Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
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) ach...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN FR |
| Publicado: |
EDP Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b3f4a657281c4fdd9686eb6b7fc72233 |
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| Sumario: | 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. |
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