Numerical Homogenization of Bone Microstructure

Author(s):  
Nikola Kosturski ◽  
Svetozar Margenov
Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3786
Author(s):  
Tomasz Garbowski ◽  
Anna Knitter-Piątkowska ◽  
Damian Mrówczyński

The corrugated board packaging industry is increasingly using advanced numerical tools to design and estimate the load capacity of its products. This is why numerical analyses are becoming a common standard in this branch of manufacturing. Such trends cause either the use of advanced computational models that take into account the full 3D geometry of the flat and wavy layers of corrugated board, or the use of homogenization techniques to simplify the numerical model. The article presents theoretical considerations that extend the numerical homogenization technique already presented in our previous work. The proposed here homogenization procedure also takes into account the creasing and/or perforation of corrugated board (i.e., processes that undoubtedly weaken the stiffness and strength of the corrugated board locally). However, it is not always easy to estimate how exactly these processes affect the bending or torsional stiffness. What is known for sure is that the degradation of stiffness depends, among other things, on the type of cut, its shape, the depth of creasing as well as their position or direction in relation to the corrugation direction. The method proposed here can be successfully applied to model smeared degradation in a finite element or to define degraded interface stiffnesses on a crease line or a perforation line.


Life Sciences ◽  
2021 ◽  
pp. 119450
Author(s):  
Fernanda Batista de Souza ◽  
Rômulo Dias Novaes ◽  
Cynthia Fernandes Ferreira Santos ◽  
Franciele Angelo de Deus ◽  
Felipe Couto Santos ◽  
...  

2011 ◽  
Vol 88 (6) ◽  
pp. 455-463 ◽  
Author(s):  
Chwan-Li Shen ◽  
Jay J. Cao ◽  
Raul Y. Dagda ◽  
Thomas E. Tenner ◽  
Ming-Chien Chyu ◽  
...  

2010 ◽  
Vol 128 (5) ◽  
pp. 3181-3189 ◽  
Author(s):  
Katsunori Mizuno ◽  
Hiroki Somiya ◽  
Tomohiro Kubo ◽  
Mami Matsukawa ◽  
Takahiko Otani ◽  
...  

2012 ◽  
Vol 27 (3) ◽  
pp. 637-644 ◽  
Author(s):  
Kristy M Nicks ◽  
Shreyasee Amin ◽  
Elizabeth J Atkinson ◽  
B Lawrence Riggs ◽  
L Joseph Melton ◽  
...  

2021 ◽  
Author(s):  
Wanyu Li ◽  
Jun Xu ◽  
Shunan Zhang ◽  
Han Guo ◽  
Jianqi Sun ◽  
...  

Abstract Background: As the gold standard for clinical osteoporosis diagnosis, bone mineral density has significant limitations in bone strength assessment and fracture risk prediction. The purpose of this study is to explore a new osteoporotic bone quality evaluation criteria from both diagnosis site selection and bone strength prediction. Methods: Ovariectomized rats with different intensity swimming therapy were investigated in this study. The lumbar vertebrae and femurs of all the rats were scanned by synchrotron radiation computed tomography. Bone microstructure analysis and finite element analysis were combined to obtain bone microstructure parameters and estimated bone strength. And the sensitivity of different skeletal sites to therapy was explored. An elastic network regression model was established to predict bone strength by integrating additional bone microstructure characteristics besides bone mass.Results: Histomorphometry analysis showed that swimming therapy could reduce the risk of osteoporosis of lumbar vertebrae and femur and suggested that the femur might be a more suitable site for osteoporosis diagnosis and efficacy evaluation than the lumbar vertebrae. The average coefficient of determination and average root mean squared error of our predictive model were 0.774 and 0.110. Bland-Altman analysis showed that our model could be a good alternative to the finite element method. Conclusions: The present study developed a machine learning model for prediction of bone strength of osteoporosis model based on synchrotron x-ray imaging and demonstrated that different skeletal sites had different sensitivity to therapy, which is of great significance for the early diagnosis of osteoporosis, the prevention of fractures and the monitoring of therapy.


2021 ◽  
Author(s):  
Ellianna H Zack ◽  
Stephanie M Smith ◽  
Kenneth D Angielczyk

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