an optimization design of artificial hip stem by genetic algorithm and pattern classification

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