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Research Papers: Contact Mechanics

Spectral Approach on Multiscale Roughness Characterization of Nominally Rough Surfaces

[+] Author and Article Information
Sandip Panda

Tribology Laboratory,
Department of Mechanical Engineering,
Indian Institute of Technology Kharagpur,
Kharagpur 721302, West Bengal, India
e-mail: sandippanda13@gmail.com

Anand Panzade, Mihir Sarangi, S. K. Roy Chowdhury

Tribology Laboratory,
Department of Mechanical Engineering,
Indian Institute of Technology Kharagpur,
Kharagpur 721302, West Bengal, India

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received January 13, 2016; final manuscript received June 22, 2016; published online October 10, 2016. Assoc. Editor: Robert L. Jackson.

J. Tribol 139(3), 031402 (Oct 10, 2016) (10 pages) Paper No: TRIB-16-1018; doi: 10.1115/1.4034215 History: Received January 13, 2016; Revised June 22, 2016

This paper attempts to demonstrate a systematic spectral approach for the characterization of the topographic nature of rough surfaces. Multiscale roughness characterization reveals a panoramic structure of microgeometric features of engineering surfaces, and this is of practical importance in order to include length scale consideration in real contact problems. Surfaces with different levels of root mean square (rms) roughness values were prepared using mechanical finishing processes for this study. Both optical and stylus profilometry data were recorded and analyzed to plot autocorrelation and power spectral density functions (PSDFs) at five different cutoff bandwidths (BWs). Correlation distances were estimated by choosing normalized autocorrelation declination to 1/e as well as to 0.1. In most of the cases, these distances were found to be less than 10% of the corresponding cutoff lengths. Nature of power spectrum has been analyzed and discussion extended to the estimation of bandwidth limited fractal characteristics based on specific spectral information. Power spectral densities (PSD) and their higher moments were extensively used to compute roughness parameters of functional significance such as asperity curvature, asperity density, etc. Evolution of asperity sharpness and asperity density during finishing processes was demonstrated at par with their physical significances. The intrinsic bandwidth parameter as per Nayak's definition was estimated closely to be a value of two for all cases.

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Figures

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Fig. 1

Steps in roughness measurement: (a) disk surface with markings indicating one quarter scanning zone, (b) locating scanning area, (c) optical profilometry, and (d) mechanical stylus profilometry

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Fig. 2

Visible damage on smooth surface (S1) after stylus profilometry (optical microscopy image)

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Fig. 3

One-dimensional profile data extraction from two-dimensional, optically measured surface heights data (surface: S2 with 0.8 mm cutoff)

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Fig. 4

Schematic of correlated roughness profile

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Fig. 5

Autocorrelation functions for five cutoff lengths (log–linear scale): (a) and (d); (b) and (e); and (c) and (f) are for surfaces S1, S2, and S3, respectively (see Table1)

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Fig. 6

Estimated correlation distances (log–log scale) (a) stylus-profilometry and (b) optical profilometry

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Fig. 7

PSDFs for five cutoff lengths (log–log scale): (a)–(d), (b)–(e), and (c)–(f) are for surfaces S1, S2, and S3, respectively (see Table 1)

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Fig. 8

Spectral consequences of correlation characteristics (m represents slope of power spectra in log10 scale)

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Fig. 9

Sample of linear fitting on PSDF in log–log scale (PSDF is of S2 measured under optical profilometer)

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Fig. 10

Bandwidth limited fractal characteristics: (a) fractal dimension, D and (b) fractal roughness, G (data shown in log10 scale where G reads in μm)

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Fig. 11

Standard deviation of asperity heights at different cutoff bands

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Fig. 12

Measure of average asperity curvature at different cutoff bands

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Fig. 13

Square root of fourth and sixth moments of power spectrum at different cutoffs (for optical profilometry data)

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Fig. 14

Measure of asperity density per unit area at different cutoff bands

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