Presentation on theme: "Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm Presentation : D. Semnani ICSIP 2009, Amsterdam Isfahan University of Technology."— Presentation transcript:
Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm Presentation : D. Semnani ICSIP 2009, Amsterdam Isfahan University of Technology
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 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) 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 Direct tracking Furrior transform Hough transform Flow Field Analys Orientation measuring methods
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 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 GAUSSIAN FILTER Replacing each point by regarding adjacent points H and W : size of kernal matrice
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 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 Threshold 2Threshold 1Threshold 3 MEASURING THE POROSITY OF VARIOUS SURFACE LAYERS 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
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 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 Image Processing for Fitness ICSIP 2009, Amsterdam Plotting the structure
34 Real Web ICSIP 2009, Amsterdam Histogram Modification, Thresholding, Converting to binary form and Thinning.
35 Optimal Web ICSIP 2009, Amsterdam
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 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