4.6 Article

Level of Activity Changes Increases the Fatigue Life of the Porous Magnesium Scaffold, as Observed in Dynamic Immersion Tests, over Time

Journal

SUSTAINABILITY
Volume 15, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/su15010823

Keywords

cycles of failure; dynamic immersion; fatigue; porous magnesium scaffold

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This study investigates the impact of human physiological activity levels on the fatigue life of a porous magnesium scaffold. Dynamic immersion and biomechanical testing were conducted to simulate physiological conditions. Numerical data analysis and computer simulations were used to predict implant failure values. The findings suggest that activity levels play a crucial role in implant fatigue rate.
In the present study, the effects of human physiological activity levels on the fatigue life of a porous magnesium scaffold have been investigated. First, the dynamic immersion and biomechanical testing are carried out on a porous magnesium scaffold to simulate the physiological conditions. Then, a numerical data analysis and computer simulations predict the implant failure values. A 3D CAD bone scaffold model was used to predict the implant fatigue, based on the micro-tomographic images. This study uses a simulation of solid mechanics and fatigue, based on daily physiological activities, which include walking, running, and climbing stairs, with strains reaching 1000-3500 mu m/mm. The porous magnesium scaffold with a porosity of 41% was put through immersion tests for 24, 48, and 72 h in a typical simulated body fluid. Longer immersion times resulted in increased fatigue, with cycles of failure (Nf) observed to decrease from 4.508 x 10(22) to 2.286 x 10(11) (1.9 x 10(11) fold decrease) after 72 hours of immersion with a loading rate of 1000 mu m/mm. Activities played an essential role in the rate of implant fatigue, such as demonstrated by the 1.1 x 1(05) fold increase in the Nf of walking versus stair climbing at 7.603 x 10(11) versus 6.858 x 1(05), respectively. The dynamic immersion tests could establish data on activity levels when an implant fails over time. This information could provide a basis for more robust future implant designs.

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