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Measurement of Nonwoven Surface Roughness With Machine Vision Method
Presentation : D. Semnani
ICSIP 2009, Amsterdam
Isfahan University of Technology
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Image Processing in Textile Engineering
ICSIP 2009, Amsterdam
•Online Quality Control of Textiles
• Detection Of Yarn And Fabric Faults
• Classification of Products
• Measuring Uniformity of Fibrous Structures
• Determination of Woven And Nonwoven Fabrics Surface Roughness
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Spunbond Nonwovens
ICSIP 2009, Amsterdam
• Application & End Use
• Importance of Surface Friction
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Measurement of Textile Surface Roughness
ICSIP 2009, Amsterdam
• Conventional Measurement Advice
KAWABATA Evaluation System• Disadvantages
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Our Method
ICSIP 2009, Amsterdam
First :Simulate An Ideal Surface
Finite element model of human finger
• Complete and Regular Sine Roughnesses
• Minimum Sensible Amplitude and Wave Length
2 :Compare of Simulated Ideal Surface with Samples Surface Profile3 :Surface Roughness Factor determination4 :Compare Friction Coefficient With Evaluated Surface Roughness Factor
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Simulating Ideal Surface
)/2sin(.)/2sin(0025.0 byaxz
mmA
mmba
0025.0
1
00125.0/2sin(.)/2sin(00125.0 byaxz
ICSIP 2009, Amsterdam
• Mathematically Aspect of an Complete sine Surface
• Adjust the Confine of Amplitude between 0 to 0.0025 mm Rather Than -0.00125 to 0.00125 mm
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Plotting The Simulated Ideal Surface
ICSIP 2009, Amsterdam
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Grayscale Image
•Sample Properties
• Image Acquisition of Sample Surfaces
•Conversion and Processing
Image Processing of Sample Surfaces
ICSIP 2009, Amsterdam
• Plotting the Surface Profile Of Samples
RGB Image
Histogram Equalization Gaussian and Wiener Filtering7/13
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Extracted Parameters From Preprocessed Sample Images and Simulated ideal Surface
N : Number of picks in the surface
T : Variance of distance between picks from point (0,0) in image matrices
E : Volume of surface profile
Id : Dispersion ratio (presented by Pourdeyhimi)
V : Variance of gray scale values of image
ICSIP 2009, Amsterdam
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Definition of Normalized Factors For Compare of Ideal And Sample Surfaces
ICSIP 2009, Amsterdam
s : index of simulated surface
r : index of generated profile from real surface
s
rst T
TTK
s
rse E
EEK
s
rsn N
NNK
s
rs
d
d
dd
I I
IIK
r
rsv V
VVK
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vIetns KKKKKRd
5
1
And Finally : Definition of Surface Roughness Factor
ICSIP 2009, Amsterdam
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Friction Standard Test ASTM D1894
ICSIP 2009, Amsterdam
Determination The Surface Friction Coefficient of Samples
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ICSIP 2009, Amsterdam
Regression Between Surface Roughness Factor (Rs) and Surface Friction Coefficient of Samples
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μ = 1.027 Rs – 0.023 R’
s = 1.027 Rs – 0.023
New Roughness Factor with effect of friction
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Final
Conclusion
Advantages of This Method
• Present an appropriate roughness factor which originally implies both elements of roughness :
1. Point by point consideration of surface roughness height compare with line by line height measurement in KES
2. Consideration of fabric surface friction in roughness factor determination
ICSIP 2009, Amsterdam
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Thanks for your Attention