Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods (Record no. 61962)
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| 000 -LEADER | |
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| fixed length control field | 02667nam a2200265Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251009160708.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-21873 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251009s9999 xx 000 0 und d |
| 245 10 - TITLE STATEMENT | |
| Title | Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods |
| 490 0# - SERIES STATEMENT | |
| Series statement | Composites Science and Technology, 184, 107861, 2019. |
| 500 ## - GENERAL NOTE | |
| General note | Artículo |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Effective thermal conductivity is an important property of composites for different thermal management applications. Although physics-based methods, such as effective medium theory and solving partial differential equations, are widely applied to extract effective thermal conductivity, recently there is increasing interest to establish the structure-property linkage through machine learning methods. The prediction accuracy of conventional machine learning methods highly depends on the features (descriptors) selected to represent the microstructures. In comparison, 3D convolutional neural networks (CNNs) can directly extract geometric features of composites, which have been demonstrated to establish structure-property linkages with high accuracy. However, to obtain the 3D microstructure in the composite is challenging in reality. In this work, we use 2D cross-section images and 2D CNNs to predict effective thermal conductivity of 3D composites, since 2D pictures can be much easier to obtain in real applications. The results show that by using multiple cross-section images along or perpendicular to the preferred directionality of the fillers, 2D CNNs can provide quite accurate prediction. Such a result is demonstrated with isotropic particle filled composites and anisotropic stochastic complex composites. In addition, we also discuss how to select representative cross-section images. It is found that the average over multiple images and the use of large-size images can reduce the uncertainty and increase the prediction accuracy. Besides, since cross-section images along the heat flow direction can distinguish between serial structures and parallel structures, they are more representative than cross-section images perpendicular to the heat flow direction. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPOSITES |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | THERMAL CONDUCTIVITY |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | DEEP LEARNING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | FINITE ELEMENT ANALYSIS (FEA) |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rong, Q. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wei, H. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huang, X. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bao, H. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1WVtz1VDbP8JrUAM7FFmAa15IuH0pPdKH/view?usp=drive_link">https://drive.google.com/file/d/1WVtz1VDbP8JrUAM7FFmAa15IuH0pPdKH/view?usp=drive_link</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
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| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 09.10.2025 | B-21873 | 09.10.2025 | 09.10.2025 | Documentos solicitados |