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Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm

This presentation discusses the simulation of fibrous scaffold optimal distribution using genetic algorithms. It explores the application of artificial intelligence in textile engineering for optimization in textile processes, materials, and machinery. The presentation also covers the measurement and prediction of tissue quality, as well as the use of image processing to determine the properties of non-woven and nano-fibers.

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Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm

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  1. Isfahan University of Technology Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm ICSIP 2009, Amsterdam Presentation : D. Semnani

  2. ICSIP 2009, Amsterdam Artificial Intelligence in Textile Engineering Optimization in Textiles Process, Material and Machinery Classification of Products Measuring Uniformity of Fibrous Structures Determination of Woven And Nonwoven Fabrics Characteristics Prediction of Tissue Quality

  3. Application of image processing to determine the properties of the non-woven and nano-fibers Lighting • Light source with low wave length, laser and light emitting diodes Magnification Preparation • Value of the each pixel based on the adjacent values

  4. SKELETONIZING OR THINNING Replacing each object with a narrow line (thickness: 1 pixel) Morphological or Pruning

  5. FIBER ORIENTATION DISTRIBUTION (FOD) Creating artificial images and testing algorithms Comparing obtained results Orientation distribution function α: the angle between fiber and horizontal axis

  6. Hough transform Direct tracking Furrior transform Flow Field Analys

  7. DIRECT TRACKING SEARCH Using Morphological or Pruning methods Every pixel has 8 adjacent pixels

  8. DIRECT TRACKING SEARCH It is assumed that the fibers are one pixel thick and have not severe disruptions or kinks or bends within one pixel distance.

  9. FOURIOR TRANSFORM An image web was formed from light cycles (dark to white and vice versa) u : frequency in X axis v : frequency in Y axis

  10. FURRIOR TRANSFORM Power Spectrum Function If fiber are orientated in a special direction so frequency in same direction is low and in perpendicular direction is high.

  11. FURRIOR TRANSFORM Evaluating image by special radius and loop thickness

  12. FURRIOR TRANSFORM If the image is not periodic, then discontinuation points appear in transformed image.

  13. FLOW FIELD ANALISYS 13 The edges of image present the field orientations stages Morphological operation Calculating gradient vector for all points Dividing image to the small images Determining the mean orientation of fields in each small image Calculating the final image orientation by using mean orientations of small images

  14. GAUSSIAN FILTER Replacing each point by regarding adjacent points H and W : size of kernal matrice

  15. GRADIENT Sobel matrice

  16. FLOW FIELD ANALISYS

  17. HOUGH TRANSFORM

  18. HOUGH TRANSFORM

  19. COMPARING METHODS Furrior Transform Direct Tracking Flow Field Analisys Accuracy ranking Direct Tracking is the best method for on-line controlling but it has low speed process because of loops in its algorithm. Flow Field Analisys evaluate the STD lower than the other methods and can be used in on-line controlling. Furrior Transform is the best choice to non-on-line controlling. The results of Hough and Furrior Transform is so close.

  20. ORIENTATION IN REAL WEB The best image will be one that represents the entire field as a two dimensional projection.

  21. EDGE THRESHOLDING

  22. FIBER DIAMETER DISTRIBUTION

  23. FIBER DIAMETER DISTRIBUTION

  24. MEASURING THE POROSITY OF VARIOUS SURFACE LAYERS Threshold 2 Threshold 1 Threshold 3 Threshold 1 : Threshold 2 : Threshold 3 :

  25. MEASURING THE POROSITY OF VARIOUS SURFACE LAYERS • n :Number white points • N : Number of all points • p : Porosity percentage

  26. Calculating the Porosity

  27. LAYER UNIFORMITY

  28. Layer Uniformity

  29. MEASURING LAYER WEIGHT

  30. ICSIP 2009, Amsterdam Our Method Ideal Structure

  31. GA ICSIP 2009, Amsterdam Optimizing the model SELECTION: selecting individuals for reproduction. REPRODUCTION: Cross over and Mutation are most common reproduction operators of GA. EVALUATION: the fitness of new chromosome is evaluated. REPLACEMENT: individuals from the old population are removed and replaced by the new ones. The algorithm is stopped when the population converges toward optimal solution e.g. finding minimum of a function

  32. ICSIP 2009, Amsterdam GA MOdel The Number of lines in each group was equal. The Chromosomes have defined in a binary from There were two Genes with lengths of 5 and 19 bits The angle drops between 0-179.

  33. ICSIP 2009, Amsterdam Image Processing for Fitness Plotting the structure

  34. ICSIP 2009, Amsterdam Real Web Histogram Modification, Thresholding, Converting to binary form and Thinning.

  35. ICSIP 2009, Amsterdam Optimal Web

  36. ICSIP 2009, Amsterdam Comparison optimal model and real web with real web Find the optimal web Fitness measuring Real web production Image Enhancement Measuring the fitness of real web Comparison optimal model and real web Analysis mechanical properties

  37. ICSIP 2009, Amsterdam Breaking load and fitness value VS Sample

  38. ICSIP 2009, Amsterdam Conclusion Simulated a non-woven web with optimal distribution, using Genetic Algorithm Relationship between distribution uniformity of a web and its breaking load Validity of such a relation has been investigated by performing the fitness function In another words, the sample which were more uniform, had a higher breaking load. In a further research we will investigate this relationship on a three dimensional structure of a fiber reinforcement composite.

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