Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-24T03:06:00.711Z Has data issue: false hasContentIssue false
  • English
  • Français

Online Wear Detection Using High-Speed Imaging

Published online by Cambridge University Press:  12 August 2016

Seyfollah Soleimani Open the ORCID record for Seyfollah Soleimani [Opens in a new window] ,
Jacob Sukumaran ,
Koen Douterloigne ,
Patrick De Baets  and
Wilfried Philips
Seyfollah Soleimani*
Affiliation:
Department of Computer Engineering, Faculty of Engineering, Arak University, Arak 38156-8-8349, Iran
Jacob Sukumaran
Affiliation:
Ghent University, Laboratory Soete, Technologiepark, Zwijnaarde 903, B-9052 Gent, Belgium
Koen Douterloigne
Affiliation:
Ghent University iMinds-Telin-IPI, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium
Patrick De Baets
Affiliation:
Ghent University, Laboratory Soete, Technologiepark, Zwijnaarde 903, B-9052 Gent, Belgium
Wilfried Philips
Affiliation:
Ghent University iMinds-Telin-IPI, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium Senior member of IEEE
*
*Corresponding author. [email protected]
Get access

Abstract

In this paper, the change detection of a fast turning specimen is studied at micro-level, whereas the images are acquired without stopping the rotation. In the beginning of the experiment, the imaging system is focused on the surface of the specimen. By starting the rotation of the specimen, the diameter of the specimen changes due to wear, which results in de-focusing of the imaging system. So the amount of blur in the images can be used as evidence of the wear phenomenon. Due to the properties of the microscope, the corners of the frames were dark and had to be cropped. So, each micrograph reflects only a small area of the surface. Nevertheless, techniques like stitching of multiple images can provide a significant surface area for micro-level investigation which increases the effectiveness of analyzing the material modification. Based on the results computer vision could detect a change of about 1.2 µm in the diameter of the specimen. More important is that we could follow the same locations of the surface in the microscopic images despite blurring, uneven illumination, change on the surface, and relatively a high-speed rotation.

Keywords

Rolling-slidingoptical microscopymicro-scale abrasionsurface analysis
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 22 , Issue 4 , August 2016 , pp. 820 - 840
Copyright
© Microscopy Society of America 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aharoni, S. (1973). Wear of polymers by roll-formation. Wear 25(3), 309–327.CrossRefGoogle Scholar
Al-Kindi, G. & Shirinzadeh, B. (2007). An evaluation of surface roughness parameters measurement using vision-based data. Int J Mach Tool Manu 47(3), 697–708.CrossRefGoogle Scholar
Alshibli, K.A. & Alsaleh, M. (2004). Characterizing surface roughness and shape of sands using digital microscopy. ASCE, J Comput Civ Eng 18(1), 36–45.CrossRefGoogle Scholar
Artyushkova, K., Svitlana, P., Madhu, D. & Plamen, A. (2012). Use of digital image processing of microscopic images and multivariate analysis for quantitative correlation of morphology, activity and durability of electrocatalysts. RSC Adv 2, 4304–4310.CrossRefGoogle Scholar
Bayer, R.G. (2004). Mechanical Wear Fundamentals and Testing, Revised and Expanded. New York: CRC Press.CrossRefGoogle Scholar
Benabdallah, H. (1997). Reciprocating sliding friction and contact stress of some thermoplastics against steel. J Mater Sci 32(19), 5069–5083.CrossRefGoogle Scholar
DeVoe, D., Knox, L. & Zhang, G. (1992). An experimental study of surface roughness assessment using image processing. Technical report. Systems Research Center, University of Maryland, College Park.Google Scholar
Dhanasekar, B. & Ramamoorthy, B. (2008). Assessment of surface roughness based on super resolution reconstruction algorithm. Int J Adv Manuf Tech 35(11), 1191–1205.CrossRefGoogle Scholar
Gaudreau-Balderrama, A. (2012). Multi-Modal Image Registration. Boston: Boston University.Google Scholar
Kano, S., Homma, H., Sasaki, S. & Shimura, H. (2008). In situ monitoring of friction surfaces and their sequence pattern analysis. Philos Trans A Math Phys Eng Sci 366(1865), 665–671.Google ScholarPubMed
Kano, S. & Suzuki, T. (2009). In-situ monitoring of friction surfaces and friction modelling by surface pattern analysis. Wear 267(5), 1075–1079.CrossRefGoogle Scholar
Kiran, M., Ramamoorthy, B. & Radhakrishnan, V. (1998). Evaluation of surface roughness by vision system. Int J Mach Tool Manu 38(5), 685–690.CrossRefGoogle Scholar
Kumar, R., Kulashekar, P., Dhanasekar, B. & Ramamoorthy, B. (2005). Application of digital image magnification for surface roughness evaluation using machine vision. Int J Mach Tool Manu 45(2), 228–234.CrossRefGoogle Scholar
Ledda, A. (2006). Mathematical morphology in image processing. PhD thesis, Ghent University, Ghent, Belgium.Google Scholar
Lee, B., Yu, S. & Juan, H. (2004). The model of surface roughness inspection by vision system in turning. Mechatronics 14(1), 129–141.CrossRefGoogle Scholar
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. & Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2), 187–198.CrossRefGoogle ScholarPubMed
Raadnui, S. (2005). Wear particle analysis—utilization of quantitative computer image analysis: a review. Tribol Int 38(10), 871–878.CrossRefGoogle Scholar
Rymuza, Z. (2007). Tribology of polymers. Arch Civ Mech Eng 7(4), 177–184.CrossRefGoogle Scholar
Soleimani, S. (2014). Computer vision system for wear analysis. PhD thesis, Ghent University, Ghent, Belgium.Google Scholar
Soleimani, S., Rooms, F. & Philips, W. (2013). Efficient blur estimation using multi-scale quadrature filters. Signal Processing 93(7), 1988–2002.CrossRefGoogle Scholar
Soleimani, S., Sukumaran, J.P., Douterloigne, K., Rooms, F., Philips, W. & De Baets, P. (2012). Correction, stitching and blur estimation of micro-graphs obtained at high speed. In Advanced Concepts for Intelligent Vision Systems, Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. & Zemcik, P. (Eds.), pp. 84–95. Berlin Heidelberg: Springer.CrossRefGoogle Scholar
Sukumaran, J. (2011). Roll-slip phenomenon of polymer composites: online analysis assisted by computer vision. In 12th FEA PhD Symposium, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium.Google Scholar
Sukumaran, J. (2014). Vision assisted tribolography of rolling-sliding contact pf polymer-steel pairs. PhD thesis, Ghent University, Ghent, Belgium.Google Scholar
Sukumaran, J., Soleimani, S., De Baets, P., Rodriguez, V., Douterloigne, K., Philips, W. & Ando, M. (2012). High-speed imaging for online micrographs of polymer composites in tribological investigation. Wear 296, 702–712.CrossRefGoogle Scholar
Tasan, Y., De Rooij, M. & Schipper, D. (2005). Measurement of wear on asperity level using image-processing techniques. Wear 258(1), 83–91.CrossRefGoogle Scholar
Thévenaz, P. & Unser, M. (2000). Optimization of mutual information for multi-resolution image registration. IEEE Trans Image Process 9, 2083–2099.Google Scholar
Vaziri, M., Spurr, R. & Stott, F. (1988). An investigation of the wear of polymeric materials. Wear 122(3), 329–342.CrossRefGoogle Scholar
Ville, O. & Heikkila, J. (2007). Image registration using blur-invariant phase correlation. IEEE Signal Process Lett 14(7), 449–452.Google Scholar
Wang, W., Wong, P. & Zhang, Z. (2000). Experimental study of the real time change in surface roughness during running-in for pehl contacts. Wear 244, 140–146.CrossRefGoogle Scholar
Zhang, J., Korsten, M. & Regtien, P. (2003). A vision system for online wear detection. In Proceedings XVII IMEKO World Congress, pp. 1960–1964, Dubrovnik, Croatia.Google Scholar
Supplementary material: File

Soleimani supplementary material S1

Supplementary Video

Download Soleimani supplementary material S1(File)
File 33.1 KB
Supplementary material: File

Soleimani supplementary material S2

Supplementary Video

Download Soleimani supplementary material S2(File)
File 34.3 KB
Supplementary material: File

Soleimani supplementary material S3

Supplementary Video

Download Soleimani supplementary material S3(File)
File 33.1 KB
Supplementary material: File

Soleimani supplementary material S4

Supplementary Video

Download Soleimani supplementary material S4(File)
File 270 KB
Supplementary material: File

Soleimani supplementary material S5

Supplementary Video

Download Soleimani supplementary material S5(File)
File 1.2 MB
Supplementary material: File

Soleimani supplementary material S6

Supplementary Video

Download Soleimani supplementary material S6(File)
File 1.5 MB
Supplementary material: File

Soleimani supplementary material S7

Supplementary Video

Download Soleimani supplementary material S7(File)
File 1.7 MB
Supplementary material: File

Soleimani supplementary material S8

Supplementary Video

Download Soleimani supplementary material S8(File)
File 270 KB
Supplementary material: File

Soleimani supplementary material S9

Supplementary Video

Download Soleimani supplementary material S9(File)
File 1.2 MB
Supplementary material: File

Soleimani supplementary material S10

Supplementary Video

Download Soleimani supplementary material S10(File)
File 270 KB
Supplementary material: File

Soleimani supplementary material S11

Supplementary Video

Download Soleimani supplementary material S11(File)
File 1.2 MB
Supplementary material: File

Soleimani supplementary material S12

Supplementary Video

Download Soleimani supplementary material S12(File)
File 32.5 KB
Supplementary material: File

Soleimani supplementary material S13

Supplementary Video

Download Soleimani supplementary material S13(File)
File 136.9 KB
Supplementary material: File

Soleimani supplementary material S14

Supplementary Video

Download Soleimani supplementary material S14(File)
File 32 KB
Supplementary material: File

Soleimani supplementary material S15

Supplementary Video

Download Soleimani supplementary material S15(File)
File 134.9 KB
Supplementary material: File

Soleimani supplementary material S16

Supplementary Video

Download Soleimani supplementary material S16(File)
File 33.1 KB
Supplementary material: File

Soleimani supplementary material S17

Supplementary Video

Download Soleimani supplementary material S17(File)
File 140.6 KB
Supplementary material: File

Soleimani supplementary material S18

Supplementary Video

Download Soleimani supplementary material S18(File)
File 33.1 KB
Supplementary material: File

Soleimani supplementary material S19

Supplementary Video

Download Soleimani supplementary material S19(File)
File 140.7 KB
Supplementary material: File

Soleimani supplementary material S20

Supplementary Video

Download Soleimani supplementary material S20(File)
File 32.9 KB
Supplementary material: File

Soleimani supplementary material S21

Supplementary Video

Download Soleimani supplementary material S21(File)
File 142.5 KB