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Compressive properties and energy absorption of ordered porous aluminum with strengthening structures

  • Moqiu Li , Yu Bai EMAIL logo , Mingming Su and Hai Hao ORCID logo EMAIL logo
Published/Copyright: January 30, 2025
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Abstract

In this paper, Z-strengthening structure (strengthening the Z-axis struts) and Graded-Z-strengthening structure (strengthening the Z-axis struts and node by gradient) based on cubic structure were investigated. Specimens were fabricated by indirect additive manufacturing. The compressive properties and energy absorption were characterized by axial quasi-static compression tests. The results showed that Z-strengthening enhances the compressive properties and energy absorption capacity of the original cubic structure. As for Graded-Z-strengthening, the stiffness of the structures was further increased. Moreover, different strengthening structures change the failure mode of the structures, which has a significant effect on mechanical properties and energy absorption capacities.


Corresponding authors: Yu Bai and Hai Hao, Key Laboratory of Solidification Control and Digital Preparation Technology (Liaoning Province), School of Materials Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, P.R. China, E-mail: (Y. Bai), (H. Hao)

  1. Research ethics: ALL procedures performed in studies were in accordance with the ethical standards.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: Moqiu Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation. Yu Bai: Writing – review & editing, Supervision, Funding acquisition, Formal analysis. Mingming Su: Methodology, Investigation. Hai Hao: Writing – review & editing, Supervision, Funding acquisition, Formal analysis.

  4. Use of Large Language Models, AI and Machine Learning Tools: This paper did not use Large Language Models, AI and Machine Learning Tools.

  5. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

  6. Research funding: National Natural Science Foundation of China [52171030], the Key Basic Research Project of the Basic Strengthen Program [2021-JCJQ-ZD-043-00] and the National Key Research and Development Program of China [2018YFA0702903].

  7. Data availability: Data will be made available on request.

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Received: 2021-05-05
Accepted: 2024-08-02
Published Online: 2025-01-30
Published in Print: 2025-01-29

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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