| Autor: |
Dirk Lehmhus, Arouna Patouossa Mounchili, Adrian Struss, Stefan Bosse |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
MATEC Web of Conferences, Vol 349, p 03001 (2021) |
| ISSN: |
2261-236X |
| DOI: |
10.1051/matecconf/202134903001 |
| Popis: |
Manufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) achieves this aim via iterative, linear-elastic Finite Element (FE) simulations providing element- and part-level strain energy data under a given design load and using it to redistribute predefined material fractions to minimize total strain energy. The result us a part configuration offering maximum stiffness. The present study implements different material redistribution and optimization techniques and compares them in terms of optimization results and performance: Genetic algorithms are matched against simulated annealing, the latter with and without physics-based constraints. Both types employ partial randomization in generating new configurations to avoid settling into local rather than global minima of the objective function. This allows exploring a larger fraction of the full search space than accessed by classic gradient-based algorithms. Evaluation of the objective function depends on FE simulation, a computationally intensive task. Minimizing the required number of simulation runs is the task of the aforementioned constraints. The methodology is validated via a three point bending test scenario. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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