Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption
- Publisher:
- PERGAMON-ELSEVIER SCIENCE LTD
- Publication Type:
- Journal Article
- Citation:
- International Journal of Mechanical Sciences, 2023, 246
- Issue Date:
- 2023-05-15
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Additively manufactured materials and structures A state-of-the-art review on their mechanical characteristics and energy absorption.pdf | Accepted version | 9.04 MB |
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Lightweight materials and structures have been extensively studied for a wide range of applications in design and manufacturing of more environment-friendly and more sustainable products, such as less materials and lower energy consumption, while maintaining proper mechanical and energy absorption characteristics. Additive manufacturing (AM) or 3D printing techniques offer more freedom to realize some new designs of novel lightweight materials and structures in an efficient way. However, the rational design for desired mechanical properties of these materials and structures remains a demanding topic. This paper provides a comprehensive review on the recent advances in additively manufactured materials and structures as well as their mechanical properties with an emphasis on energy absorption applications. First, the additive manufacturing techniques used for fabricating various materials and structures are briefly reviewed. Then, a variety of lightweight AM materials and structures are discussed, together with their mechanical properties and energy-absorption characteristics. Next, the AM-induced defects, their impacts on mechanical properties and energy absorption, as well as the methods for minimizing the effects are discussed. After that, numerical modeling approaches for AM materials and structures are outlined. Furthermore, design optimization techniques are reviewed, including parametric optimization, topology optimization, and nondeterministic optimization with fabrication-induced uncertainties. Notably, data-driven and machine learning-based techniques exhibit compelling potential in design for additive manufacturing, process-property relations, and in-situ monitoring. Finally, significant challenges and future directions in this area are highlighted. This review is anticipated to provide a deep understanding of the state-of-the-art additively manufactured materials and structures, aiming to improve the future design for desired mechanical properties and energy absorption.
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