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They consist of micro- and nanoscale size copper, solvents, organic metal precursors, and organic binders. The size of nanoparticles and microparticles is about 150 nm and approximately within a micrometer, respectively. Two other differences are the viscosity and solid content of the copper pastes. At ambient conditions and with a shear rate of 50/s, leading to different viscosities of HPA, HPB, and NPC. In addition, the solid content of HPA, HPB, and NPC are 78.8%, 76.0%, and 84%, respectively.
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Here, the material is in a more advanced stage than HPA at this temperature. Indeed, the electrical conductivity σHPA and σNPC at 175 °C is 2.3 μS.cm−1 and 78.2 μS.cm−1, respectively. This finding is in line with the tortuosity analysis and it provides further insight into the enhanced electrical property of the material NPC. Further, we investigate the pore-copper interface evolution upon sintering using the Gaussian curvature G and mean curvature M47. Both curvatures classify the local surface geometries with their joint distributions48.
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Color coding indicates the strut diameters variation within the volume ranging from 0 (white) to 1 µm (yellow). E Statistical analysis of the strut diameter ϕ for different temperatures and sample sets. Sample HPA, HPB and NPC are indicated by blue, gold, and red, respectively.
Physics-informed machine learning
While accepting the 2002 Machine Design Award presented by the Design Engineering Division of ASME, Robert Norton argued passionately for the preservation of a machine theory course as the gateway to a mechanical design curriculum. From beginning to end he focuses attention on design including many project ideas, as he presents the analysis and synthesis of machine systems constructed from gears, cams and linkages. The result is 850 well-written pages packed with useful examples and illustrations. The material covered in this book includes the fundamentals of design, the kinematics of mechanisms, as well as force analysis in machines. Graphical techniques for both analysis and synthesis build visualization and insight to the meaning of the algebraic formulations, and the complex vector approach provides a compact notation for the study of planar machines.
On the kinematics and synthesis of a geared five-bar slider–crank mechanism
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The curtaining and shadowing artifacts of the obtained tomography image data are reduced with FFT-filter69 and histogram shifting methods35,38, respectively. During the sintering, the surface area is reduced by the growing of bonds between the sinter particles. The driving force for the sintering decreases as the surface area is annihilated. The decline of the specific surface area SA with the sinter temperature is shown in Fig. At 175 °C, the specific surface area for HPA and NPC is about a factor of two larger than for HPB.
For the prediction of the electrical conductivity, we define three different microstructure sets, as an input for the MVLR, which are not used as training data sets, see Supplementary Note 8. Subsequently, the outcomes are compared with the experimental data. For an improved design of the microstructure in context to the targeted property, it is essential to understand the correlation between the physical descriptors describing the microstructure and the property. Figure 2a illustrates the segmented 3D microstructure for sample HPA, HPB and NPC, utilizing the U-net model based on the semi-automatic hybrid annotation.
This type of surface illustrates that HPA is still in an early stage of sintering50; therefore, the particles are just starting to coalesce and their necks are newly formed. As a result, the necks radii are flattened and its G magnitude is decreased towards zero. Subsequently, the small radii nanoparticles’ convex surfaces are diminishing and M decreases.
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Each reconstructed 3D dataset comprises about 450 images with an image size of 1120 × 640 pixels2 which makes an automated analysis approach indispensable, see Methods for further details regarding the image acquisition. China’s rapid progress in e-commerce and logistics warehousing has introduced a new era of efficient and high-quality industries, presenting new problems for training professionals in logistics. This research presents an innovative method that employs machine learning and digital twin AI simulation technology to tackle these difficulties.
The analysis is performed by utilizing the shape_index module in the python’s scikit-image library. The alignment is performed by FIB stack wizard module with least square method. The volume of interest (VOI) from each reconstructed 3D dataset is 1120 × 640 × 450 voxel3. The raw 3D data has voxel sizes of 18.6 nm, 18.6 nm, and 25 nm in the x, y, and z directions, respectively. We cut 3 VOIs of 10 × 10 × 10 μm3 from this volume for each samples measurements.
6 and Table 3 even for the NPC material, which illustrates a homogenous nano-porous structure, the DDPM predicts better than the cGAN. As a result, the structure-property relationship can be defined arithmetically. We train the models with at least two microstructure features obtained from the segmented VOIs.
We conduct FIB-SEM tomography to image three different porous copper materials. For the first and second sample set, we use sinter pastes consisting of micro- and nanoparticles. Those sample sets are indicated as hybrid-paste material A (HPA) and B (HPB), respectively. The third sample set is composed of nanoparticles and is labeled as nano-paste material C (NPC), see Methods for further sample details. For the investigation of the microstructure evolution upon temperature, six sinter temperatures with 175 °C, 200 °C, 225 °C, 250 °C, 350 °C, and 400 °C are selected, see Fig.1a.
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