Optimally‐Tailored Spinodal Architected Materials for Multiscale Design and Manufacturing
Source abstract
Abstract
Abstract Spinodal architected materials with tunable anisotropy unify optimal design and manufacturing of multiscale structures. By locally varying the spinodal class, orientation, and porosity during topology optimization, a large portion of the anisotropic material space is exploited such that material is efficiently placed along principal stress trajectories at the microscale. Additionally, the bicontinuous, nonperiodic, unstructured, and stochastic nature of spinodal architected materials promotes mechanical and biological functions not explicitly considered during optimization (e.g., insensitivity to imperfections, fluid transport conduits). Furthermore, in contrast to laminated composites or periodic, structured architected materials (e.g., lattices), the functional representation of spinodal architected materials leads to multiscale, optimized designs with clear physical interpretation that can be manufactured directly, without special treatment at spinodal transitions. Physical models of the optimized, spinodal‐embedded parts are manufactured using a scalable, voxel‐based strategy to communicate with a masked stereolithography (m‐SLA) 3D printer.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Cellular and Composite Structures · Advanced Materials and Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive NW Atlanta GA 30332 USA
George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA
Department of Civil and Environmental Engineering Princeton University Princeton NJ 08544 USA
Princeton Institute for the Science and Technology of Materials Princeton University Princeton NJ 08544 USA
Funding
National Science Foundation
References (32)
Show source-provided references
1. 10.1016/j.pmatsci.2007.06.00110.1016/j.pmatsci.2007.06.001 ↗
2. Gibson L. J. · 2010 · 83
3. 10.1016/j.actbio.2009.06.03310.1016/j.actbio.2009.06.033 ↗
4. 10.1002/ar.109219010410.1002/ar.1092190104 ↗
5. 10.1002/ar.a.1011910.1002/ar.a.10119 ↗
6. 10.1038/s41524-020-0341-610.1038/s41524-020-0341-6 ↗
7. 10.1007/s10853-010-4567-410.1007/s10853-010-4567-4 ↗
8. 10.1002/jbmr.9210.1002/jbmr.92 ↗
9. 10.1073/pnas.191681711710.1073/pnas.1916817117 ↗
10. 10.1002/smll.20190383410.1002/smll.201903834 ↗
11. 10.1016/j.jmps.2019.01.00210.1016/j.jmps.2019.01.002 ↗
12. 10.1063/1.169573110.1063/1.1695731 ↗
13. 10.1016/j.actamat.2018.01.00510.1016/j.actamat.2018.01.005 ↗
14. 10.1016/0045-7825(88)90086-210.1016/0045-7825(88)90086-2 ↗
15. 10.1137/07068890010.1137/070688900 ↗
16. 10.1016/j.camwa.2018.08.00710.1016/j.camwa.2018.08.007 ↗
17. 10.1002/nme.557510.1002/nme.5575 ↗
18. 10.1007/s00158-002-0209-z10.1007/s00158-002-0209-z ↗
19. 10.1016/j.compstruc.2007.04.03010.1016/j.compstruc.2007.04.030 ↗
20. 10.1016/j.cma.2021.11389410.1016/j.cma.2021.113894 ↗
21. 10.1080/1478644040946322910.1080/14786440409463229 ↗
22. 10.1073/pnas.100120810710.1073/pnas.1001208107 ↗
23. 10.1007/s11517-015-1418-010.1007/s11517-015-1418-0 ↗
24. 10.1007/s00158-015-1274-410.1007/s00158-015-1274-4 ↗
25. 10.1016/j.biomaterials.2005.02.00210.1016/j.biomaterials.2005.02.002 ↗
26. 10.1016/j.matdes.2021.10983810.1016/j.matdes.2021.109838 ↗
27. Bensoussan A. · 1978
28. 10.1126/sciadv.abf483810.1126/sciadv.abf4838 ↗
29. 10.1098/rspa.2018.053510.1098/rspa.2018.0535 ↗
30. G.Electric GE jet engine bracket challenge https://grabcad.com/challenges/ge-jet-engine-bracket-challenge(accessed: March2021).
31. Vranceanu M. · J. Optoelectron. Adv. Mater. · 2012 · 14 · 671
32. 10.1038/s41368-020-0073-y10.1038/s41368-020-0073-y ↗