Data-driven optimization design of a novel combined metamaterial for robotic grinding vibration control
In order to suppress low-frequency vibration induced by low structural stiffness and rapid point-to-point motion in robotic grinding, a novel combined mechanical metamaterial is proposed. Two cosine beams are integrated into a previously developed lattice and are aligned along the load direction, en...
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| Vydáno v: | European journal of mechanics, A, Solids Ročník 116; s. 105904 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Masson SAS
01.03.2026
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| Témata: | |
| ISSN: | 0997-7538 |
| On-line přístup: | Získat plný text |
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| Abstract | In order to suppress low-frequency vibration induced by low structural stiffness and rapid point-to-point motion in robotic grinding, a novel combined mechanical metamaterial is proposed. Two cosine beams are integrated into a previously developed lattice and are aligned along the load direction, enabling near-zero load conditions within geometric constraints. A data-driven bi-objective optimization algorithm is applied, utilizing adaptive constraints to maximize effective Young's modulus and to tailor the band gap. A two-output machine learning model is trained to separately predict the upper and lower boundaries of the band gap. To address the discontinuity caused by band gap jumps, the model incorporates a jump feature and a target-weighted loss, thereby achieving stable and accurate predictions. Compared with the original lattice, the optimized metamaterial is shown to exhibit a 2.5-fold increase in effective Young's modulus (from 150.34 Pa to 374.38 Pa), a reduction in the lower bound of the first band gap (from 18.33 Hz to 9.47 Hz), and an expansion of both band gap range and count. Experimental validation is conducted, demonstrating an approximately double reduction in minimum vibration transmission values, thereby confirming the effectiveness of the design. The provided efficient and accurate data-driven framework is potentially applicable to the simultaneous optimization of two properties in metamaterials.
•Cosine beams aligned with load direction in the combined metamaterial achieve 0 load.•A data-driven bi-objective adaptive-constrained optimization framework is developed.•The framework simultaneously optimizes effective Young's modulus and band-gap.•Effective Young's modulus increased 2.5 times; band-gap lower bound halved.•Robotic grinding experiments confirm doubled reduction in vibration transmission. |
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| AbstractList | In order to suppress low-frequency vibration induced by low structural stiffness and rapid point-to-point motion in robotic grinding, a novel combined mechanical metamaterial is proposed. Two cosine beams are integrated into a previously developed lattice and are aligned along the load direction, enabling near-zero load conditions within geometric constraints. A data-driven bi-objective optimization algorithm is applied, utilizing adaptive constraints to maximize effective Young's modulus and to tailor the band gap. A two-output machine learning model is trained to separately predict the upper and lower boundaries of the band gap. To address the discontinuity caused by band gap jumps, the model incorporates a jump feature and a target-weighted loss, thereby achieving stable and accurate predictions. Compared with the original lattice, the optimized metamaterial is shown to exhibit a 2.5-fold increase in effective Young's modulus (from 150.34 Pa to 374.38 Pa), a reduction in the lower bound of the first band gap (from 18.33 Hz to 9.47 Hz), and an expansion of both band gap range and count. Experimental validation is conducted, demonstrating an approximately double reduction in minimum vibration transmission values, thereby confirming the effectiveness of the design. The provided efficient and accurate data-driven framework is potentially applicable to the simultaneous optimization of two properties in metamaterials.
•Cosine beams aligned with load direction in the combined metamaterial achieve 0 load.•A data-driven bi-objective adaptive-constrained optimization framework is developed.•The framework simultaneously optimizes effective Young's modulus and band-gap.•Effective Young's modulus increased 2.5 times; band-gap lower bound halved.•Robotic grinding experiments confirm doubled reduction in vibration transmission. |
| ArticleNumber | 105904 |
| Author | Cui, Ziyao Müller, Wolfgang H. Guo, Jingkun Jin, Yutong Yang, Hua Dai, Shijie |
| Author_xml | – sequence: 1 givenname: Yutong surname: Jin fullname: Jin, Yutong organization: School of Mechanical Engineering, Hebei University of Technology, Tianjin, China – sequence: 2 givenname: Hua surname: Yang fullname: Yang, Hua organization: School of Mechanical Engineering, Hebei University of Technology, Tianjin, China – sequence: 3 givenname: Wolfgang H. surname: Müller fullname: Müller, Wolfgang H. organization: Institute of Mechanics, Technische Universität Berlin, Berlin, Germany – sequence: 4 givenname: Jingkun surname: Guo fullname: Guo, Jingkun organization: School of Mechanical Engineering, Hebei University of Technology, Tianjin, China – sequence: 5 givenname: Ziyao surname: Cui fullname: Cui, Ziyao organization: Industrial Technology Research Institute, Hebei University of Technology, Tianjin, China – sequence: 6 givenname: Shijie surname: Dai fullname: Dai, Shijie email: dsj@hebut.edu.cn organization: School of Mechanical Engineering, Hebei University of Technology, Tianjin, China |
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| Keywords | Effective Young's modulus Combined metamaterial Band gap Robotic grinding vibration control Data-driven optimization |
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