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Title:: Method on the prediction of bone micro-level mechanical parameter ?combining apparent compressive experiment and finite element analysis

作者:: 范若寻1; 2△; 刘杰1; 甘树坤1; 张敏2; 1.吉林化工学院汽车工程学院，吉林 132102；2. 吉林大学机械科学与工程学院，长春 130025

Author(s):: FAN Ruoxun1; 2; LIU Jie1; GAN Shukun1; ZHANG Min2; 1. Department of Automotive Engineering, Jilin Institute of Chemical Technology, Jilin 132102，China;2. Department of Engineering Mechanics, Jilin University, Changchun 130025，China

Keywords:: Apparent mechanical properties; Micro-level mechanical properties; Compressive experiment; Finite element analysis; Back-calculate; Cortical bone

摘要:: 骨表观力学性能参数常被用来评定骨质量，而微观力学性能参数由于难以测得尚未被广泛应用。为从微观水平评判骨质量，本研究提出一种骨微观力学性能参数的预测方法。该方法以大鼠股骨皮质骨为研究对象，预测其在微观水平的失效应变。首先依据扫描影像建立大鼠股骨皮质骨有限元模型，然后以表观压缩实验为研究依据，模仿实验条件，模拟皮质骨有限元模型在压缩载荷作用下的断裂过程，并通过与实验所测表观应力-应变曲线进行对比、反演，预测出大鼠股骨皮质骨在微观水平的失效应变。分析结果显示四只7月龄大鼠股骨皮质骨的微观失效应变数值处于4.53%~4.75%之间。经验证，本方法能够准确预测出骨结构在微观水平的力学性能参数。

Abstract:: Bone apparent mechanical parameters are used to evaluate bone quality; however, bone micro-level mechanical parameters cannot serve as evaluation criteria because of the difficulty in measurement. In order to investigate bone quality at micro-level, a method in predicting bone micro-level mechanical parameter was provided in this study. Rat femoral cortical bone was used as research example to predict its micro-level failure strain. The rat femoral cortical bone finite element models were primarily established based on the computed tomography; then, finite element analyses were performed to simulate the failure processes under compressive load, which simulated the boundary and loading conditions of the apparent compressive experiment; meanwhile, micro-level failure strains were back-calculated by fitting the experimental apparent stress-strain curves. The analysis results showed that the micro-level failure strains in the four rats at the age of 7 months were in the range of 4.53%~4.75%. This method can predict bone micro-level mechanical parameter in an accurate way based on the verification with experiments.