Friction, Lubrication, and Wear Technology (1997) Part 6 pot

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Calibration Specimens Stylus Instruments Types, Calibration and Use of Specimens, ISO 5436- 1985, International Organization for Standardization, 1985 42. T.R. Thomas, Ed., Rough Surfaces, Longman, London, 1982, p 24-25 43. M.N.H. Damir, Error in Measurement Due to Stylus Kinematics, Wear, Vol 26, 1973, p 219 44. S. Ajioka, The Dynamic Response of Stylus, Bull. Jpn. Soc. Prec. Eng., Vol 1, 1966, p 228 45. J.I. McCool, Assessing the Effect of Stylus Tip Radius and Flight on Surface Topography Measurements, J. Tribology (Trans. ASME), Vol 202, 1984 46. E.C. Teague, "Evaluatio n, Revision, and Application of the NBS Stylus/Computer System for the Measurement of Surface Roughness," Tech. Note 902, National Bureau of Standards, 1976 47. J.C. Wyant, C.L. Koliopoulos, B. Bhushan, and O.E. George, An Optical Profilometer for Surface Characterization of Magnetic Media, ASLE Trans., Vol 27, 1984, p 101; and B. Bhushan, J.C. Wyant, and C.L. Koliopoulos, Measurement of Surface Topography of Magnetic Tapes by Mirau Interferometry, Appl. Opt., Vol 24, 1985, p 1489 48. T.V. Vorburger, "App endix A: Measurement Conditions and Sources of Uncertainty for NIST Roughness and Step Height Calibration Reports," unpublished 49. J. Hasing, Herstellung und Eigenschaften von Referenznormalen für das Einstellen von Oberflachenmessgeraten, Werkstattstechnik, Vol 55, 1965, p 380 50. J.F. Song, T.V. Vorburger, and P. Rubert, Comparison Between Precision Roughness Master Specimens and Their Electroformed Replicas, Prec. Eng., Vol 14, 1992, p 84 51. B.J. Griffiths, Manufacturing Surface Design and Monitoring for Performance, Surf. Topog., Vol 1, 1988, p 61 52. H.K. Tonshoff and E. Brinksmeier, Determination of the Mechanical and Thermal Influences on Machined Surfaces by Microhardness and Residual Stress Analysis, CIRP Ann., Vol 29 (No. 2), 1980, p 519 53. P.A. Willermet and S.K. Kandah, Wear Asymmetry A Comparison of the Wear Volumes of the Rotating and Stationary Balls in the Four-Ball Machine, ASLE Trans., Vol 26, 1982, p 173 54. I.M. Feng, A New Approach in Interpreting the Four-Ball Wear Results, Wear, Vol 5, 1962, p 275 55. E.P. Whitenton and P.J. Blau, A Comparison of Methods for Determining Wear Volumes and Surface Parameters of Spherically Tipped Sliders, Wear, Vol 124, 1988, p 291 56. E.P. Whitenton and D.E. Deckman, Measuring Matching Wear Scars on Balls and Flats, Surf. Topog., Vol 2, 1989, p 205 57. C.H. Bovington, Surface Finish and Engine Testing of Lubricants: An Industrialist View, Surf. Topog., Vol 1, 1988, p 483 58. E.J. Davis, P.J. Sullivan, and K.J. Stout, The Application of 3-D Topography to Engine Bore Surfaces, Surf. Topog., Vol 1, 1988, p 63; K.J. Stout, E.J. Davis, and P.J. Sullivan, Atlas of Machined Surfaces, Chapman and Hall, London, 1990 59. B. Bhushan, Tribology and Mechanics of Magnetic Storage Devices, Springer-Verlag, 1990 60. B. Bhushan, R.L. Bradshaw, and B.S. Sharma, Friction in Magnetic Tapes II: Role of Physical Properties, ASLE Trans., Vol 27, 1984, p 89 61. K. Miyoshi, D.H. Buckley, and B. Bhushan, "Friction and Morphology of Magnetic Tapes in Sliding Contact With Nickel- Zinc Ferrite," Technical Paper 2267, National Aeronautics and Space Administration, 1984 62. T.R. Thomas, C.F. Holmes, H.T. McAdams, and J.C. Bernard, Surface Microgeometry of Lip Seals Related to Their Performance, Paper J2, Proc. 7th Int. Conf. on Fluid Sealing, BHRA Fluid Engineering, Cranfield UK, 1975 63. T.R. Thomas and R.S. Sayles, Rough Surfaces, T.R. Thomas, Ed., Longman, London, 1982, p 231-233 Surface Topography and Image Analysis (Area) Eric P. Whitenton, National Institute of Standards and Technology Introduction SEVERAL CONCEPTS and methods involved in the topography and image analysis of engineered and worn surfaces are described in this article, in terms of the past, present, and future of this characterization technique. Although linear profilometry has long been used in materials research to study both machined and worn surfaces (Ref 1), there is typically more information about a surface in a scanned area profile (Ref 2). The fact that both computers and machines that can perform scanned topographical profile measurements over an entire area are becoming more powerful and less costly, combined with the scanned area profile advantage, represent two of several reasons why the techniques for analyzing profile information are becoming similar to techniques used for analyzing optical and scanning electron microscope (SEM) images in materials research. Image analysis of optical and SEM photomicrographs have been used for many years for various purposes related to materials science (Ref 3, 4, 5). Both optical and SEM images are essentially two-dimensional x,y arrays of numerical values. Each value represents the intensity of the image at that x,y location. Generally, area profiling machines also produce an x,y array, but each value represents a z height at that location. If intensity and z height are allowed to be interchangeable, where one can be substituted for the other, then the same equipment, techniques, and computer software can be used to analyze both. This simplifies the data analysis tasks of the researcher by unifying many of the techniques that must be learned. One machine that applies this approach uses much of the same hardware and software to interchangeably perform laser scanning tomography, infrared (IR) transmission photomicroscopy, and noncontact optical profilometry (Ref 6). Historically, the topographical analysis of machined surfaces has predominantly consisted of compiling statistics of geometrical properties, such as average slope or the root mean square (rms) of the z heights. The theory behind this is described in detail in the literature (Ref 1). Techniques like this have been of limited use in the characterization of worn surfaces, particularly those that are severely worn, but can be efficiently performed in an image analysis environment. Examples are given in this article. Image analysis is also becoming increasingly useful to pick out, characterize, manipulate, and classify the features on a surface individually, as well as in groups. It seems unlikely that purely statistical techniques will ever reach this level of sophistication. Investigators may soon see surfaces described in terms of the organizational structure of features, instead of rms. This article discusses a few of the potential pitfalls, capabilities, and opportunities of this evolving tool. A novel example of how image analysis and profiling are interrelated is in the measurement of pigment agglomeration in rubber (Ref 7). The standard procedure is to microtome the frozen rubber and examine it under an optical microscope. Using image analysis techniques, the darker-colored agglomerates are differentiated from the lighter-colored rubber, and the dispersion is computed. The researchers noticed that a stylus profile tracing of the rubber, sliced with a knife blade at room temperature, essentially yields a flat plane that has distinct holes and bumps. This is because the soft rubber "cuts" in a flat plane, whereas the harder agglomerates are not cut and protrude through the cutting plane. The number of peaks per unit area, a method long used in both image and profile analysis, is used to compute the dispersion. This method was judged to be very accurate and fast. Definitions and Conventions. Where possible, cited reference works were selected because they present techniques in "cookbook" form. It is hoped that this encourages readers to try such techniques on their own systems. A topographic image refers to an image where each x,y location represents a z height. This image is generally acquired by a scanning profiling machine. An intensity image refers to an image where each x,y location represents an intensity, and is normally obtained by SEM or video camera. A binary image is derived from either a topographic or an intensity image. Each x,y location has a value of either "0" or "1," indicating which locations in the original image have some property, such as z height above a threshold value or the edge of a feature as determined by local slopes. Some of the techniques discussed in this article are performed on binary images, which are described more fully in the section "Computing Differences Between Two Traces or Surfaces" and portrayed in Fig. 5. The word image, by itself, is intended to be very generic. It can refer to a topographic image, an intensity image, and, in certain circumstances, individual traces. A single trace is, in fact, the special case of an image with only one row of data. Note that what makes a topographic image different from an intensity image is simply the meaning of the value at each x and y, and not how it is displayed, or rendered. If an isometric line drawing of an intensity image is displayed, the image is still an intensity image, even though it "looks" as though it were a topographic surface. It should be remembered that all images are single-valued functions, which is to say that for any given x and y value, there is one and only one z value. The ramifications of this are discussed throughout this article. Motifswere the first profile analysis technique developed especially for use on computers (Ref 8). Using a set of four simple and easily understood rules, a complex trace can be reduced to a simpler one. This technique has been used in the French automotive industry for many years, and numerous practical uses have been found (Ref 8, 9, 10, 11). Currently, these rules only apply to a two-dimensional trace. If appropriate rules were discovered, this technique could also be performed on three-dimensional images. Surfaces are sometimes referred to as either deterministic, nondeterministic, or partially deterministic. A deterministic surface is a surface in which the z heights can be predicted if position on the surface is known. Sinusoidal (Ref 12) and step-height calibration blocks are examples. A nondeterministic surface has random z heights, such as a sand-blasted surface. Some surfaces have both a deterministic and a nondeterministic character. A ground surface often has a distinct, somewhat predictable, lay pattern with a random fine roughness superimposed on it. Such a surface is often termed partially deterministic. Leveling refers to the process of defining z = 0 for an image. For example, a single-profile trace is taken across a flat specimen. If one side of the specimen were higher than the other side, then the trace could be leveled by subtracting a line from that trace. For an engineered surface, the line would typically be determined by performing the least-squares fit of a line to all of the data in the trace. For a worn surface, where part of the trace includes the worn area and part includes the unworn area, only some of the data in the trace would be used to determine the least squares line. The data in the unworn area only would be used to determine the least-squares line when the worn volume, or wear scar depth, was to be determined. Implementation on Personal Computers and Data Bases. Both software (Ref 13, 14) and books (Ref 15, 16, 17, 18) have become readily available to perform image analysis on personal computers. At least one source (Ref 18) not only describes many of the techniques, but also includes software. If a profiling or other image-producing machine, such as a microscope, were under heavy use, then users could take a floppy disk containing the stored images to another work station and free the measuring equipment for others to use. Some data base programs allow images to be stored along with other textual and numeric information (Ref 19). It is also possible to have the images themselves as part of the querying process, where a user "enters" an image and the computer finds similar images (Ref 20). Thus, both the topography, or topographic image, and visual appearance, or intensity image, of a surface can be an integral part of a data base. Point Spacing and Image Compression The issue of how many x,y points to acquire in an image generally involves a compromise. If too few points are used, then valuable information can be lost. It has been shown, for example, that a surface with an exponential correlation function appears as a Gaussian correlation, unless there are at least ten data values per correlation length (Ref 21). The determination of even a simple parameter, such as rms roughness, is also affected (Ref 22, 23). When too many points are used, more mass storage and computing time per image are required than necessary. Also, the determination of noise- sensitive parameters can be adversely affected (Ref 24). This is because extremely fine point spacings may enhance the ability of the computer to record the noise in the profiling system, along with the topographic information. One solution is to acquire as many points as possible and later discard the redundant or unimportant values. There are a variety of image data-compression techniques that remove redundant or unimportant information when the image is stored in memory or disk. The best compression technique depends on which aspects of the image are redundant or not important to image quality. Several data-compression techniques have been proposed for surfaces of materials. One technique uses Fourier transforms (Ref 25, 26). By storing only the "important" frequencies, the amount of data can be reduced. The selection of which frequencies are not stored implies that features of that lateral size range can either be extremely small in vertical height, compared to other features, or are unimportant. Other procedures attempt to determine the "optimum" point spacing using autocorrelation functions (Ref 27), bandwidths (Ref 24), or information content (Ref 28). If variable point spacings are allowed, then motifs provide another technique (Ref 8). Many of the possible data- compression techniques do not appear to have been tried on images of surfaces of materials. Walsh or Hadamard transforms, where a surface is modeled as a series of rectangular waves, can be used in place of Fourier transforms. This often results in less noise in the reconstructed image, although Fourier transforms may better reproduce the original peak shape (Ref 26). Although there do not appear to be any references in the literature on usage as a data-compression technique specifically for the surfaces of materials, the coefficients have been used to characterize these surfaces (Ref 29, 30). Many other data-compression techniques are also available. Potential Pitfalls Many of the potential pitfalls in intensity image processing are potential pitfalls in topographic image processing as well. For example, when determining the roundness of an object, the number computed is dependent on the magnification used (Ref 31). A computed area or length also depends on the scale used, this being one of the basic concepts behind fractals, which are discussed in detail in the section "Fractals, Trees, and Future Investigations" in this article. Another pitfall is the fact that the surface is being modeled as a single-valued function in x and y, when it may in fact not be. One example is a case where a "chip" of material is curled over the side of a machined groove. There are at least three z heights: the top side of the curled chip, the underside of the curled chip, and the top surface of the bulk material below that chip. A profiling machine would report only the top side of the curled chip as the z height at that x,y location. Any estimate of volume would obviously be larger than the actual volume of material. Thus, an image of a surface is actually made up of only the highest points on the surface. A top view is the only truly accurate rendering of the image; other renderings, such as isometric or side views, are only approximations. This is because these other renderings give the appearance of "knowing" what is below those highest points. An analogous situation in intensity images is the "automatic tilt correction" on some SEMs (Ref 31). Suppose an intensity image of a sphere on a steeply sloped plane is acquired and that slope is removed in software so as to make the plane appear horizontal. A side view of this situation is shown in Fig. 1. When the software attempts to "level" the image, the radius of the sphere will be elongated in the direction of the tilt and remain constant in the orthogonal direction. The sphere will then appear as an ellipsoid, and not as a sphere. Fig. 1 Side view of a sphere on a sloped plane Estimation and Combination of Intensity and Topographic Images Simply displaying a topographic image as though it were an intensity image (which can be a very powerful tool) does not show the user how the surface would actually appear under a microscope. The heights are known, but the color, reflectivity, and translucency of the surface are not. Conversely, a microscope image gives clues as to the surface heights, but does not do so quantitatively. It may be obvious that a surface is pitted, for example, but the depth of those pits are not known. Three issues are therefore addressed: (1) The manipulation of an optical or SEM image to yield topographic information; (2) The rendering of topographic information that actually looks like the surface; (3) The combination of optical and topographic information together onto one rendering. Transforming an intensity image to a topographic image can be approached in several ways. All approaches involve a "nicely behaved" characteristic of the surface. One approach matches stereo pairs. Each feature in a left-eye image is matched to the same feature in a right-eye image. When the two images are compared, the amount of lateral displacement of each feature is related to its z height. Thus, a z height image can be created. The features must be distinct and well defined for this approach to work well. An example of this in use is in the measuring of integrated circuit patterns (Ref 32). Another approach assumes that the optical properties of the surface are relatively constant. If the original surface does not have this property, then a replica can be made and examined, instead. When properly lighted, each gray level in the intensity image is proportional to the slope of the surface at that location (Ref 33). The topographic image can therefore be found by integrating the intensity image. An example of a third approach is a wear scar on a ball. The volumes of such scars are often determined by measuring the scar width in an intensity image and assuming that the scar is relatively flat or of a fixed radius in z (Ref 34). However, the scars may be of unknown or varying radii. More accurate volume estimates can be obtained by outlining the edge of the worn scar and assuming the outlines are connected by lines or curves across that scar (Ref 35). This is shown in Fig. 2, where the surface has, in effect, been estimated from its intensity image and the known geometries in that image. Fig. 2 Example of estimating a topographic image from an intensity image using known geometries A nonrotating ball was slid repeatedly against abrasive paper in the y direction, forming a scar on the ball. An optical photomicrograph that looks down onto the scar was taken, digitized, and the intensity image was shown on the computer screen. The user then traced the outline of the scar using a pointing device. This is shown as the near-elliptical shape in Fig. 2(a). The software then assumed that the x,y location of the center of the scar coincided with the x,y coordinate of the center of the ball. Knowing the radius of the ball, the software then computed the z heights of all the x,y points on the outline of the scar, because they must lie on the sphere. To estimate the z values inside the scar outline, the values of the outline were connected by straight lines in the y direction, as shown in Fig. 2(b). Rendering and Combining Images. Actually transforming a topographic image to an intensity image is rarely done for surfaces of materials. The appearance of a surface under a microscope is typically approximated by simply rendering the topographic image as an isometric view. Isometric views can be generated by most image analysis software. The simplest isometric view is a stick-figure type of drawing, where no attempt is made to show how a light source would interact with the surface (Ref 18). These views may or may not have hidden lines removed. The next level of sophistication assumes that the optical properties are constant across the entire surface. One or more light sources are assigned locations in space, and the view is "shaded," giving a more realistic appearance. Some software takes into account the shadows that one feature casts onto another, whereas others do not. Often, however, the optical properties of real surfaces are not constant across the entire surface. Given optical properties maps of reflectivity, for example, some software can create very realistic renderings (Ref 36). An intensity image of a properly lighted surface can be used as a reflectivity map. Therefore, such software can be used to combine an intensity image and a topographic image of the same area to produce a rendering that exhibits both optical and topographic qualities of the surface. Relating Two- and Three-Dimensional Parameters Situations in which researchers have preferred the more traditional two-dimensional parameters have occurred. One example is the case where a large body of two-dimensional data has already been collected and there is a need to compare newly acquired data with previously obtained values. Even in these cases, the ability to select which two-dimensional trace to use for analysis from a three-dimensional topographic image is sometimes necessary (Ref 37). Additionally, the repetitive application of the analysis for a large number of traces can provide statistical information as to the repeatability of the results obtained for a given specimen (Ref 38, 39, 40, 41, 42). When applied to worn surfaces, a two-dimensional parameter can often be plotted as a function of sliding distance, giving clues as to the mechanisms involved (Ref 43). It is possible to estimate three-dimensional parameters from two orthogonal traces. This has been applied to mold surface finish (Ref 44) and has been used in the comparison of the fractal dimension (discussed later in this article) both with and across the lay of engineered surfaces (Ref 45). However, better results are often obtained from full images (Ref 46). Many of the customary two-dimensional parameters are easily extendable to three dimensions. Perhaps the best-studied parameters in both two and three dimensions are roughness parameters, such as rms values. Generally, two-dimensional roughness parameters have smaller values than their three-dimensional counterparts for nondeterministic surfaces, and have about equal values for deterministic surfaces. This result is derived from both theoretical work (Ref 1) and actual data (Ref 38). There are two explanations for this result. One is that single traces have a high probability of missing the highest peaks on a surface, whereas an area profile has a much better chance of taking these into account (Ref 1). Another explanation involves the fact that nondeterministic surfaces have waviness in both the x and y directions (Ref 47). Waviness in the x direction is generally removed by filtering for both the two- and three-dimensional roughness calculations. The two- dimensional calculation always removes waviness in the y direction, because each trace is leveled individually. The three- dimensional calculation, where the same plane is subtracted from all of the trees, does not do so unless a filter is specifically applied to the image in the y direction. Thus, the three-dimensional roughness parameter may or may not include the waviness in the y direction, depending on how the parameter is computed. When analyzing worn surfaces, some area profiling machines use the unworn part of a surface as a reference. This is done by fitting the unworn part of each trace to a line, and subtracting the line from that trace (Ref 41, 43). An example of this is shown in Fig. 3. Typically, this is performed because of drift problems while the traces are being acquired and to make the worn volume measurements more accurate. The effect is to filter the waviness in the y direction. One might therefore expect that a three-dimensional roughness parameter computed from this image would be more nearly equal to the two- dimensional equivalent than the same parameter applied to an image acquired by a machine that only uses its own reference plane. However, this does not appear to have been rigorously demonstrated. Fig. 3 An x, ,z coordinate image of the doughnut-shaped scar on the top ball in a four-ball test Figure 3(a) shows the "as traced" data. Note the vertical undulation of the surface. This is due primarily to mechanical errors in the motor stage used to hold the ball during image acquisition. For each trace, the unworn area can be fit to a line, and that line used to make the trace level with respect to the other traces. This is shown in Fig. 3(b). The relationships between the two- and three-dimensional values for other parameters are not as well documented as roughness. Other statistical parameters, such as skewness and kurtosis (which help characterize the distribution of z heights), have been computed for both engineered (Ref 42, 46) and worn (Ref 48) surfaces. Aspect ratio parameters have been proposed for circular wear scars (Ref 40) and for the features in worn areas (Ref 43). Fractal dimensions can also be determined in three dimensions (Ref 49, 50). It should be remembered that the values obtained for many two-dimensional parameters are often quite different, depending on the direction of the trace. Rms roughness (Ref 51), autocorrelation (Ref 52), and fractal dimension (Ref 45) are examples of this. Lessons from Two-Dimensional Analysis Example 1: Understanding How a Parameter Behaves. In the late 1970s, it was discovered that there is nearly the same linear relationship between the log of the wavelength and the log of the normalized power spectral density for a very large variety of surfaces (Ref 53). These surfaces span almost nine orders of magnitude in size. Values for motorways, concrete, grass runways, lava-flows, ship hulls, honed raceways, ground disks, ring-lapped balls, and other surfaces were used. An amazingly universal characteristic of real surfaces was discovered. Today, it is known that this occurs because these surfaces are fractal in nature (Ref 45). Imagine that a researcher does not know of this universality, but notices that this relationship exists for a particular set of surfaces. It might be tempting to assume that something was unique about these particular surfaces, when, in actuality, certain parameters behave in certain ways regardless of the type of surface. [...]... Engineering Surfaces, Wear, Vol 109, 19 86, p 181-193 E.P Whitenton and D.E Deckman, Measuring Matching Wear Scars on Balls and Flats, Surf Topogr., 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 Vol 2 (No 3), 1989, p 205-222 F George and S.J Radcliffe, Automated Wear Measurement on a Computerized Profilometer, Wear, Vol 83, 1982, p 327-337 E.J Davis, K.J Stout, and P.J Sullivan, The... 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Reddy, Different Functions and Computations for Surface Topography, Wear, Vol 83, 1982, p 203-214 68 Surface Texture (Surface Roughness, Waviness, and Lay), The American Society of Mechanical Engineers, ANSI/ASME B 46. 1, 1985 69 D.J Whitehouse and R.E Reason, The Equation of the Mean Line of Surface Texture found by an Electronic Wave Filter, Rank Taylor Hobson, 1 965 70 T.V Vorburger and J Raja, Surface Finish... which areas of the image are worn and which are unworn A binary image is shown in Fig 5(b) For each x,y location, the binary image has a value of 1 if that location is to be considered a part of the wear scar, and a value of 0, otherwise This binary image can then be used to "eliminate" parts of the original image and difference image that are not part of the wear scar, and should therefore not be considered... 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