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Research Papers: Elastohydrodynamic Lubrication

Development of Infrared Microscopy for Measuring Asperity Contact Temperatures

[+] Author and Article Information
Julian Le Rouzic

e-mail: j.le-rouzic@imperial.ac.uk

Tom Reddyhoff

e-mail: t.reddyhoff@imperial.ac.uk
Tribology Group,
Department of Mechanical Engineering,
Imperial College,
London SW7 2AZ, United Kingdom

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received August 29, 2012; final manuscript received December 2, 2012; published online March 18, 2013. Assoc. Editor: Dong Zhu.

J. Tribol 135(2), 021504 (Mar 18, 2013) (9 pages) Paper No: TRIB-12-1138; doi: 10.1115/1.4023148 History: Received August 29, 2012; Revised December 02, 2012

Surface temperature measurements within sliding contacts are useful since interfacial heat dissipation is closely linked to tribological behavior. One of the most powerful techniques for such measurements is in-contact temperature mapping whereby a sliding contact is located beneath an infrared microscope. In this approach, one of the specimens must be transparent to infrared and coated such that radiation components can be distinguished and isolated from background values. Despite its effectiveness, a number of practical constraints prevent this technique from being applied to rough surfaces—a research area where temperature maps could provide much needed two-dimension input data to inform mixed and boundary friction models. The research described in this paper is aimed at improving the infrared temperature mapping technique in terms of validity, robustness, and spatial resolution, so that measurements of rough surfaces contacts can be made. First, Planck's law is applied in order to validate the use of surface coating as a means of removing background radiation. Second, a refined method of calibration is put forward and tested, which negates the need for a soft aluminum coating and hence enables rough surfaces to be measured. Finally, the use of super-resolution algorithms is assessed in order extend spatial resolution beyond the current limit of 6 μm.

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References

Figures

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Fig. 1

Schematic diagram of apparatus

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Fig. 2

Schematic diagram showing radiation components of the system

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Fig. 9

Geometry of patterned surface on the first photolithography specimen (repeated lines of 20 and 40 μm in thickness)

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Fig. 10

LR and HR infrared images of the sample surface given in Fig. 9

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Fig. 8

Schematic diagram showing super resolution principle

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Fig. 7

Maximum contact temperature versus speed for a contact between steel roller and disk, lubricated with Santotrac50. The contact is loaded with 20 N load and a slide-roll ratio of 1.0 is applied. The temperatures shown have been calculated using either Eq. (2a) or (13).

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Fig. 6

Maximum contact temperature versus speed for a contact between ball and disk, lubricated with Santotrac50. The contact is loaded with 20 N load and a slide-roll ratio of 1.0 is applied. The temperatures shown have been calculated using either Eq. (2a) or (10).

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Fig. 5

Normalized single-pixel camera counts, as a function of temperature, from an interface between a steel ball and an uncoated (Un) and a chromium coated (Cr), with subtraction of camera counts against an aluminum coated disk (Al)

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Fig. 4

Normalized single-pixel camera counts, as a function of temperature, from an interface between a steel ball and an uncoated (Un), a chromium coated (Cr), and an aluminum (Al) coated disk

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Fig. 3

Single-pixel camera counts, as a function of temperature, from an interface between a steel ball and an uncoated (Un), a chromium coated (Cr), and an aluminum (Al) coated disk

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Fig. 11

Geometry of surface pattern on photolithography specimen (a lattice of 30 μm spaced dots of 10 μm diameter)

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Fig. 12

(a) LR image. (b) LR image with simple linear interpolation. (c) HR image with fast robust super resolution of 50 images. (d) HR image with interpolation super resolution of 50 images.

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Fig. 13

Pixel values for a line across the LR and HR infrared images shown in Figs. 12(a) and 12(b)

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Fig. 14

(a) Circles detection of on HR image by circular Hough transform. (b) High magnification of (a).

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Fig. 15

Number of circles detected and their average diameter versus the number of LR images used for (a) and (b) fast robust SR and (c) and (d) interpolation SR algorithms

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