A universal wear law for abrasion
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Wear 262 (2007) 883–888 A universal wear law for abrasion Matthew T. Siniawski a,∗ , Stephen J. Harris b , Qian Wang c aLoyola Marymount University, 1 LMU Drive, MS 8145, Los Angeles, CA 90045, USA b MD 3083, Ford Motor Company, Dearborn, MI 48121, USA c Northwestern University, Department of Mechanical Engineering, Evanston, IL 60208, USA Received 14 March 2006; received in revised form 4 August 2006; accepted 31 August 2006 Available online 2 October 2006 Abstract Finding a wear law that is valid over a wide range of conditions and materials would have enormous practical value. The authors have previously discovered a simple relationship describing the evolution of the abrasive wear rate of steel sliding against boron carbide-coated coupons, and have developed a model accounting for its kinetics. The authors show here that this wear equation accurately describes the evolution of abrasive wear rates for several additional material pairs and contact conditions that were tested, as well as for all of the material pairs for which literature data could be found. The only material parameters are the initial abrasiveness and the initial rate at which the abrasiveness changes with number of cycles. No other wear law so simple, accurate and widely applicable is known. © 2006 Elsevier B.V. All rights reserved. Keywords: Abrasive wear; Abrasion; Universal model 1. Introduction during a test, even when external conditions appear to remain constant. In 1962, Mulhearn and Samuels [3] presented a simple Mechanical and materials engineers have spent more than a model for the abrasion of steel sliding against silicon carbide century searching for mathematical models for the wear rates paper. Other, more detailed models have also been developed of materials that are both general and based on fundamental to predict the abrasive rates of materials under specific condi- principles. Finding a wear equation that can be trusted over a tions. For example, Lawn [4] proposed a model for wear of wide range of conditions and materials would have enormous brittle solids under fixed abrasive conditions. Sundararajan [5] practical value, allowing laboratory tests to substitute for expen- presented a model for two-body abrasive wear based on local- sive, potentially dangerous field tests and allowing for smaller ization of plastic deformation. More recently, Bull and Rickerby margins of safety to be designed into machinery and structures. [6] proposed a model of abrasive wear based upon multiple-pass Thus, it is not surprising that Meng and Ludema [1] found more scratching experiments. Because both the functional forms and than 300 proposed wear models and equations in the published even the parameters considered by these and other models differ, literature. However, such a large number is itself evidence that as a group they offer little guidance as to what are the control- this goal has been elusive. ling material and environmental parameters and what are the Perhaps the simplest and most widely used model for abra- fundamental relations among them. sive wear is that of Archard [2]. Its derivation is straightforward The reason that no abrasive wear equation proposed to date and intuitive, and it predicts the wear volume of an abraded has general validity may be that the abrasion process is intrin- material as a function of sliding distance in terms of a wear sically extraordinarily complex, with the process varying sub- coefficient, the applied normal load and the material hardness. stantially from one material pair to another and during a single Unfortunately, few systems obey this law over a wide range of test. The abrasion rate depends, among other factors, on the conditions [2]. For many systems the wear coefficient changes evolution of two surfaces, typically at the micro- or nano-scale [7]; on intimate details of how these two surfaces mate; and on the changing responses of each of the surfaces to that mating. ∗ Corresponding author. Tel.: +1 310 338 5849; fax: +1 310 338 2782. Scanning electron microscope (SEM) images in Fig. 1 show this E-mail address: msiniawski@lmu.edu (M.T. Siniawski). evolution for the surface of a boron carbide (B4 C) coating that 0043-1648/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.wear.2006.08.017
884 M.T. Siniawski et al. / Wear 262 (2007) 883–888 Fig. 1. SEM images of: (a) unworn and (b) worn B4 C coatings after sliding against 52100 steel for 500 cycles [8]. Evidence indicates that the steel chemically polishes the boron carbide even as the steel is mechanically polished by the much harder boron carbide [9]. has run against AISI 52100 steel [8]. The changes are profound, ical data illustrating this relationship. By differentiating Eq. (1), and the steel counter-surface shows equally dramatic changes. It the abrasion rate on any given cycle i is found as is unlikely that simple analytical models can capture the details of such processes, while finite element models of the contact Ai ∼ = (1 + β)A1 iβ . (2) mechanics cannot handle the complexity and three-dimensional Thus, measurements of the abrasion rate during the first few nature of the surfaces and the details of the evolution process. cycles, which determine A1 and β, allow prediction of the abra- Furthermore, a fundamental understanding of the abrasion pro- sion rate on any subsequent cycle (up to at least n = 20,000 cycles cess at the micrometer and nanometer scale is lacking. Therefore, in Fig. 2). β is a truly remarkable parameter. Experiments with a completely different approach is required. sputtered B4 C and DLC have shown that it does not change over at least tens of thousands of cycles, even with the dramatic 2. Model development changes in surface morphology seen in Fig. 1. It is independent of the friction coefficient, the surface finish and sliding speeds Recently, the authors discovered a simple relationship that between 1 and 20 cm/s, when the frictional heating is small. It describes the kinetics for the abrasion of 52100 steel ball bear- takes the same value for 52100 and SAE 1010 steel ball bearings. ings sliding against coupons coated with sputtered B4 C and β is even independent of the load, while A1 scales as (load)2/3 diamond-like carbon (DLC) in pin-on-disk (unidirectional dry [16]. sliding) experiments [7–17]. The relationship is Borodich, Harris and Keer [18] proposed a mathematical framework with which to treat the evolution of abrasive wear. It V (n) A(n) ≡ = A1 nβ , (1) was suggested that the key to understanding the origin of Eq. (1) d lies in treating the surface statistically rather than mechanically. where A(n) is the abrasion rate averaged over the first n cycles, V(n) the volume of steel removed from the ball during the first n cycles by the abrasion process, d = 2πrn the distance traveled by the steel ball over the coated disk at a wear track radius r and A1 is the abrasion rate (volume of steel removed/meters traveled) on the first cycle. The value of β controls the cycle- dependence (or time-dependence) of the abrasion rate. It is an empirical constant that satisfies −1 ≤ β < 0 for the com- monly encountered case where the abrasion rate falls with time. (Since the abrasion rate cannot fall below zero, the average abrasion rate A(n) cannot fall faster than inversely with n.) β = −1 corresponds to the case where all of the abrasiveness is lost after a single cycle, while β = 0 corresponds to the case where the abrasion rate is constant. β > 0 would apply to systems where the abrasion rate increases with time. Experiments have shown that β is approximately −0.8 for the sputtered B4 C–steel system. Fig. 2. Average abrasion rate A(n) as a function of cycles for 52100 steel sliding against B4 C coatings (black squares, dry sliding with a B4 C surface The simple relationship given by Eq. (1) holds in this system roughness Ra = 300 nm; white diamonds, dry sliding with a B4 C surface rough- for n ranging from 1 to at least 104 or 105 cycles and for A(n) ness Ra = 10 nm; white squares, lubricated sliding with B4 C surface roughness varying over more than 3 orders of magnitude. Fig. 2 gives typ- Ra = 300 nm).
M.T. Siniawski et al. / Wear 262 (2007) 883–888 885 In particular, it was assumed that: (1) asperities become dull and lose their abrasiveness during the wear process through a pro- cess that converts the relatively sharp abrasive asperities seen in Fig. 1a into dull, relatively less-abrasive regions such as the flat terraces seen in Fig. 1b. Asperities are classified as either sharp or dull. (2) The abrasion rate is proportional to the area density of sharp asperities. Thus, if li is the average distance between sharp asperities on the ith cycle, then the abrasion rate on that cycle is inversely proportional to li2 . The result is that changes in the abrasion rate during an experiment are determined by changes in a single variable, l. The particular form of the wear law then depends on the rule for how l evolves or, equivalently, the rate at which sharp asperities become dull. Eq. (1) is readily obtained if it is assumed that li /lj depends only on i/j, because a power-law function is the only solution to li /lj = f(i/j). This results from an assumption that Fig. 3. Average abrasion rate A(n) for B4 C coating with a surface roughness Ra = 300 nm against bronze (black squares) and 1100 aluminum (white squares). the distribution of sharp asperities on the surface remains self- The white diamonds are for the DLC coating run against 52100 steel. The lines similar throughout the abrasion process [18]. That is, a 2D map are linear least square fits to Eq. (1). showing the location of sharp asperities after n1 cycles would look, statistically, identical to a map showing the location of data for every material pair for which data could be found. Thus, sharp asperities after n2 cycles, except for a scale factor. it takes just two material constants, A1 and β, to predict the abra- An immediate consequence of this power-law form, as is sion rate for any load and after any number of cycles for any of clear from Fig. 2, is that the time (number of cycles) required to the material pairs for which data was found. Eq. (1) appears to achieve a given percentage change in l (abrasiveness) increases be a universal wear law for abrasion. as i becomes larger. A physical interpretation for this mathemat- ical result, based on Fig. 1, is that the most prominent asperities 3. Analysis and application wear down rapidly, while remaining asperities are more and more difficult to wear down because taller neighboring asper- Fig. 2 shows data for 52100 steel sliding against various ities and terraces shelter them. Thus, this sort of relationship B4 C coatings under a variety of conditions [11,13,15]. The is an almost automatic consequence of having a distribution of white squares represent experiments run under an unformulated asperity heights, a property of nearly any surface [19]. [Note that OBOA base Chevron mineral oil, with a viscosity of approxi- if it were instead assumed that li /lj = f(i − j), then f is an expo- mately 20 cSt at 40 ◦ C and approximately 4 cSt at 100 ◦ C. The nential function, and, like radioactive decay, the time required tests were conducted under boundary lubrication conditions, to achieve a given percentage change in l is independent of i. with a calculated minimum film thickness of 0.0138 m. The In fact, just such an exponential form has been reported for the presence of a lubricant strongly affects the abrasion rate, reduc- exceptional case of sandpaper [3], where all grains (asperities) ing it by around a factor of 3. Significantly, however, the data still are nominally of the same size. For this case there is no sheltering fits well to the functional form given in Eq. (1), and the slope β is effect, since all sharp asperities are equally exposed. As a result, hardly affected (compare to black squares). This result indicates, they have a constant probability of becoming dull on any given cycle. While the results from sandpaper can thus be explained within the context of the present model, it will be excluded from further discussion because of its idiosyncratic asperity height distribution.] The 2/3 power dependence of A1 on load that was observed comes out of this analysis in a natural way. It is fully consistent with Hertzian mechanics and with the predictions of Greenwood and Williamson’s statistical model for elastic con- tact of rough surfaces [19]. It is interesting to note that the same 2/3 exponent is predicted using an elastic statistical theory, even though cutting is clearly not elastic. Although the authors’ previous work demonstrated the valid- ity of Eq. (1) for dry and lubricated sliding of sputtered B4 C against steel and dry sliding of DLC coatings against steel, it had not been tested for other systems or conditions. This paper provides new experimental data showing its validity for other material pairs. These results are then combined with abrasion Fig. 4. Average abrasion rate A(n) for data obtained from the literature. The data from the literature that were recast in terms of Eq. (1). Eq. symbols are the experimental data and the lines are linear least square fits to Eq. (1) satisfactorily correlates both the authors’ and the literature (1) which correspond to the listed reference.
886 M.T. Siniawski et al. / Wear 262 (2007) 883–888 Table 1 Summary of the complete model input data and error results General material Worn Counterpart Contact A1 (mm3 /m) β RMS Model Reference category material material conditions deviation error (%) (%) Metal–coating 52100 steel B4 C coating 1 Ball-on-disc, dry sliding 2.24E−03 −0.7586 7.0 25 [11] 52100 steel B4 C coating 2 Ball-on-disc, dry sliding 2.83E−03 −0.8501 5.9 22 [13] 52100 steel B4 C coating 2 Ball-on-disc, lubricated 7.81E−04 −0.7678 11.3 29 [15] 52100 steel B4 C coating 3 Ball-on-disc, dry sliding 3.79E−05 −0.5696 5.0 27 [13] 52100 steel DLC coating Ball-on-disc, dry sliding 2.03E−03 −0.7712 4.0 27 [17] Bronze B4 C coating 1 Ball-on-disc, dry sliding 1.14E−02 −0.6922 8.4 28 – 1100 aluminum B4 C coating 1 Ball-on-disc, dry sliding 1.22E−02 −0.5002 10.0 32 – Coating–metal DLC coating Tungsten carbide Ball-on-disc, dry sliding, 1.27E−04 −0.4179 0.7 11 [20] 3.1%RH MoS2 coating Steel ball bearing Ball-on-disc, dry sliding 1.33E−06 0.0071 2.1 3 [21] (WTi)C–Ni coating Tool steel Block-on-ring, lubricated 9.54E−06 −0.0275 2.0 7 [36] Metal–metal CoCrMo CoCrMo Reciprocating, lubricated 2.97E−03 −0.1667 2.4 15 [24] CoCrMo CoCrMo Hip wear simulator, 1.14E−03 −0.3503 0.5 2 [25] lubricated ZnAlCuSi St 37 steel Rolling, lubricated 1.32E−01 −0.7339 2.7 10 [28] 7075-T6 aluminum Al2 O3 Ball-on-disc, dry sliding 4.94E−04 0.043 6.3 24 [29] 7075-T6 aluminum Al2 O3 Ball-on-disc, corrosive 4.01E−05 0.4122 6.3 22 [29] Al–8Fe–4Ce 440C stainless steel Crossed-cylinder rolling, dry 4.65E−03 −0.2491 3.4 13 [30] Al–13Si 440C stainless steel Crossed-cylinder rolling, dry 6.51E−03 −0.2156 3.3 17 [30] Zn–35A1 440C stainless steel Crossed-cylinder rolling, dry 1.40E−03 −0.0601 0.9 3 [30] Zn–35Al–Si 440C stainless steel Crossed-cylinder rolling, dry 2.02E−03 −0.1105 1.6 8 [30] Zn–35Al–3.75Si 440C stainless steel Crossed-cylinder rolling, dry 3.47E−03 −0.1777 8.8 3 [30] Zn–35Al–5.8Si 440C stainless steel Crossed-cylinder rolling, dry 2.03E−03 −0.1248 1.1 6 [30] A6061 MMC AISI 01 tool steel Pin-on-disc, dry sliding 1.90E−01 −0.4177 3.6 24 [31] Zn–40Al 4140 steel Block-on-ring, dry sliding 8.80E−07 0.0782 3.3 14 [32] Mg–9Al–0.9Zn (AZ91) 52100 steel Block-on-ring, dry sliding 3.68E−02 −0.0116 1.0 5 [33] Fe–25%TiC Steel Block-on-ring, dry sliding 1.87E−03 0.0739 0.9 3 [34] Ti–50.3 at%Ni Cr-steel Block-on-ring, dry sliding 2.11E−04 0.0361 2.9 9 [35] 2Crl3 Cr-steel Block-on-ring, dry sliding 2.72E−05 0.0174 2.6 11 [35] Copper Carbon steel Reciprocating, dry sliding 5.51E−02 −0.1629 4.2 16 [26] AISI1045 steel 52100 steel Pin-on-ring, dry sliding 6.73E−05 0.1495 0.8 4 [27] Ceramic–metal Si3 N4 Steel ball bearing Dry rolling 1.24E−04 −0.2156 8.2 7 [22] Ceramic–ceramic Si3 N4 (20 wt%HBN) Si3 N4 (20 wt%HBN) Pin-on-disc, sliding 7.64E−02 −0.7311 4.5 27 [23] somewhat surprisingly, that the rate of loss of relative abrasive- explain why diamond wears quickly when run against steel. The ness depends only slightly on the presence of a lubricant, at least smaller value of β for B4 C run against bronze and aluminum under these boundary lubrication conditions. (compared to steel) may then be due to the fact that there are Fig. 3 shows results for bronze and 1100 series aluminum no analogous chemical reactions between carbon and bronze sliding against B4 C, together with sample data for 52100 steel or aluminum which can cause the coating asperities to wear sliding against DLC. Comparing Figs. 2 and 3, it can be seen down. that the initial abrasion rate A1 of the softer materials is roughly The published literature is next used to explore the range one order of magnitude higher than that of steel, as might be of validity of Eq. (1). A total of 17 publications [20–36] were expected. There is also an effect on β, which is in the range −0.5 found in which experiments were described in sufficient detail to −0.7 for bronze and aluminum compared to −0.8 for B4 C. to be analyzed for this study. This literature data is largely, That is, the abrasiveness of the coating drops more slowly when but not exclusively from pin-on-disk, unidirectional systems. run against the softer materials. It seems intuitively reasonable Table 1 briefly describes the contact conditions of each exper- that softer materials should have a smaller impact on a hard iment. Additional details about the specific experimental setup abrasive coating, but the situation is actually more complex. For for each material pair are in the appropriate reference. After example, β takes the same value when B4 C is run against 52100 converting to the format presented in Fig. 2, a linear fit was cal- steel as when it is run against the much softer 1010 steel. It culated for each set of published data, with its quality evaluated would appear that the observed morphological changes (Fig. 1) by determining the root-mean-square (RMS) deviation between that lead to the loss of abrasiveness for B4 C and DLC are not the fit and the experimental data. The power-law relationship caused simply by mechanical processes. Instead, it is likely that accurately represents all of the experimental data, as the maxi- they are due to stress-induced chemical reactions between the mum RMS percent deviation in Table 1 is only about 10%, and carbon in these coatings and steel. Analogous reactions may in most cases the deviation is less than half that. Fig. 4 shows
M.T. Siniawski et al. / Wear 262 (2007) 883–888 887 This experimental result makes plausible the assumption that the surfaces remain self-similar. For B4 C–steel and DLC–steel systems, over the range of con- ditions considered, β is independent of load, number of cycles, friction coefficient, sliding speed, surface finish and the presence or absence of a lubricant. These remarkable results suggest that β is a fundamental property of each abrasion pair. The authors plan to explore the significance of β in future work. 4. Conclusions It was shown that a wear equation derived to account for abra- sion between boron carbide coatings and 52100 steel applies to all abrasive material pairs for which appropriate data are Fig. 5. Range of the wear model parameters. The black squares are for the available, including all available data from the literature, except metal–coating category, the grey squares are for the coating–metal category, the for sandpaper. This exception is believed due to the use of white squares are for the metal–metal category, the black diamond is for the monodisperse silicon carbide grains that are glued to the paper. ceramic–metal data set and the grey diamond is for the ceramic–ceramic data Otherwise, the only parameters required for any material pair set. The ellipses illustrate the approximate range of each category. are the initial abrasiveness and the initial rate at which the abra- siveness decreases with cycles, both of which can be readily the quality of the fits, where the numbers that correspond to the obtained from rapid, simple laboratory experiments. With these references identify the data. parameters, the abrasion rate at any point in the future can be β is negative for the majority of the cases, indicating that predicted with considerable accuracy. No other relationship so the abrasion rate usually decreases with time. This is interpreted simple, accurate and widely applicable as Eq. (1) is known to to indicate that the aerial density of sharp asperities decreases exist for the analysis of wear. with time. For two of the metal–metal cases, β is modestly pos- A key enabler for understanding abrasion is the recogni- itive, indicating that the abrasion rate increases with time. This tion that wear is too complex to treat from a purely mechan- increase in abrasion may be due to sharp wear debris particles ical or deterministic perspective. The combination of detailed created during the abrasion process. morphological information from SEM images together with a A wide range of values for A1 and β is evident from Table 1. statistical perspective has improved the understanding of the However, some relationship between them appears to exist kinetics of abrasion. It is expected that this sort of combined within each general material category, as illustrated in Fig. 5, mechanical–statistical approach will be a fruitful one for study- which plots β against A1 . The ellipses illustrate approximate ing other forms of wear. Further work is underway to understand ranges of various material combinations. According to Fig. 5, the material and environmental properties that determine β. β is generally small when A1 is large. In other words, highly abrasive material pairs tend to lose abrasiveness faster than less- Acknowledgements abrasive material pairs. The relative importance of chemical and physical mechanisms and any relationship to hardness have yet The authors would like to acknowledge the National Science to be elucidated. Foundation for funding for this research, as well as Mr. Wes These results show that the abrasion rate can be predicted Bredin for assistance with the literature review and Dr. Gordon with only two parameters, A1 and β. Some of the factors Krauss for experimental assistance. that control A1 are known. 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