npj Advanced Manufacturing最新文献

The National Aeronautics and Space Administration (NASA) plans to deploy a fully functional 3D printer in low Earth orbit for in-space manufacturing (ISM) of semiconductor electronics. Earlier, our group demonstrated the successful application of high-resolution printing for semiconductor manufacturing using electrohydrodynamic printing. However, sintering of printed films is a significant challenge due to payload constraints. In this paper, we demonstrate laser sintering as a viable technology for ISM. The first experiments of successful demonstration of laser sintering of EHD printed silver on silicon in micro and lunar gravity are presented. The microstructures and conductivity of the samples are compared to those of the ground samples, and we observe that samples under microgravity exhibit increased heat transport compared to those under gravity. We hypothesize that the samples under microgravity experience lower convective heat transport, resulting in increased surface melting. A COMSOL heat transport model supports the laser sintering studies and hypotheses.

This study investigates an artificial neural network (ANN) for predicting cross-sectional geometry in fused granulate fabrication (FGF), a key polymer-based technology in large-format additive manufacturing (LFAM). Critical process parameters-layer height, transverse speed, and screw speed-were systematically varied to study their effects on bead morphology. A full factorial design generated a robust training dataset, and cross-sectional images were processed for model training. The ANN architecture, featuring two hidden layers, was paired with image processing techniques to manage computational demands. Results showed strong agreement between predicted and experimental cross-sections, with a mean absolute error of 8.88%, highlighting the ANN's capability in capturing geometry. This approach advances prior LFAM studies by predicting full cross-sectional images rather than contour points, improving complex shape prediction. The findings demonstrate the ANN's effectiveness for FGF profiles and its potential to enhance geometric precision and generate complex shapes across LFAM technologies.

As multiple macro scale directed energy deposition (DED) processes begin to be industrially adopted for large scale component manufacture, it is imperative that interface strategies between the processes are fully understood. The present work investigates the asynchronous deposition of a wire component (DED-arc), followed by a powder-based deposition (DED-LP) with varying surface treatments which were evaluated for flatness, porosity, hardness, and Charpy impact energy. The self-regulation effect of DED-LP was fully realized with up to 55% reduction in surface variation relative to the DED-arc surface. Contrarily, as surface contaminants were not removed between each process, the resultant DED-LP porosity was significantly reduced from 99.5% to 92.4%. Albeit the reduction in density did not negatively impact the impact toughness as evidenced by a low correlation coefficient of -0.46. As such, the overall manufacturing costs and application space must be considered for selection of the different interface strategies presented in the current work.

The performance of electrochemical cells for energy storage and conversion can be improved by optimizing their manufacturing processes. This can be time-consuming and costly with the traditional trial-and-error approaches. Machine Learning (ML) models can help to overcome these obstacles. In academic research laboratories, manufacturing dataset sizes can be small, while ML models typically require large amounts of data. In this work, we propose a simple but still novel application of a Transfer Learning (TL) approach to address these manufacturing problems with a small amount of data. We have tested this approach with pre-existing experimental and stochastically generated datasets. These datasets consisted of component properties (e.g., electrode density) related to different manufacturing parameters (e.g., solid content, comma gap, coating speed). We have demonstrated the robustness of our TL approach for manufacturing problems by achieving excellent prediction performance for electrodes in lithium-ion batteries and gas diffusion layers in fuel cells.

This study provides a comprehensive analysis of residual stress characteristics in nitinol parts fabricated via laser powder bed fusion (PBF-LB). Unlike previous works that primarily focus on qualitative assessments or single-measurement techniques, this research employs a multi-modal experimental approach, Electronic Speckle Pattern Interferometry-Hole Drilling (ESPI-HD) and X-ray Diffraction (XRD), to achieve a more precise and spatially resolved evaluation of residual stress distribution. Furthermore, the study establishes a direct correlation between residual stress evolution and in situ pyrometric melt pool temperature data, an aspect that has not been extensively explored in prior investigations. A key novel finding is the non-monotonic relationship between volumetric energy density (VED) and residual stress. In this work, laser power was kept constant, and VED was varied by adjusting scanning speed and hatch spacing. The results show that the average residual stress initially increases with decreasing scan speed and hatch spacing, plateaus at a critical threshold, and subsequently decreases. However, residual stress was also found to vary in the build direction, indicating the complex stress distributions and accommodation mechanisms within the material. Additionally, an inverse relationship was recorded between the thermal gradient and VED which challenges conventional assumptions about their relationship. These insights offer a new perspective on optimizing PBF-LB process parameters for enhanced structural performance and long-term reliability of additively manufactured nitinol.