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

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

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