A Biomimetic Titanium Scaffold with and Without Magnesium Filled for Adjustable Patient-Specific Elastic Modulus.
Authors
Affiliations (5)
Affiliations (5)
- School of Medical Science and Technology IIT Kharagpur, India.
- Department of Mechanical Engineering IIEST, Shibpur, Howrah, India.
- Department of Aerospace Engineering and Applied Mechanics, IIEST, Shibpur, Howrah, India.
- Department of Computer Science, Jadavpur University.
- Department of Metallurgical and Materials Engineering, IIT Kharagpur, India.
Abstract
This study focuses on determining the effective young modulus (stiffness) of various lattice structures for titanium scaffolds filled with magnesium and without magnesium. For specific patient success of the implant is depends on adequate elastic modulus which helps proper osteointegration. The Mg filled portion in the Ti scaffold is expected to dissolve with time as the bone growth through the Ti scaffold porous cavity is started. The proposed method is based on a general numerical homogenization scheme to determine the effective elastic properties of the lattice scaffold at the macroscopic scale. A large numerical campaign has been conducted on 18 geometries. The 3D scaffold is conceived based on the model generated from the Micro CT data of the prepared sample. The effect of the scaffold local features, e.g., the distribution of porosity, presence of scaffold's surface area to the adjacent bone location, strut diameter of implant, on the effective elastic properties is investigated. Results show that both the relative density and the geometrical features of the scaffold strongly affect the equivalent macroscopic elastic behaviour of the lattice. 6 samples are made (three each Mg filled and three without Mg) The compression test was carried out for each type of samples and the displacement obtained from the test results were in close match with the simulated results from finite element analysis. To predict the unknown required stiffness what would be the ratio between Ti scaffold and filled up Mg have been calculated using the data driven AI model.