Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909589808611328 |
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| author | Humer, Alexander Platzer, Sebastian |
| author_facet | Humer, Alexander Platzer, Sebastian |
| contents | Compliant mechanisms achieve motion through elastic deformation. In this work, we address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes while approximating the behavior of an ideal revolute joint. To capture the competing demands of kinematic fidelity, rotational stiffness, and resistance to parasitic motion, we formulate a multi-objective optimization problem based on kinetostatic performance measures. A hybrid design strategy is employed: an efficient beam-based structural model enables extensive exploration of a high-dimensional design space using evolutionary algorithms, followed by fine-tuning with high-fidelity three-dimensional finite element analysis. The resulting Pareto-optimal designs reveal diverse geometric configurations and performance trade-offs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization Humer, Alexander Platzer, Sebastian Numerical Analysis Neural and Evolutionary Computing Compliant mechanisms achieve motion through elastic deformation. In this work, we address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes while approximating the behavior of an ideal revolute joint. To capture the competing demands of kinematic fidelity, rotational stiffness, and resistance to parasitic motion, we formulate a multi-objective optimization problem based on kinetostatic performance measures. A hybrid design strategy is employed: an efficient beam-based structural model enables extensive exploration of a high-dimensional design space using evolutionary algorithms, followed by fine-tuning with high-fidelity three-dimensional finite element analysis. The resulting Pareto-optimal designs reveal diverse geometric configurations and performance trade-offs. |
| title | Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization |
| topic | Numerical Analysis Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.16451 |