| Description |
1 online resource (480 pages) |
| Series |
Woodhead Publishing series in composites science and engineering |
|
Woodhead Publishing series in composites science and engineering.
|
| Summary |
Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures presents a state-of-the-art review of the latest advances and cutting-edge technologies in this important research field. Sections provide an introduction to functionally graded porous structures and detail the effects of graded porosities on bending, buckling, and vibration behaviors within the framework of Timoshenko beam theory and first-order shear deformable plate theory. Other sections cover the usage of machine learning techniques for smart structural analysis of porous components as an evolution from traditional engineering and methods and focus on additive manufacturing of structures with graded porosities for end-user applications. The book follows a clear path from design and analysis to fabrication and applications. Readers will find extensive knowledge and examples of functionally graded porous structures that are suitable for innovative research and market needs, with applications relevant to a diverse range of industrial fields, including mechanical, structural, aerospace, energy, and biomedical engineering |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Composite materials -- Mechanical properties
|
|
Artificial intelligence -- Engineering applications.
|
|
Machine learning -- Industrial applications
|
|
Additive manufacturing.
|
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Additive manufacturing.
|
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Artificial intelligence -- Engineering applications.
|
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Composite materials -- Mechanical properties.
|
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Machine learning -- Industrial applications.
|
| Form |
Electronic book
|
| Author |
Yang, Jie, editor
|
|
Chen, Da, editor.
|
|
Gao, Kang, editor.
|
| ISBN |
9780443154263 |
|
0443154260 |
|
