基于反向传播神经网络的TPMS结构的机械性能预测
Jiayao Li1,2, Ketong Luo1,2, Wen Qi3
1School of Materials and Environment, Guangxi Minzu University, Nanning, China.
Computer methods in biomechanics and biomedical engineering
|January 29, 2024
概括
三次周期性最小表面 (TPMS) 结构显示出对骨组织工程的前景. 一个逆向传播神经网络 (BPNN) 优化了TPMS的机械性能,显著增加了超过100%的收益率强度,用于混合体状腺-钻石结构.
科学领域:
- 生物材料科学 生物材料科学
- 机械工程 机械工程
- 计算机建模 计算建模
背景情况:
- 三重周期性最小表面 (TPMS) 结构具有高孔隙性,相互连接的网络和光滑的表面,非常适合骨组织工程.
- 为所需的机械性能优化TPMS结构参数是复杂的,因为复杂的关系.
研究的目的:
- 开发一个反向传播神经网络 (BPNN) 模型,以预测和优化TPMS结构的机械性能.
- 为了提高TPMS脚手架的机械性能,用于骨组织工程应用.
主要方法:
- 使用反向传播神经网络 (BPNN) 来建模TPMS参数和机械性质之间的关系.
- 使用训练的BPNN模型优化TPMS结构参数.
- 评估了优化结构的机械性能和拓形态.
主要成果:
- BPNN模型显示出高准确度,预测和实验结果之间的相关系数 (R) 为0.955475.
- 经过优化混合化陀螺-钻石结构 (HGDS) 的收益强度增加了102.61% (从3.06 MPa增加到6.20 MPa).
- 优化的模型显示有效轴承面积增加了12.92%,有助于增强收益强度.
结论:
- BPNN是准确预测TPMS结构属性的有效工具,并优化其机械性能.
- 优化的TPMS结构显著提高了机械强度,使它们更适合骨组织工程.
- 导致有效承载面积增加的结构修改是提高TPMS脚手架收益强度的关键.
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