ReliabilityBased Optimal Design of
Orthopedic Implants, 189927
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Principal Investigator
Daniel P. Nicolella
Inclusive Dates: 10/01/95  Current
Background  The longterm reliability of
today's orthopedic implants is a major concern. As a result, much of today's research is
aimed at improving the overall reliability (or some specific aspect of reliability) of
orthopedic implants. During current analyses, the actual probability of failure is not
computed per se, rather a relative measure of performance is inferred. Therefore, the goal
of this program is to improve the longterm reliability of orthopedic implants by using a
design optimization approach that integrates probabilistic analysis methods and
systematically accounts for uncertainty and randomness to calculate a measure of implant
reliability.
Approach  A numerical shape optimization of
the femoral component of a total hip replacement was performed using engineering design
optimization techniques that incorporate a measure of implant reliability. The implant
reliability was calculated using advanced probabilistic methods developed at the Institute
(NESSUS™). Cemented femoral components were addressed specifically in this work, but
the techniques developed can be modified to investigate noncemented femoral components as
well. A previously developed probabilistic finite element model (ABAQUS^{™)}
was modified to model the cement mantle and the implantcement and cementbone interfaces
and then integrate the model with a numerical optimal implant design framework. The
femurimplant finite element model input variables included trabecular and cortical bone
properties, joint loading, cement properties, interface descriptions, implant material
properties, and initial implant geometry. Based on existing literature and expert opinion,
reasonable assumptions were made to develop input probability distributions. The NESSUS^{™}ABAQUS™
interface was used with the ABAQUS^{™ }model of the femurimplant system.
Reliability was calculated based on performance functions involving the cement,
implantcement interface, and bonecement interface strength characteristics. From this
analysis, sensitivities of the optimal implant design reliability to uncertainties in the
system were quantified.
Accomplishments  Using a general
computational framework that couples the NESSUS^{™} probabilistic analysis
code with a generalpurpose design optimization program, the reliability of a cemented
femoral orthopedic implant was investigated. This framework allows the existing
engineering optimal design models to be incorporated into a reliabilitybased optimal
design methodology. In cemented orthopedic implants, failures are typically initiated by
deterioration of the cement mantle or the implantcement interface or both. Using failure
criteria to monitor the stress in the bonecement mantle and the implantcement interface,
the probability of failure of an implant was calculated. The probability that stress
caused by forces generated at the hip during walking would reach a level that initiates
failure of the cement mantle was calculated to be 0.42 or 42 percent. The shape of
the implant was then optimized to minimize the probability of failure of the implant.
Although the implant design optimization analysis resulted in a 15percent decrease in
stress in the cement mantle, the reduction in stress resulted in only a 2percent decrease
in the probability of failure to 40 percent. Additionally, the resulting
probabilistic sensitivities indicate the importance of understanding joint loading during
normal, everyday activities, one of the least understood biomechanical parameters required
for implant design. The implication of this analysis is that optimal design methods may
result in implant designs that satisfy specific deterministic design constraints and
performance goals, but the reliability of the implant is not necessarily improved.
Advanced design and analysis methods are required to quantify the effects of uncertainties
or lack of knowledge of crucial information (such as applied joint loads) and variability
due to differences within and between patient populations and to incorporate this
uncertainty into the design process.
Materials Research and
Structural Mechanics Program
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