ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

(a) (b)
Figure 9. Cumulative probability for the von Mises stress in the knee implants (a),
contact pressure in UHMWPE.
However, as outlined in this paper, a complex 3D model was generated and used in both
stress and probabilistic analysis since the response surface method [11], by which a
mathematical model is formed in place of a time consuming analysis, was utilized in
order to carry out probabilistic analyses in a reasonable computational time.
Having carried out probabilistic analysis, the cumulative probability curves for the von
Mises stress distribution of the knee implant and the contact pressure on UHMWPE are
given in Fig.9(a) and (b), respectively. As these results indicates, even for the stress
values higher than the mean values of these stresses obtained from the deterministic
analysis given in Fig.7, there is a great chance to occur. Therefore, any variation that the
model might possible have during the design and analysis stages must be taken into
account using a probabilistic analysis.
6. CONCLUSIONS
In this study, a realistic patient-specific model of the knee implant was developed to use
in probabilistic analysis. The 3D model of the knee implant was obtained from a patient
at the age of 65, suffering from gonarthrosis, and the 3D CAD model was modified by
the orthopedic by slicing both femur and tibia.
The knee implant model used in this paper includes a realistic separation of the cortical
and trabecular bone without using a uniform thickness for the cortical along the femur
and tibia. This model also has an empty medullary canal, rather than having trabecular
bone in the most of the models in the literature.
The material properties were assigned considering both the values in the literature and
the HU values obtained from the patient’s CT data. Different grouping approaches were
conducted to determine the distribution of the HU values of the trabecular bone, and
statistical approaches were utilized to determine the distribution type with their
parameters.
After validation of the knee implant by comparing the results obtained with that in the
literature, the probabilistic analysis was carried out in order to see the effect of the
variations in the design parameters on the performance of the knee implant. As outlined
in the results, the analysis presented in the paper illustrated that the probabilistic
analysis helps to account for variations and uncertainties in knee implant design as
opposed to the deterministic analysis.
8