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

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Presentation on theme: "Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm Presentation : D. Semnani ICSIP 2009, Amsterdam Isfahan University of Technology."— Presentation transcript:

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

2 2 Artificial Intelligence in Textile Engineering ICSIP 2009, Amsterdam 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 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 4 SKELETONIZING OR THINNING Replacing each object with a narrow line (thickness: 1 pixel) Morphological or Pruning

5 5 FIBER ORIENTATION DISTRIBUTION (FOD) Orientation distribution function α: the angle between fiber and horizontal axis Fibers were made from direct and short lines Creating artificial images and testing algorithms Comparing obtained results

6 6 Direct tracking Furrior transform Hough transform Flow Field Analys Orientation measuring methods

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

8 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 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 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 11 FURRIOR TRANSFORM Evaluating image by special radius and loop thickness

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

13 13 FLOW FIELD ANALISYS The edges of image present the field orientations stages 1.Morphological operation 2.Calculating gradient vector for all points 3.Dividing image to the small images 4.Determining the mean orientation of fields in each small image 5.Calculating the final image orientation by using mean orientations of small images 13

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

15 15 GRADIENT Sobel matrice Gy Gy Gx Gx x7x7 x4x4 x1x1 i-1 x8x8 x5x5 x2x2 i x9x9 x6x6 x3x3 i+1 j+1jj-1

16 16 FLOW FIELD ANALISYS

17 17 HOUGH TRANSFORM

18 18 HOUGH TRANSFORM

19 19 COMPARING METHODS 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. Direct Tracking Furrior Transform Flow Field Analisys Accuracy ranking

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

21 21 EDGE THRESHOLDING

22 22 FIBER DIAMETER DISTRIBUTION

23 23 FIBER DIAMETER DISTRIBUTION

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

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

26 26 C ALCULATING THE P OROSITY

27 27 LAYER UNIFORMITY

28 28 L AYER U NIFORMITY

29 29 MEASURING LAYER WEIGHT

30 30 Our Method ICSIP 2009, Amsterdam Ideal Structure

31 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 32 GA MOdel ICSIP 2009, Amsterdam 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

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

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

35 35 Optimal Web ICSIP 2009, Amsterdam

36 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 37 ICSIP 2009, Amsterdam Breaking load and fitness value VS Sample

38 38 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. ICSIP 2009, Amsterdam

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