such as laser power, layer thickness, scanning speed, etc. When the volumetric energy density is low, the insufficient fusion of metal particles leads to pore defects. When the line energy density is high, the melt track is smooth with low porosity, resulting in the high density of the products. Additionally, the partial melting of powder particles at the beginning and end of the melting track usually contributes to pore formation. These findings provide valuable insights for improving the quality and reliability of metal additive manufacturing. Full article

For the local oscillation phenomenon of the APF algorithm in the face of static U-shaped obstacles, the path cusp phenomenon caused by the vehicle corner and path curvature constraints is not taken into account, as well as the low path safety caused by ignoring the vehicle volume constraints. Therefore, an APF-Dijkstra path planning fusion algorithm based on steering model and volume constraints is proposed to improve it. First, perform an expansion treatment on the obstacles in the map, optimize the search direction of the Dijkstra algorithm and its planned global path, ensuring that the distance between the path and the expanded grid is no less than 1 m, and use the path points as temporary target points for the APF algorithm. Secondly, a Gaussian function is introduced to optimize the potential energy function of the APF algorithm, and the U-shaped obstacle is ellipticized, and a virtual target point is used to provide the gravitational force. Again, the three-point arc method based on the steering model is used to determine the location of the predicted points and to smooth the paths in real time while constraining the steering angle. Finally, a 4.5 m × 2.5 m vehicle rectangle is used instead of the traditional mass points to make the algorithm volumetrically constrained. Meanwhile, a model for detecting vehicle collisions is established to cover the rectangle boundary with 14 envelope circles, and the combined force of the computed mass points is transformed into the combined force of the computed envelope circles to further improve path safety. The algorithm is validated by simulation experiments, and the results show that the fusion algorithm can avoid static U-shaped obstacles and dynamic obstacles well; the curvature change rate of the obstacle avoidance path is 0.248, 0.162, and 0.169, and the curvature standard deviation is 0.16, which verifies the smoothness of the fusion algorithm. Meanwhile, the distances between the obstacles and the center of the rear axle of the vehicle are all higher than 1.60 m, which verifies the safety of the fusion algorithm. Full article

High-entropy alloys are known for their promising mechanical properties, wear and corrosion resistance, which are maintained across a wide range of temperatures. In this study, a CoCrFeNiCu-based high-entropy alloy, distinguished from conventional CoCrFeNi systems by the addition of Cu, which is known to enhance toughness and wear resistance, was investigated to better understand the effects of compositional modification on processability and performance. The influence of key process parameters, specifically laser power and scan speed, on the processability of CoCrFeNiCu-based high-entropy alloys produced by laser powder bed fusion additive manufacturing was investigated, with a focus of low laser power, which is critical for minimizing defects and improving the resulting microstructure and mechanical performance. The printed sample density gradually increases with higher volumetric energy density, achieving densities exceeding 99.0%. However, at higher energy densities, the samples exhibit susceptibility to hot cracking, an issue that cannot be mitigated by adjusting the process parameters. Mechanical properties under optimized parameters were further evaluated using Charpy impact and (in situ) tensile tests. These evaluations were supplemented by in situ tensile experiments conducted within a scanning electron microscope to gain insights into the behavior of defects, such as hot cracks, during tensile testing. Despite the sensitivity to hot cracking, the samples exhibited a respectable ultimate tensile strength of 662 MPa, comparable to fine-grained steels like S500MC (070XLK). These findings underscore the potential of CoCrFeNiCu-based high-entropy alloys for advanced applications. However, they also highlight the necessity for developing strategies to ensure stable and reliable processing methods that can mitigate the susceptibility to hot cracking. Full article

Phase change materials (PCMs) can be utilized in buildings for peak load shifting in air conditioning systems, and the use of salt hydrate-based PCMs can reduce the cost of thermal energy storage devices. Glauber’s salt is an economical salt hydrate PCM with a melting point of around 32 °C. However, the desired melting range typically falls between 18 and 22 °C for building air conditioning applications. Although many researchers have characterized Glauber’s salt and its composites with modified melting points, enthalpy–temperature curves for composites of Glauber’s salt and NaCl are unavailable. In this study, we report the melting and solidification enthalpy–temperature curves for two different composites of Glauber’s salt and NaCl with a melting point of 21 °C obtained by the T-history method. Both composites contain NaCl to suppress the melting point, borax to reduce supercooling, and sodium polyacrylate as a thickener to enhance cyclic stability. The first composite with 12 wt.% NaCl demonstrated 139 kJ·kg⁻¹ of latent heat of fusion, and the second composite with 9 wt.% NaCl demonstrated 171 kJ·kg⁻¹. Both the composites have high volumetric energy densities compared to their organic counterparts with similar melting points. Full article

The Volumetric Neutron Source (VNS) is a pivotal facility proposed for advancing fusion nuclear technology, particularly for the qualification of breeding blanket systems, a key component of DEMO and future fusion reactors. This study focuses on the design and optimisation of the VNS Thermal Shield, adopting a multidisciplinary approach to address its thermal and structural behaviours. The Thermal Shield plays a crucial role in protecting superconducting magnets and other cryogenic components by limiting heat transfer from higher-temperature regions of the tokamak to the cryostat, which operates at temperatures between 4 K and 20 K. To ensure both thermal insulation and structural integrity, multiple design iterations were conducted. These iterations aimed to reduce electromagnetic (EM) forces induced during magnet charge and discharge cycles by introducing strategic cuts and reinforcements in the shield design. The optimisation process included the evaluation of various aluminium alloys and composite materials to achieve a balance between rigidity and weight while maintaining structural integrity under EM and mechanical loads. Additionally, an integrated thermal study was performed to ensure effective temperature management, maintaining the shield at an operational temperature of around 80 K. Cooling channels were incorporated to homogenise temperature distribution, improving thermal stability and reducing thermal gradients. This comprehensive approach demonstrates the viability of advanced material solutions and design strategies for thermal and structural optimisation. The findings reinforce the importance of the VNS as a dedicated platform for testing and validating critical fusion technologies under operationally relevant conditions. Full article

Coupling organic light-harvesting materials with lead halide perovskite quantum dots (LHP QDs) is an attractive approach that could provide great potential in optoelectronic applications owing to the diversity of organic materials available and the intriguing optical and electronic properties of LHP QDs. Here, we demonstrate energy collection by CsPbI₃ QDs from a near-infrared (NIR) light-harvesting upconversion system. The upconversion system consists of Pd-tetrakis-5,10,15,20-(p-methoxycarbonylphenyl)-tetraanthraporphyrin (PdTAP) as the sensitizer to harvest NIR photons and rubrene as the annihilator to generate upconverted photons via triplet fusion. Steady-state and time-resolved photoluminescence spectra reveal that CsPbI₃ QDs are energized via radiative energy transfer from the singlet excited rubrene with photophysics fidelity of respective components. In addition, a volumetric display demo incorporating CsPbI₃ QDs as light emitters employing triplet fusion upconversion was developed, showing bright luminescent images from CsPbI₃ QDs. These results present the feasibility of integrating organic light-harvesting systems and perovskite QDs, enabling diverse light harvesting and activation of perovskite materials for optoelectronic applications. Full article

Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. In this paper, we present an integrated approach based on hypergraph convolutional networks (HGCNs) for longitudinal predictions of KOA grades and progressions from MRI images. We propose two novel models, namely, the C_Shape.Net and the predictor network. The C_Shape.Net operates on a hypergraph of volumetric nodes, especially designed to represent the surface and volumetric features of the cartilage. It encompasses deep HGCN convolutions, graph pooling, and readout operations in a hierarchy of layers, providing, at the output, expressive 3D shape descriptors of the cartilage volume. The predictor is a spatio-temporal HGCN network (ST_HGCN), following the sequence-to-sequence learning scheme. Concretely, it transforms sequences of knee representations at the historical stage into sequences of KOA predictions at the prediction stage. The predictor includes spatial HGCN convolutions, attention-based temporal fusion of feature embeddings at multiple layers, and a transformer module that generates longitudinal predictions at follow-up times. We present comprehensive experiments on the Osteoarthritis Initiative (OAI) cohort to evaluate the performance of our methodology for various tasks, including node classification, longitudinal KL grading, and progression. The basic finding of the experiments is that the larger the depth of the historical stage, the higher the accuracy of the obtained predictions in all tasks. For the maximum historic depth of four years, our method yielded an average balanced accuracy (BA) of 85.94% in KOA grading, and accuracies of 91.89% (+1), 88.11% (+2), 84.35% (+3), and 79.41% (+4) for the four consecutive follow-up visits. Under the same setting, we also achieved an average value of Area Under Curve (AUC) of 0.94 for the prediction of progression incidence, and follow-up AUC values of 0.81 (+1), 0.77 (+2), 0.73 (+3), and 0.68 (+4), respectively. Full article

The effects of laser powder bed fusion (LPBF) parameters, such as power (200 to 350 W) and scan speeds (from 200 to 2000 mm/s), on the microstructure and mechanical properties of high-strength, low-alloy (HSLA) AISI 4340 steel were examined. A wide range of volumetric energy density (VED) between 93 and 162 J/mm³ produced samples with relative densities greater than 99.8%. The optimal parameter set was identified with laser power = 200 W, scan speed = 600 mm/s, hatch spacing = 0.12 mm, and slice thickness = 0.03, corresponding to VED = 92.6 J/mm³. Scanning electron microscopy revealed a predominantly martensitic microstructure for all processing parameters examined, although X-ray diffraction revealed the minor presence of retained austenite within the as-fabricated 4340 steel. Using the optimized LPBF parameters, the as-fabricated 4340 steel exhibited a yield strength of 1317 MPa ± 16 MPa, ultimate tensile strength of 1538 MPa ± 22 MPa, and 18.6 ± 1% strain at failure. These are similar to wrought 4340 steel quenched and tempered between 400 and 600 °C. Full article

Accurate segmentation is essential for creating digital twins based on volumetric images for high fidelity composite material analysis. Conventional techniques typically require labor-intensive and time-consuming manual effort, restricting their practical use. This paper presents a deep learning model, MBL-TransUNet, to address challenges in accurate tow-tow boundary identification via a Boundary-guided Learning module. Fabrics exhibit periodic characteristics; therefore, a Multi-scale Feature Fusion module was integrated to capture both local details and global patterns, thereby enhancing feature fusion and facilitating the effective integration of information across multiple scales. Furthermore, BatchFormerV2 was used to improve generalization through cross-batch learning. Experimental results show that MBL-TransUNet outperforms TransUNet. MIoU improved by 2.38%. In the zero-shot experiment, MIoU increased by 4.23%. The model demonstrates higher accuracy and robustness compared to existing methods. Ablation studies confirm that integrating these modules achieves optimal segmentation performance. Full article

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing. Full article

This paper focuses on optimizing the process parameters for manufacturing commercially pure titanium grade 2 using Laser Powder Bed Fusion (L-PBF) technology. The most common approach involves trial-and-error builds with varying parameter combinations, followed by characterizing the bulk samples for defects and the microstructure. This method, typically based on Volumetric Energy Density (VED), is time-consuming and overlooks key powder properties. An alternative approach involves the use of efficient Volumetric Energy Density (VED_eff), which represents the energy density effectively available for the L-PBF process, considering both the process parameters and powder properties such as absorptivity and thermal diffusivity. In this study, VED_eff was applied and compared to a work window defined by thermodynamic data, with limits corresponding to the energy needed for titanium melting and evaporation. Forty-two tests were performed with different combinations of laser powers and scanning speeds; the samples were then characterized in terms of porosity, microstructure, and hardness. The findings showed no correlation between VED and the work window while VED_eff aligned with the work window, although the highest relative densities (>99%) and hardness values were achieved in a narrower range. Despite this, the VED_eff approach proved to be a useful starting point for optimizing the process parameters. Full article

The properties of the final product obtained by solidification directly result from the thermal variables during solidification. This study aims to analyze the influence of thermal solidification variables on the hardness, microstructure, and phases of the CuAl₆Si₂ alloy. The material was solidified using unidirectional solidification equipment under non-stationary heat flow conditions, where heat extraction is conducted through a water-cooled graphite base. The thermal solidification variables were extracted using a data acquisition system, and temperature was monitored at six different positions, with cooling rates ranging from 217 to 3 °C/min from the nearest to the farthest position from the heat extraction point. An optical microscope, scanning electron microscope (SEM), and X-ray diffraction (XRD) were used to verify the fusion structure and determine the volumetric fraction of the formed phases. The XRD results showed the presence of β phases, α phases, and possible Fe₃Si₂ and Fe₅Si₃ intermetallics with different morphologies and volumetric fractions. Positions with lower cooling rates showed an increased volume fraction of the α phase and possible intermetallics compared to positions with faster cooling. High cooling rates increased the Brinell hardness of the alloy due to the refined and equiaxed β metastable phase, varying from 143 HB to 126 HB for the highest and lowest rates, respectively. Full article