Aulig, Nikola:
Generic Topology Optimization Based on Local State Features.
[Online-Edition: http://tuprints.ulb.tu-darmstadt.de/6861]
Fortschritt-Berichte VDI : Reihe 20, 468.
VDI Verlag, Düsseldorf
ISBN 978-3-18-346820-1
[Book]
, (2017)
Abstract
The automatic creation of optimal concepts for mechanical structures in the computer-aided design process has become an important area of research. Continuum topology optimization methods determine the distribution of material within a pre-defined design space and, thus, not only the shape, but also the fundamental geometric layout of a structure. For this task, the majority of the existing, numerical optimization methods requires mathematical gradient information. However, when addressing optimization problems that involve highly non-linear or black-box simulations, it can be difficult to obtain satisfactory results or gradient information at all. In order to provide design concepts also for these types of problems, this thesis presents a generic topology optimization approach. The novel method realizes a self-contained learning component that utilizes physical simulation data to generate a search direction. Based on a continuous problem formulation, every design variable is improved iteratively by a learned update-signal. The individual update-signals are computed from local state features and substitute sensitivities of the design variables. Evolutionary optimization or supervised learning adapt the model parameters for determination of the update-signals to the chosen optimization goal. In empirical studies, the novel method reproduces reference structures with minimum compliance. When applied to a practical problem from the challenging domain of vehicle crashworthiness optimization, specifically the minimization of intrusion, it provides superior design concepts when compared to a frequently applied heuristic method. The results confirm that the proposed method is capable to yield innovative solutions to so far unsolved topology optimization problems.
| Item Type: | Book | ||||
|---|---|---|---|---|---|
| Erschienen: | 2017 | ||||
| Creators: | Aulig, Nikola | ||||
| Title: | Generic Topology Optimization Based on Local State Features | ||||
| Language: | German | ||||
| Abstract: | The automatic creation of optimal concepts for mechanical structures in the computer-aided design process has become an important area of research. Continuum topology optimization methods determine the distribution of material within a pre-defined design space and, thus, not only the shape, but also the fundamental geometric layout of a structure. For this task, the majority of the existing, numerical optimization methods requires mathematical gradient information. However, when addressing optimization problems that involve highly non-linear or black-box simulations, it can be difficult to obtain satisfactory results or gradient information at all. In order to provide design concepts also for these types of problems, this thesis presents a generic topology optimization approach. The novel method realizes a self-contained learning component that utilizes physical simulation data to generate a search direction. Based on a continuous problem formulation, every design variable is improved iteratively by a learned update-signal. The individual update-signals are computed from local state features and substitute sensitivities of the design variables. Evolutionary optimization or supervised learning adapt the model parameters for determination of the update-signals to the chosen optimization goal. In empirical studies, the novel method reproduces reference structures with minimum compliance. When applied to a practical problem from the challenging domain of vehicle crashworthiness optimization, specifically the minimization of intrusion, it provides superior design concepts when compared to a frequently applied heuristic method. The results confirm that the proposed method is capable to yield innovative solutions to so far unsolved topology optimization problems. |
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| Series Name: | Fortschritt-Berichte VDI : Reihe 20 | ||||
| Volume: | 468 | ||||
| Place of Publication: | Düsseldorf | ||||
| Publisher: | VDI Verlag | ||||
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics |
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| Date Deposited: | 29 Oct 2017 20:55 | ||||
| Official URL: | http://tuprints.ulb.tu-darmstadt.de/6861 | ||||
| URN: | urn:nbn:de:tuda-tuprints-68616 | ||||
| Additional Information: | Fortschr.-Ber. VDI Reihe 20 Nr. 468, ISSN 0178-9473 |
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| Referees: | Adamy, Prof. Dr. Jürgen and Sendhoff, Prof. Dr. Bernhard | ||||
| Refereed / Verteidigung / mdl. Prüfung: | 4 May 2017 | ||||
| Alternative Abstract: |
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