Academic Journal
Reinforcement learning based agents for improving layouts of automotive crash structures.
| Τίτλος: | Reinforcement learning based agents for improving layouts of automotive crash structures. |
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| Συγγραφείς: | Trilling, Jens, Schumacher, Axel, Zhou, Ming |
| Πηγή: | Applied Intelligence; Jan2024, Vol. 54 Issue 2, p1751-1769, 19p |
| Θεματικοί όροι: | Reinforcement learning, Structural frame models, Vehicle models |
| Περίληψη: | The topology optimization of crash structures in automotive and aeronautical applications is challenging. Purely mathematical methods struggle due to the complexity of determining the sensitivities of the relevant objective functions and restrictions according to the design variables. For this reason, the Graph- and Heuristic-based Topology optimization (GHT) was developed, which controls the optimization process with rules derived from expert knowledge. In order to extend the collected expert rules, the use of reinforcement learning (RL) agents for deriving a new optimization rule is proposed in this paper. This heuristic is designed in such a way that it can be applied to many different models and load cases. An environment is introduced in which agents interact with a randomized graph to improve cells of the graph by inserting edges. The graph is derived from a structural frame model. Cells represent localized parts of the graph and delineate the areas where agents can insert edges. A newly developed shape preservation metric is presented to evaluate the performance of topology changes made by agents. This metric evaluates how much a cell has deformed by comparing its shape in the deformed and undeformed state. The training process of the agents is described and their performance is evaluated in the training environment. It is shown how the agents and the environment can be integrated as a new heuristic into the GHT. An optimization of the frame model and a vehicle rocker model with the enhanced GHT is carried out to assess its performance in practical optimizations. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10489-024-05276-6 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Reinforcement learning based agents for improving layouts of automotive crash structures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Trilling%2C+Jens%22">Trilling, Jens</searchLink><br /><searchLink fieldCode="AR" term="%22Schumacher%2C+Axel%22">Schumacher, Axel</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ming%22">Zhou, Ming</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Intelligence; Jan2024, Vol. 54 Issue 2, p1751-1769, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+frame+models%22">Structural frame models</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicle+models%22">Vehicle models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The topology optimization of crash structures in automotive and aeronautical applications is challenging. Purely mathematical methods struggle due to the complexity of determining the sensitivities of the relevant objective functions and restrictions according to the design variables. For this reason, the Graph- and Heuristic-based Topology optimization (GHT) was developed, which controls the optimization process with rules derived from expert knowledge. In order to extend the collected expert rules, the use of reinforcement learning (RL) agents for deriving a new optimization rule is proposed in this paper. This heuristic is designed in such a way that it can be applied to many different models and load cases. An environment is introduced in which agents interact with a randomized graph to improve cells of the graph by inserting edges. The graph is derived from a structural frame model. Cells represent localized parts of the graph and delineate the areas where agents can insert edges. A newly developed shape preservation metric is presented to evaluate the performance of topology changes made by agents. This metric evaluates how much a cell has deformed by comparing its shape in the deformed and undeformed state. The training process of the agents is described and their performance is evaluated in the training environment. It is shown how the agents and the environment can be integrated as a new heuristic into the GHT. An optimization of the frame model and a vehicle rocker model with the enhanced GHT is carried out to assess its performance in practical optimizations. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-024-05276-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1751 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Structural frame models Type: general – SubjectFull: Vehicle models Type: general Titles: – TitleFull: Reinforcement learning based agents for improving layouts of automotive crash structures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Trilling, Jens – PersonEntity: Name: NameFull: Schumacher, Axel – PersonEntity: Name: NameFull: Zhou, Ming IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 54 – Type: issue Value: 2 Titles: – TitleFull: Applied Intelligence Type: main |
| ResultId | 1 |