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使用生物灵感的层次梯度设计和混合堆叠机器学习来设计和预测软到硬的过渡

Masoud Shirzad¹, Juhyun Kang², Mahdi Bodaghi³

¹Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan, 48513, Republic of Korea.

Computers in biology and medicine

|September 27, 2025

概括

研究人员开发了模仿肌与骨接口的生物灵感结构. 这些新的设计显著增强了像Young这样的机械性能.

科学领域:

生物材料科学生物材料科学
组织工程是组织工程.
机械工程机械工程

背景情况:

由于机械性能不匹配,制造软硬接口具有挑战性.
自然肌与骨的接口表现出复杂的等级和梯度结构.

研究的目的:

设计和制造生物灵感结构,复制肌与骨的接口.
为了研究体的结构和机械特性之间的关系.
优化生物灵感设计,以提高机械性能.

主要方法:

负波桑比率 (NPR) 架构与正波桑比率 (PPR) 结构的整合.
使用有限元法 (FEM) 进行机械性质预测.
采用混合堆叠机器学习 (HSML) 模型来提高预测准确度.

主要成果:

新的层次梯度设计提高了扬模和故障力高达58.1%.
在小数据集上,HSML模型实现了2%的预测误差,超过了传统的机器学习模型.
模仿自然吸收能源的接口显著提高了机械性能.

结论:

生物启发的等级梯度设计有效地复制复杂的自然界面.
像HSML这样的先进计算方法对于优化生物材料设计至关重要.
这种方法为开发先进的软硬组织接口提供了一个有希望的策略.

关键词:

生物灵感是一种生物灵感.阶层级梯度结构的结构.机器学习机器学习软到硬的元材料.

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