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Updated: Jun 19, 2026

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基于机器学习的纤维状粘合剂的最佳设计

Mohammad Shojaeifard1, Matteo Ferraresso1, Alessandro Lucantonio2

  • 1Mechanical Engineering Department, University of British Columbia, Vancouver, BC V6T1Z4, Canada.

Journal of the Royal Society, Interface
|February 25, 2025
PubMed
概括

我们开发了一个机器学习工具,以优化纤维的合规性,以获得更强的粘附力. 这种方法可以增强生物灵感粘合剂和微型架构材料的设计,以提高抗折断的性能.

关键词:
粘附性 粘附性 粘附性 粘附性生物灵感纤维状粘合剂生物灵感纤维状粘合剂断裂力学 断裂力学 断裂力学设计的反向设计.机器学习是机器学习.优化的优化优化优化.

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Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...