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Multi-Material Topology Optimization Considering Manufacturing Constraints

Journal Article
2020-01-0628
ISSN: 2641-9645, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Multi-Material Topology Optimization Considering Manufacturing Constraints
Sector:
Citation: Shah, V., Kashanian, K., Pamwar, M., Sangha, B. et al., "Multi-Material Topology Optimization Considering Manufacturing Constraints," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(6):3268-3277, 2020, https://doi.org/10.4271/2020-01-0628.
Language: English

Abstract:

The field of topology optimization (TO) has been evolving rapidly, notably due to the emergence of multi-material topology optimization (MMTO) algorithms. These developments follow the establishment of TO tools within industry, which has been accelerated and promoted through the introduction of various manufacturing constraints within algorithms. The integration of manufacturing constraints within MMTO is critical for promoting industry usage and adoption of these new software algorithms, as current usage of MMTO is dissuaded by the typically complex design solutions.
The presented MMTO implementation is an extension of classical single-material topology optimization (SMTO). The TO problem is expanded to consider both material existence and selection, solid isotropic material with penalization (SIMP) is utilized for material interpolation. The method of moving asymptotes (MMA) has been integrated into MMTO as the optimization algorithm as it can handle large-scale problems with many design variables.
A design variable mapping system has been incorporated into MMTO, which determines element groups based on symmetry or extrusion manufacturing constraints. The design variables of the group elements are constrained to equivalent values, resulting in either extruded or symmetric MMTO geometry. Several problems, of varying scale and optimization problem statement are solved using MMTO without manufacturing constraints, MMTO with extrusion, and MMTO with symmetry constraints. The optimized geometry and numeric performance are analyzed to determine the feasibility of the manufacturing constraints in reducing computational time and increasing design manufacturability. These benefits are contrasted against the reduction in structural performance; a consequence of reduced design freedom when enforcing manufacturing constraints.