1. Automating the Lee Model

2. Major Components Simulator code –Verifying outputs –Verifying model equations –Graphical User interface Auto-tuning the model parameters –Simple pre-processing of trace data –Optimization of fit of current curves –Tuning of genetic algorithm code

3. Simulator Code Rebuilding the simulation code –Port Excel VBA to C# –Object Oriented Design 5 models – Corona, Axial, Radial, Radial RS, Radiative, Expanded Axial Provide for future modifications –Runs in < 1sec –Export results to Excel –Graphical interface Ease of use to run with new parameters and view results Advanced graphing Predefined tuned models

4. Object Oriented Design

5. Graphical output NEW EXISTING

6. Machine Configuration

7. Simulation Results

8. Detailed Graphs

9. Auto-tuning problem Defining what is a good-fit –Formulating a numerical problem –Coefficient of determination, R2 (inadequate) –Other visual cues of good fit Finding the model parameters that “fits” –Optimization search algorithm Local maxima problem Genetic algorithm

10. Goodness of fit R2 definition R2 = 1- SS reg / SS tot where SS reg = Sum (I meas - I comp ) 2 i.e. sum of errors squared SS tot = Sum ( I - I mean ) 2 normalization factor Curve features –Peak –Slope –End of radial phase Peak End of Radial Phase Slope of radial phase

11. Locating features on current trace Peak – easy End of radial phase –Take 2 nd difference –Take maximum difference as the point Slope –Calculate slope from end of pinch to mid- radial phase Most linear portion from mid slope to end

12. Fitness Function Fitness, R2’ R2’ = w1 * R2 + w2 * ME + w3 * PE + w4 * SE w1, w2, w3, w4 are weights, w1 = 1 w3 = peak current / (peak current – pinch current) w4 = 2 * w3 w2 = 1.2 * w4 ME is the maximum current errors (peaks), ME = (1 - ME1) + (1 - ME2) ME1 = computed peak current – measured peak current measured peak current – measured pinch current ME2 = computed peak time – measured peak time measured peak time – measured pinch time PE is similarly calculated. The radial slope error is calculated as follows:- SE = 1 - computed peak current – mid-radial current pinch time – mid-radial time

13. Genetic Algorithm Create population Rank using fitness function Select parents for new population Create new population using –Mutation –Crossover Add fittest genes from last pop

14. GA Concepts –Population –Chromosome –Genes –Mutation –Cross-over –Elitism

15. Optimization strategy Preprocess measured current Local optimization stages –Axial for fitness F( massf, currf) –Radial for fitness F( massfr, currfr) Global optimization stage –Whole model for fitness F( massf, currf, massfr, currfr) Repeat above as “stages”

16. Results