Isfahan University of Technology
Measurement of Nonwoven Surface Roughness With
Machine Vision Method
ICSIP 2009, Amsterdam
Presentation : D. Semnani
ICSIP 2009, Amsterdam
Image Processing in Textile Engineering
•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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ICSIP 2009, Amsterdam
Spunbond Nonwovens
• Application & End Use
• Importance of Surface Friction
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ICSIP 2009, Amsterdam
Measurement of Textile Surface Roughness
• Conventional Measurement Advice
• Disadvantages
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ICSIP 2009, Amsterdam
Our Method
First :Simulate An Ideal Surface
• Complete and Regular Sine Roughnesses
• Minimum Sensible Amplitude and Wave Length
2 :Compare of Simulated Ideal Surface
with Samples Surface Profile
3 :Surface Roughness Factor determination
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4 :Compare Friction Coefficient With Evaluated
Surface Roughness
Factor
Finite element model of
human finger
ICSIP 2009, Amsterdam
Simulating Ideal Surface
• Mathematically Aspect of an Complete sine Surface
z  0.0025 sin( 2x / a).sin( 2y / b)
a  b  1 mm

 A  0.0025 mm
• Adjust the Confine of Amplitude between 0 to 0.0025 mm Rather Than -0.00125
to 0.00125 mm
z  0.00125 sin( 2x / a).sin( 2y / b  0.00125
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ICSIP 2009, Amsterdam
Plotting The Simulated Ideal Surface
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ICSIP 2009, Amsterdam
Image Processing of Sample Surfaces
••Sample
PlottingProperties
the Surface Profile Of Samples
• Image Acquisition of Sample Surfaces
•Conversion and Processing
RGB
Image
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Grayscale Image
Gaussian
and Wiener
Filtering
Histogram
Equalization
ICSIP 2009, Amsterdam
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
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ICSIP 2009, Amsterdam
Definition of Normalized Factors For Compare of Ideal
And Sample Surfaces
s : index of simulated surface
r : index of generated profile from real surface
Kt 
Ts  Tr
Ke 
Ts
Kv 
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Vs  Vr
Vr
E s  Er
Kn 
Es
K Id 
I ds  I dr
I ds
Ns  Nr
Ns
ICSIP 2009, Amsterdam
And Finally : Definition of Surface Roughness Factor
1
Rs    K n  K t  K e  K I d  K v
5

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
ICSIP 2009, Amsterdam
Determination The Surface Friction Coefficient of Samples
Friction Standard Test ASTM D1894
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ICSIP 2009, Amsterdam
Regression Between Surface Roughness Factor (Rs) and
Surface Friction Coefficient of Samples
R’s
= 1.027 Rs – 0.023
New Roughness Factor with effect of friction
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μ = 1.027 Rs – 0.023
ICSIP 2009, Amsterdam
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
Final
Thanks for your Attention