.png){kind=icon}
返回
Advancing programmable metamaterials through machine learning-driven buckling strength optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cossms.2024.101161.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Metamaterials are specially engineered materials distinguished by their unique properties not typically seen in naturally occurring materials. However, the challenge lies in achieving lightweight yet mechanically rigid architectures, as these properties are sometimes conflicting. For example, buckling strength is a critical property that needs to be enhanced since buckling can cause catastrophic failure of the lightweight metamaterials. In this study, we introduce a generative machine learning based approach to determine the superior geometries of metamaterials to maximize their buckling strength without compromising their elastic modulus. Our results, driven by machine learning based design, remarkably enhanced buckling strength (over 90 %) compared to conventional metamaterial designs. The simulation results are validated by a series of experimental testing and the mechanism of the high buckling strength is elucidated by correlating stress field with the metamaterial geometry. Our results provide insights into the interplay between shape and buckling strength, unveiling promising avenues for designing efficient metamaterials in future applications.
Keyword:
Metamaterials
Machine learning
Optimization
Buckling strength
Additive manufacturing
期刊
IF:
13.4
论文数:
1.3K
被引数:
5.5K
机构
引用论文
On buckling behaviors of a typical bending-dominated periodic lattice关于典型的弯曲主导周期晶格的屈曲行为.png)
COMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
分享收藏Explaining individual predictions when features are dependent: More accurate approximations to Shapley values当特征依赖时解释单个预测: 更准确地逼近Shapley值
OAAI
OAAI分享收藏Generative machine learning algorithm for lattice structures with superior mechanical propertiesMATERIALS HORIZONS
MATERIALS HORIZONSIF10.7
PREAI
分享收藏Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model使用基于注意力的扩散模型，基于二级结构约束的从头蛋白质的生成设计CHEM
OAAI
CHEMIF19.6
OAAI分享收藏Mechanical properties of open-cell metallic biomaterials manufactured using additive manufacturing使用增材制造制造的开孔金属生物材料的机械性能MATERIALS & DESIGN
MATERIALS & DESIGNIF7.9
PREAI
分享收藏
查看更多内容