Simulation of Fibrous Scaffold Optimal Distribution by Genetic Algorithm

Presentation : D. Semnani

ICSIP 2009, Amsterdam

Isfahan University of Technology

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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

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Application of image processing to determine the properties of the non-

woven and nano-fibers

• LightingLight source with low wave length, laser and light emitting diodes

• Magnification• Preparation

Value of the each pixel based on the adjacent values

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SKELETONIZING OR THINNING

• Replacing each object with a narrow line (thickness: 1 pixel)

• Morphological or Pruning

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FIBER ORIENTATION DISTRIBUTION (FOD)

• Orientation distribution functionα: the angle between fiber and horizontal axis

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DIRECT TRACKING SEARCH

• Using Morphological or Pruning methods

• Every pixel has 8 adjacent pixels

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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.

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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

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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.

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FURRIOR TRANSFORM

• Evaluating image by special radius and loop thickness

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FURRIOR TRANSFORM • If the image is not periodic, then discontinuation

points appear in transformed image.

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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

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GAUSSIAN FILTER• Replacing each point by regarding

adjacent points

• H and W : size of kernal matrice

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GRADIENT

Sobel matrice

Gy Gx

1 2 1 1- 0 1

0 0 0 2- 0 2

1- 2- 3- 1- 0 1

x7 x4 x1 i-1

x8 x5 x2 i

x9 x6 x3 i+1

j+1 j j-1

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FLOW FIELD ANALISYS

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HOUGH TRANSFORM

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HOUGH TRANSFORM

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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.

Accuracy ranking

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ORIENTATION IN REAL WEB• The best image will be one that

represents the entire field as a two dimensional projection.

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EDGE THRESHOLDING

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FIBER DIAMETER DISTRIBUTION

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FIBER DIAMETER DISTRIBUTION

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Threshold 2Threshold 1Threshold 3

MEASURING THE POROSITY OF VARIOUS SURFACE

LAYERS

• Threshold 1 : • Threshold 2 : • Threshold 3 :

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MEASURING THE POROSITY OF VARIOUS SURFACE

LAYERS

n :Number white points N : Number of all points p : Porosity percentage

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CALCULATING THE POROSITY

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LAYER UNIFORMITY

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LAYER UNIFORMITY

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MEASURING LAYER WEIGHT

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Our Method

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Ideal Structure

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GA

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• 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

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GA MOdel

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•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.

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Image Processing for Fitness

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• Plotting the structure

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Real Web

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Histogram Modification, Thresholding,

Converting to binary formand Thinning.

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Optimal Web

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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

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Breaking load and fitness value VS Sample

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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