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Topology optimization based on reduction methods with applications to multiscale design and additive manufacturing
- Source :
- Frontiers of Mechanical Engineering. 15:151-165
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Advanced manufacturing processes such as additive manufacturing offer now the capability to control material placement at unprecedented length scales and thereby dramatically open up the design space. This includes the considerations of new component topologies as well as the architecture of material within a topology offering new paths to creating lighter and more efficient structures. Topology optimization is an ideal tool for navigating this multiscale design problem and leveraging the capabilities of advanced manufacturing technologies. However, the resulting design problem is computationally challenging as very fine discretizations are needed to capture all micro-structural details. In this paper, a method based on reduction techniques is proposed to perform efficiently topology optimization at multiple scales. This method solves the design problem without length scale separation, i.e., without iterating between the two scales. Ergo, connectivity between space-varying micro-structures is naturally ensured. Several design problems for various types of micro-structural periodicity are performed to illustrate the method, including applications to infill patterns in additive manufacturing.
- Subjects :
- Length scale
Ideal (set theory)
Computer science
Mechanical Engineering
Distributed computing
Topology optimization
Topology (electrical circuits)
02 engineering and technology
021001 nanoscience & nanotechnology
Network topology
Reduction (complexity)
020303 mechanical engineering & transports
0203 mechanical engineering
Component (UML)
Advanced manufacturing
0210 nano-technology
Subjects
Details
- ISSN :
- 20950241 and 20950233
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Frontiers of Mechanical Engineering
- Accession number :
- edsair.doi...........930f24917b850d3dd49b7103fbaf1ecf
- Full Text :
- https://doi.org/10.1007/s11465-019-0564-8