AN OPTIMIZATION DESIGN OF ARTIFICIAL HIP STEM BY GENETIC ALGORITHM AND

PATTERN CLASSIFICATION

ARTIFICIAL HIP STEM

HISTORY• First elaborated in 1961

• More than 1,000,000 operations each year worldwide

• Performance depend on:

• Stress

• Displacement

• Amount of wear

• Fatigue

ARTIFICIAL HIP STEM

PROBLEMS IN CURRENT DESIGN• Design from Boolean operation of basic geometric primitives

• Design based on experience

• Can not fit individual needs

DESIGN METHOD• Geometry modeling

• Finite element model

• Genetic Algorithm

• Patten classification

GEOMETRY MODELING• freeform model

• represented by B-splines

• Geometric Models are stored parametrically

• randomly generate

GEOMETRY MODELING

GEOMETRY MODELING

GEOMETRY MODELING

GEOMETRY MODELING

FEA• Finite element model

• Static analysis

• Distribution of stresses

• Displacements

• SolidWorks Simulation

FEA

DONE BY SOLIDWORKS API (C#)

GENETIC ALGORITHM• Components of a Genetic Algorithm

• Representation of gene

• Selection Criteria

• Reproduction Rules

GENETIC ALGORITHM

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 2

PATTEN CLASSIFICATION• FEA is very time consuming

• Eliminate useless data

• Predict result

IMPLEMENTATION METHOD• Solidworks

• Simulation

• Matlab

• Solidworks API

• C#

• Integration