The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Reversible energy absorbing meta-sandwiches by FDM 4D printing
Open accessAbstract
The aim of this paper is to introduce dual-material auxetic meta-sandwiches by four-dimensional (4D) printing technology for reversible energy absorption applications. The meta-sandwiches are developed based on an understanding of hyper-elastic feature of soft polymers and elasto-plastic behaviors of shape memory polymers and cold programming derived from theory and experiments. Dual-material lattice-based meta-structures with different combinations of soft and hard components are fabricated by 4D printing fused deposition modelling technology. The feasibility and performance of reversible dual-material meta-structures are assessed experimentally and numerically. Computational models for the meta-structures are developed and verified by the experiments. Research trials show that the dual-material auxetic designs are capable of generating a range of non-linear stiffness as per the requirement of energy absorbing applications. It is found that the meta-structures with hyper-elastic and/or elasto-plastic features dissipate energy and exhibit mechanical hysteresis characterized by non-coincident compressive loading-unloading curves. Mechanical hysteresis can be achieved by leveraging elasto-plasticity and snap-through-like mechanical instability through compression. Experiments also reveal that the mechanically induced plastic deformation and dissipation processes are fully reversible by simply heating. The material-structural model, concepts and results provided in this paper are expected to be instrumental towards 4D printing tunable meta-sandwiches for reversible energy absorption applications.
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Cites
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Cited by
Kafle, A., Luis, E., Silwal, R., Pan, H. M., Shrestha, P. L., & Bastola, A. K. (2021) · Polymers · doi:10.3390/polym13183101 · Cited by 587
Khalid, M. Y., Arif, Z. U., Noroozi, R., Zolfagharian, A., & Bodaghi, M. (2022) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.07.035 · Cited by 336
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Metadata
- Authors
- Bodaghi, M., Serjouei, A., Zolfagharian, A., Fotouhi, M., Rahman, H., & Durand, D.
- Venue
- International Journal of Mechanical Sciences
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 173
- Pages
- 105451
- ISSN
- 0020-7403 (ISSN-L)
- OpenAlex
- W3000195753
- Cited by
- 268
- References
- 17
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.