基于有限元法和机器学习的参数缓冲格子内:初步计算分析
Zhenghui Lu1, Xin Li1, Dong Sun2
1Faculty of Sports Science, Ningbo University, Ningbo, China; Faculty of Engineering, University of Pannonia, Veszprém, Hungary; Department of Material Science and Technology, Audi Hungaria Faculty of Automotive Engineering, Széchenyi István University, Hungary.
Journal of biomechanics
|April 7, 2025
概括
本研究介绍了一种可控制的参数化格子缓冲内底 (PLI),使用有限元素 (FE) 和机器学习 (ML) 方法. 优化的PLI设计显著降低了足部压力,提高了内缓冲性能.
科学领域:
- 生物力学 生物力学
- 材料科学 材料科学 材料科学
- 计算工程 计算工程 计算工程
背景情况:
- 内底缓冲对于设计至关重要,但缺乏标准化的材料和结构选择方法.
- 增材制造 (AM) 和智能方法提供设计自由,但需要优化指导.
- 目前的方法缺乏一个系统的框架来根据特定需求量身定制内缓冲.
研究的目的:
- 提出一个可控制的参数化的格子缓冲内 (PLI),集成有限元素 (FE) 和机器学习 (ML).
- 建立一个数据驱动的方法,以优化内结构和材料,以增强缓冲.
- 为应对内设计挑战提供系统和高效的解决方案.
主要方法:
- 结合有限元 (FE) 分析与机器学习 (ML) 算法.
- 开发一个参数化的格子结构,具有可调节的参数 (a,b) 和支架厚度 (t).
- 通过计算建模和数据分析优化PLI设计.
主要成果:
- 优化的PLI显示脚部压力降低了高达44.45%.
- 为了获得最大的缓冲效果,确定了最佳参数 (a=2.54,b=3.56,t=3.15) .
- 该方法简化了内结构和材料的选择,提高了缓冲性能.
结论:
- 数据驱动的PLI优化方法显著改善了内缓冲.
- 这种方法为内设计提供了系统和高效的解决方案,减少了对重复物理测试的依赖.
- 这些发现为内结构设计的临床应用提供了基础参考.
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