The aim of this study is to create a FEA model to make a relative comparison between two implant tray materials (Co-Cr-Mo and Ti-Al) at the tibia-implant interface under the constant loading condition with the help of patient-specific bone microstructure using a representative volume element (RVE). Using the study, Was able to accurately evaluate designs under different conditions leading to more tailored, patient-specific implants. Using numerical modeling, it was possible to improve product performance by comparing various design options. Able to reduce the number of material testing and lead time reduction.

1. Engineered to Cure – Patient
Specific Tibial Implant Design using
Micro-Macro FEA Framework
www.VIAS3D.com
USA Canada India Mexico
May 2023
Dr. Arindam Chakraborty, CTO – Consulting Services
achakraborty@vias3d.com
Dr. Srikanth Srigiriraju​, Lead Consultant – Consulting Services​
ssrigiriraju@vias3d.com
Georgiy Makedonov, Simulation Engineer – Consulting Services
gmakedonov@vias3d.com

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6. 6
Engineered to Cure – Patient Specific Tibial Implant Design
using Micro-Macro FEA Framework

7. Introduction
Background
• Using simulation for knee implant design study
• The use of patient specific-specific bone geometry via µCT
• Digital evidence in the form of virtual patients can be used
• Full digital access to all relevant information enabling to make rapid, science-based, informed
decisions.
Challenges:
• Characterization of physical and mechanical properties of multiphase materials like cancellous and
cortical bones
• Modeling efficiency and accuracy

8. Objective
The aim of this study is to create a FEA model to make a relative comparison between two
implant tray materials (Co-Cr-Mo and Ti-Al) at the tibia-implant interface under the constant
loading condition with the help of patient-specific bone microstructure using a representative
volume element (RVE).

9. Implant Selection
• Based on the bone geometry, the implant size was selected, which is slightly smaller than
the cut.

10. Bone Test Data
Bone Geometry
• The patient-specific bone geometry represented through a cubic volume is
reconstructed using images from a μCT scan.
• The resulting μCT image is converted to grayscale ranging from
0 (black/void) to 255 (white/bone) with the appropriately chosen cut-off
range for further analysis.
• Based on literature, the threshold 70-255 is chosen to segment out the
bone structure.
• Only the specific region of the patient-specific bone sample is used for the
FEA simulation to reduce the memory and computational time of the
analysis.​
70-255
grayscale
(selected)
95.6% bone volume
µCT scan of bone
75-255
grayscale
Selected for FEA
90.1% bone volume
Wu Y, Adeeb S, Doschak MR. Using Micro-CT Derived Bone Microarchitecture to Analyze Bone Stiffness - A Case Study on Osteoporosis Rat Bone.
Front Endocrinol (Lausanne). 2015 May 20;6:80. doi: 10.3389/fendo.2015.00080. PMID: 26042089; PMCID: PMC4438594.

11. Representative Volume Element (RVE)
RVE Methodology
• The RVE is designed to capture effective structural and material
properties at microscale level which are used as input parameters for
the macroscale.
• The RVE approach performs statistical averaging of
the microstructural features within the given cubic volume by applying
loading from six independent directions (Micromechanics Plugin).
RVE Sample
Bone
Air
Domain Size
Part Properties
Material Properties

12. Representative Volume Element (RVE) Cont.
RVE Size Selection (Trabecular and Cortical Bones)
• To save processing memory and computational time, an iterative process of determining the RVE cube
size is done such that the porosity in the cube between two consecutive sizes of cubes is not very
different.
• Several sizes of cubic samples are selected along the ML (Mediolateral) and AP (Anteroposterior)
directions of trabecular bone and are compared for porosity.
Cortical Bone Trabecular Bone
Locations of samples selected
(RVE size 1x1x1mm)
Locations of samples selected
(RVE size 5x5x5mm)
5 x 5 x 5 mm
4 samples
~96.2% bone volume average
1 x 1 x 1 mm
3 samples
~98.9% bone volume average
Treece, G M et al. “High resolution cortical bone thickness measurement from clinical CT data.” Medical image analysis vol. 14,3 (2010): 276-90.
doi:10.1016/j.media.2010.01.003

13. Material Comparison – Overview​
Model 2
Model 1
Co-Cr-Mo
UHMWPE
Co-Cr-Mo Ti-Al
Co-Cr-Mo
φ=60%
Ti-Al
φ=60%
Trabecular
Trabecular
Cortical Cortical

14. Design Check using RVE​
Geometry Specification
Implant components and bones
Meshing of implants and bones
Meshing
half - symmetry
fixed
Load Application Point
Ffem
tied
Boundary Conditions and Constrains
Loading Conditions
Loading Condition Boundary Conditions and Constrains
Rotation of femoral
component by 75-
degrees about its
flexion axis. Coefficient of friction throughout
the model is 0.04

15. Results and Discussion
Results and Discussion
• von Mises stresses developed along the surface of the
bone with constant loading using Co-Cr-Mo implant
material is slightly less compared to Ti-Al implant material
• The bone is not expected to yield as the Von Mises
stresses are below the yield limit for trabecular bone
von Mises stresses using Co-Cr-Mo
implant material
von Mises stresses using
Ti-Al implant material
Implant movement

16. Conclusions
Conclusions:
• Was able to accurately evaluate designs under different conditions leading to more tailored,
patient-specific implants.
• Using numerical modeling, it was possible to improve product performance by comparing various
design options.
• Reduced number of material testing and lead time reduction.

17. Future Work
Future Work
• Additional failure criteria (cyclic loading, progressive damage, cracking, etc.)
• End-to-end automated solution – Workflow integrating CAD-Bone CT-FEA

18. Thank You
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