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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.

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An Optimization Design of Artificial Hip Stem by Genetic Algorithm and Pattern Classification

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  1. An Optimization Design of Artificial Hip Stem by Genetic Algorithm and Pattern Classification

  2. Artificial Hip STEM

  3. history • First elaborated in 1961 • More than 1,000,000 operations each year worldwide • Performance depend on: • Stress • Displacement • Amount of wear • Fatigue

  4. Artificial Hip STEM

  5. PROBLEMs in current DESIGN • Design from Boolean operation of basic geometric primitives • Design based on experience • Can not fit individual needs

  6. Design method • Geometry modeling • Finite element model • Genetic Algorithm • Patten classification

  7. Geometry modeling • freeform model • represented by B-splines • Geometric Models are stored parametrically • randomly generate

  8. Geometry modeling

  9. Geometry modeling

  10. Geometry modeling

  11. Geometry modeling

  12. FEA • Finite element model • Static analysis • Distribution of stresses • Displacements • SolidWorks Simulation

  13. FEA

  14. Done by Solidworks API (C#)

  15. Genetic Algorithm • Components of a Genetic Algorithm • Representation of gene • Selection Criteria • Reproduction Rules

  16. Genetic Algorithm

  17. Genetic Algorithm • Step 1: Set up an initial population P(0)—an initial set of solution Evaluate the initial solution for fitnessGeneration 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

  18. Patten classification • FEA is very time consuming • Eliminate useless data • Predict result

  19. Implementation Method • Solidworks • Simulation • Matlab • Solidworks API • C# • Integration

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