Classification Artificial Hip STEM history First elaborated in 1961 More than 1000000 operations each year worldwide Performance depend on Stress Displacement Amount of wear Fatigue ID: 618418
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Slide1
An Optimization Design of Artificial Hip Stem by Genetic Algorithm and Pattern
ClassificationSlide2
Artificial Hip STEMSlide3
history
First elaborated
in 1961
More
than 1,000,000 operations each year
worldwide
Performance depend on:
Stress
Displacement
Amount
of
wear
FatigueSlide4
Artificial Hip STEMSlide5
PROBLEMs in current DESIGN
Design from Boolean operation of basic geometric primitives
Design based on experience
Can not fit individual needsSlide6
Design method
Geometry modeling
Finite element
model
Genetic
Algorithm
Patten classificationSlide7
Geometry modeling
freeform model
represented
by
B-splines
Geometric Models are
s
tored parametrically
randomly generateSlide8
Geometry modelingSlide9
Geometry modelingSlide10
Geometry modelingSlide11
Geometry modelingSlide12
FEA
Finite element
model
Static analysis
Distribution
of stresses
Displacements
SolidWorks
SimulationSlide13
FEASlide14
Done by
Solidworks
API
(C#)Slide15
Genetic Algorithm
Components of a Genetic Algorithm
Representation
of gene
Selection
Criteria
Reproduction
RulesSlide16
Genetic AlgorithmSlide17
Genetic Algorithm
Step 1: Set up an initial population P(0)—an initial set of solution
Evaluate
the initial solution for fitness
Generation index t=0
Step 2: Use genetic operators to generate the set of children (crossover, mutation)
Add a new set of randomly generated population
Reevaluate the population—fitness
Perform competitive selection—which members will be part of next generation
Select population P(t+1)—same number of members
If not converged t←t+1
Go To Step 2Slide18
Patten classification
FEA is very time consuming
Eliminate useless data
Predict resultSlide19
Implementation Method
Solidworks
Simulation
Matlab
Solidworks API
C#
Integration