Slide 1Artificial Hip STEMhistoryArtificial Hip STEMPROBLEMs in current DESIGNDesign methodGeometry modelingGeometry modelingGeometry modelingGeometry modelingGeometry modelingFEAFEADone by Solidworks API (C#)Genetic AlgorithmGenetic AlgorithmGenetic AlgorithmPatten classificationImplementation MethodAN OPTIMIZATION DESIGN OF ARTIFICIAL HIP STEM BY GENETIC ALGORITHM AND PATTERN CLASSIFICATIONARTIFICIAL HIP STEMHISTORY•First elaborated in 1961 •More than 1,000,000 operations each year worldwide•Performance depend on:•Stress•Displacement•Amount of wear•FatigueARTIFICIAL HIP STEMPROBLEMS IN CURRENT DESIGN•Design from Boolean operation of basic geometric primitives•Design based on experience•Can not fit individual needsDESIGN METHOD•Geometry modeling•Finite element model•Genetic Algorithm•Patten classificationGEOMETRY MODELING•freeform model •represented by B-splines•Geometric Models are stored parametrically•randomly generateGEOMETRY MODELINGGEOMETRY MODELINGGEOMETRY MODELINGGEOMETRY MODELINGFEA•Finite element model•Static analysis•Distribution of stresses •Displacements•SolidWorks SimulationFEADONE BY SOLIDWORKS API (C#)GENETIC ALGORITHM•Components of a Genetic Algorithm•Representation of gene•Selection Criteria•Reproduction RulesGENETIC ALGORITHMGENETIC 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 2PATTEN CLASSIFICATION•FEA is very time consuming•Eliminate useless data•Predict resultIMPLEMENTATION METHOD•Solidworks•Simulation•Matlab•Solidworks
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