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Table Of Content

• Multiple S3 buckets as data sources
• Methods
• §3011. Machine Rooms and Machinery Spaces.
• Evolution of the copper strut-interconnectivity and investigation on the surface properties upon cycling
• Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach
• Learning Through Action: On the Use of Logistics4.0 Lab as Learning Developer

The tortuosity τ is quantified by averaging the extracted tortuosity values along the y-direction of the last 25% from the sample’s length38. Further information with respect to the tortuosity is provided in Supplementary Note 5 where we calculate the overall tortuosity in five directions and average them. The grille enclosure shall be of a design which will reject a ball 2 in. Based on the segmented 3D data, utilizing the U-NET with the hybrid training approach, we perform the curvature analysis. The Avizo 3D curvature module extracts the Gaussian and mean curvature data from the pore-copper surface. The joint probability distribution plot and the mean values of the curvatures are done with the gaussian_kde module and the NumPy package in Python, respectively.

Multiple S3 buckets as data sources

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.

Methods

The book is praised for its friendly writing style, clear exposition of difficult topics, attractive appearance, thorough and relevant coverage, its emphasis on synthesis and design, and its useful computer programs. The foremost goal of the author is to convey the art of the design process and the fundamentals of kinematics and dynamics in order to prepare students to successfully tackle genuine engineering problems encountered in practice. While both thorough and complete on the topics of analysis, the book also emphasizes the synthesis and design aspects of the subject to a greater degree than any other similar book on the market today.

§3011. Machine Rooms and Machinery Spaces.

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.

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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.

Evolution of the copper strut-interconnectivity and investigation on the surface properties upon cycling

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.

Consequently, the low temperature plots show the early stage of sintering. In contrast, the QII tails show the progress of sintering when the particles are joining. This progress indicates the formation of necks and concave radii. Interestingly as temperature increases, the QI tails tend to get shorter and the QII tails tend to get denser and longer.

Learning Through Action: On the Use of Logistics4.0 Lab as Learning Developer

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.

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.

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.

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.

SEM-based methods provide advantages with respect to contrast and resolution. Nevertheless, fully automated segmentation of SEM image data especially for porous materials is challenging because of the ever present shine through artifacts23. Here, conventional segmentation methods are often limited due to misinterpretation of fore- and background information24. Figure 5a, d, g show the segmented real microstructure indicated by the pore and copper phases for different sinter temperatures. For the segmentation the introduced U-Net architecture, trained with the hybrid model, is used. 5c, f, i the reconstructed synthetic microstructure images depicted from the cGAN model and DDPM, respectively, are illustrated.

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.

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.