Characterised copper-based alloys and the grain sizes and hardness for different production routes of nominal identical alloys.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Your researches are based in using forage conservation to improve animal performance associated with using adapted crops in semiarid regions. She is Associate Professor at Paraiba Federal University since 2008.",institutionString:"Federal University of Paraíba",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Federal University of Paraíba",institutionURL:null,country:{name:"Brazil"}}}],coeditorOne:{id:"139631",title:"Dr.",name:"Edson Mauro",middleName:null,surname:"Santos",slug:"edson-mauro-santos",fullName:"Edson Mauro Santos",profilePictureURL:"https://mts.intechopen.com/storage/users/139631/images/3204_n.jpg",biography:"Dr. Santos is a professor of Forage Crops and Pastures and Beef Cattle at the Federal University of Paraiba. 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These varistors, which are among the most non-linear discovered materials, are used in lightning arresters owing to their strongly non-linear characteristics I(V). (Figure 1).
\n\t\t\tCurrent versus Voltage characteristic in a ZnO-Based Varistor.
A varistor is a type of resistor with a significantly non-ohmic current-voltage characteristic. The name is a portmanteau of variable resistor, which is misleading since it is not continuously user-variable like a potentiometer or rheostat and is capacitor rather than resistor at low field. The most famous type of varistor is metal oxide varistor (MOV), which is also called as ZnO varistor. These varistors are used to protect circuits against excessive voltages. They have become more and more important during the past four decades due to their highly non-linear electrical characteristics and their large energy absorption capacity. They are normally connected in parallel with an electric device to protect it against the overvoltages. They contain a mass of zinc oxide grains in a matrix of other metal oxides sandwiched between two plasma sprayed metal electrodes. The ZnO grains have dimensions in the range of 10µm to 100µm. The boundaries between the grains form double potential barriers with Schottky junctions having conduction voltages in the range of 3.5V. The boundary between each grain and its neighbor forms a Zener-like diode junction. ZnO grains are separated by these “active” grain boundaries of nanometers thickness. Then the mass of randomly oriented grains is electrically equivalent to a network of back-to-back diode pairs, each pair in parallel with many other pairs. When a small or moderate voltage is applied across the electrodes, a small thermally activated reverse leakage current flows through the diode junctions. When a large voltage is applied, the diode junctions break down from the avalanche effect, and large current flows. The result of this behavior is a highly nonlinear current-voltage characteristic, in which the MOV has a high resistance at low voltages and a low resistance at high voltages.
\n\t\t\tThree regions can be distinguished in the current voltage characteristics of the ZnO varistor. At low voltages, the insulating barriers between the grains result in a very high and almost Ohmic resistivity, which is called the pre-breakdown or Ohmic region. At a certain voltage, called the threshold or breakdown voltage, the system enters the breakdown region in which the current increases abruptly, and the dependence of current on voltage is described by the empirical relation:
\n\t\t\tFrom which the parameter α is equal to:
\n\t\t\tThis parameter is a measure of the element nonlinearity, which varies with voltage. At higher current densities, the voltage starts to increase again resulting in an upturn region of the I-V characteristic. This voltage increase gradually becomes linear with current, i.e. Ohmic, and is associated with the resistivity of the ZnO grains, i.e. the voltage drop in the ZnO grains.
\n\t\t\tAmong their electric properties the most important ones are:
\n\t\t\tThe threshold voltage: It can be defined as the value of the voltage across the varistor, corresponding to a current of 1mA passing through it. From this voltage value, the varistor starts to change from the insulating state into the conducting state.
Energy capacity: It is the maximum capacity of the energy absorption of a varistor without any damage, while the discharge current due to an overvoltage passes through it.
The other properties (chemical, mechanical...) are closely related to the two properties quoted above.
\n\t\t\tWe have tried in our works to accomplish several statistical studies on these varistors, to find suitable ways to control their main characteristics such as the nonlinearity coefficient and conduction threshold voltage.
\n\t\t\tThese varistors are composed of zinc oxide and some other metal oxides, which provide the desired characteristics for these varistors. The microstructure of the varistor ceramics develops while sintering ZnO powder doped with small amounts of additives such as Bi2O3, Sb2O3, Mn3O4, Co3O4, Cr2O3 and others, at a temperature in the range of 1100 to 1300oC.
\n\t\t\tThe typical microstructure of a ZnO-based varistor is shown in figures 2 and 3. It is composed of ZnO matrix grains doped with Co, Mn, and Ni. These grains are n-type semi-conductors. Both Bi2O3-rich and Zn7Sb2O12 spinel phases are also usually present at the grain boundaries of the ZnO, but the presence of a Bi3Zn2Sb3O14 phase is possible as well. ZnO-ZnO grain boundary, rich of Bismuth, which is a highly resistive phase, is the main cause of the varistor effect, while spinel-ZnO junctions do not contribute to the nonlinear effect.
\n\t\t\tTypical microstructure of a ZnO varistor taken by electronic microscope. (ZnO=Zinc oxide grain, Bi=Bismuth, Sp=Spinel phase).
A Model of ZnO-ZnO grain boundaries in a zinc oxide varistor.
The size of ZnO grains (d in Figure 2) determines the number of ZnO grain boundaries between the electrodes of the varistor. As mentioned before, the typical breakdown voltage for a non-ohmic ZnO-ZnO grain boundary is around 3 Volts, and hence, the size of the grains determines the overall breakdown voltage of varistor and then the length of the varistor column in a lightning arrester.
\n\t\t\tSb2O3 is the standard additive for inhibiting the ZnO grain growth. The inhibition of ZnO grain growth is normally attributed to the existence of Zn7Sb2O12 spinel-type particles, formed during heat treatment in ZnO grain boundaries.
\n\t\t\tTo decrease dimensions of the varistors, and at the same time, to save the raw materials, many tests have been carried out on various formulations in order to apprehend an increase in the threshold electric field. There are many researches on this subject by adding additive oxides such as the lithium oxide, the magnesium oxide, the antimony oxide, etc. But one often runs up against the same difficulty; plus the threshold voltage is raised, plus the energy capacity decreases. For example when the threshold voltage is around 100V/mm, the capacity for energy absorption is in the order of 100 to 120 J/cm3, but when the threshold voltage is about 350 V/mm, the capacity for absorption of energy falls down to 30 J/cm3.
\n\t\t\tAnother type of varistor has been proposed, which is made by adding a certain percentage of some rare earth oxides such as praseodymium, Pr6O11, to the traditional composition. The analysis of the results of the electric characteristics of the various studied samples has made it possible to highlight a threshold voltage of about 300 to 400 V/mm, and a capacity for absorption of energy about 90 to 120 J/cm3. The increase in the height of potential barrier and the inhibition of the growth of the grains during the sintering cycle explain this physical phenomenon.
\n\t\t\tFor a high energy-absorption capacity, a micro-structural homogeneity (uniform ZnO grain-size distribution; uniform distribution of phases along the grain boundaries of ZnO; no or at least very little fine porosity) is required, that allows a uniform current and hence an energy distribution throughout the whole varistor.
\n\t\tNormally the varistors are prepared by traditional method used for electro-ceramics (Figure 4).
\n\t\t\tThe principal chemical formulation is made up of about 95% ZnO, plus Bi2O3, Sb2O3, Co2O3, MnO2, Cr2O3 and NiO as additives. All these oxides are mixed in a plastic earthenware jar with balls with zirconium, and pure ion-free water distilled during 24 hours. Rare earth oxide (Pr6O11 or Nd2O3) can be added too in the principal composition. The powder is obtained after drying and 150µm sifting. An appropriate dimension of the blocks to be made and tested in an experimental procedure can be 26mm in diameter and 2mm of thickness, but industrial varistors have much bigger dimensions, up to tens of centimeters as diameter and height. They are sintered during a period in the range of 2 hours. Electrodes are deposited on two surfaces of the samples to provide electric connections and to measure I(V) characteristics. These characteristics are measured while continuous currents up to 10mA pass through the samples, and impulses of great amplitude by using 4/10 and 8/20µs impulse generators up to tens of kA are applied.
\n\t\t\tTraditional procedure to manufacture a ZnO-based varistor.
Some examples for experimental composition of the samples are given in Table I. The composition S1 can be modified by the addition of small amounts of Pr6O11 or Nd2O3 to obtain the compositions S2 and S3, respectively.
\n\t\t\tSome experimental compositions of ZnO varistor samples.
Reagent-grade oxides are mixed in appropriate ratios for each composition, and disks are cold pressed at a pressure up to hundreds of MPa. Electrodes are coated on parallel surfaces of the sintered samples. Polished cross sections of the samples are prepared, and the microstructures of the samples are examined using Scanning Electron Microscope (SEM).
\n\t\tThe current versus voltage (I-V) characteristics of the varistor samples are measured using a dc power supply up to tens of mA, and a 4/10μs impulse generator up to 100 kA, to identify the upturn voltage and the current energy absorption capacity. Energy-absorption capacity (A) is the maximum amount of lightning energy absorbed and/or passed through a varistor when it explodes. To measure this capacity, impulse currents are applied to the samples with increasingly larger amplitudes.
\n\t\t\tThe current I(t) and the voltage V(t) waveforms are recorded with a storage oscilloscope. The energy absorption is calculated as follows.
\n\t\t\tThis parameter is calculated for all samples, and the average energy absorption is used to estimate the result. As an example, the electrical properties of some experimental varistor samples are given in Table II. As can be observed, the introduction of small amounts of rare earth oxides (REO) into composition S1, with a threshold voltage (V1mA) of 280V/mm, increased the threshold voltage of sample S2, doped with Pr6O11, and sample S3, doped with Nd2O3, to slightly above 300 V/mm. But what is more significant, is that doping with REO strongly increased the energy absorption capacity of samples S2 and S3 in comparison with sample S1, from 52 to 112 J/ cm3.
\n\t\t\tAverage Current-Voltage characteristics. Nonlinear coefficient (α), Threshold voltage (V1mA), Breakdown voltage per grain boundary (VGB), and Energy absorption capacity (A) of varistor samples.
Further investigation shows that when the Pr6O11 content increases, the threshold voltage increases as well but the coefficient of non-linearity α decreases.
\n\t\tThe microstructures of some investigated samples are presented in Figure 5. Phase composition and the distribution of phases in samples S1, S2, and S3 are evident from micrographs 5(a) to 5(f). The analyses confirms presence of the Zn7Sb2O12 spinel-type phase containing Cr, Mn, Co, and Ni and also Bi2O3-rich phases with Zn, Sb, Cr, Mn, Co, and Ni detected at the ZnO grain boundaries of all the samples.
\n\t\t\tWe can also see that the Bi2O3-rich phase, although present in all samples and more noticeable in sample S3. The analysis also confirms the presence of the Bi3Zn2Sb3O14 (called as pyrochlore-type phase), at the grain boundaries of ZnO in sample S1. In samples S2 and S3, a new phase is determined containing oxides of Pr and Nd, respectively.
\n\t\t\tImages from SEM of microstructures of varistor samples sintered at 12000C: (a) S1; (b)S1(etched), (c) S2; (d) S2(etched); (e) S3; (f) S3(etched). Key: Z=ZnO phase; B=Bi2O3-rich phase; S=Zn7Sb2O12 spinel-type phase; PY=Bi3Zn2Sb3O14 (pyrochlore-type) phase; Pr=Pr-containing phase; Nd=Nd-containing phase; P=pore.
The Pr-containing phase in particular is relatively fine-grained and distributed everywhere along the grain boundaries of the ZnO, while the grains of Nd-containing phase are larger and localized. While the grains of spinel phase are large in sample S1, they are significantly smaller in samples S2 and S3. The formation of significant spinels in compositions with large amounts of added Sb2O3 has been observed by different researchers.
\n\t\t\tThus micro-structural observations show a strong influence of REO doping on the ZnO and spinel grains, which is clearly evident from the micrographs in Figure 5. Doping of the composition with Pr6O11 results in a significant decrease in the ZnO grain size and doping with both Pr6O11 and Nd2O3 has a similar effect on the spinel phase; the size of spinel grains. Average size of the different phases of the varistor specimens are given in Table III.
\n\t\t\tAverage size D (μm) of ZnO grains, spinel grains, and pores of varistor samples S1, S2, and S3 with corresponding deviations δ (μm).
These observations indicate that doping with REO has a strong influence on the mechanism of formation of the Zn7Sb2O12 spinel phase. There are many reports in the literature about the formation of the spinel phase in the ZnO-Bi2O3-Sb2O3-based varistor compositions. Depending on the Sb2O3 / Bi2O3 ratio, the spinel phase forms either by the direct reaction of Sb2O3 with ZnO or by the decomposition of the Bi3Zn2Sb3O14 (named as pyrochlore phase) according to the following reactions:
\n\t\t\tThe increase in threshold voltage (V1mA) can be ascribed to the smaller ZnO grain size. However, the increase in V1mA is much smaller than could be expected from the large decrease in the ZnO grain size and suggests that the increase in the number of non-ohmic grain boundaries is not proportional to the increase of all ZnO-ZnO grain boundaries due to the smaller ZnO grain size in this sample. It is evident from Table II that the average breakdown voltage of the grain boundary (VGB) in sample S1 is 1.9 V, while in sample S2 it is only 1.5 V. Sample S3 also has a significantly higher V1mA than sample S1, despite the fact that it has a larger ZnO grain size than sample S1. The VGB in sample S3 is 2.5 V which indicates that a larger fraction of grain boundaries in this sample has a non-ohmic character. Sample S3 also has a higher nonlinear coefficient α of 52 than samples S1 and S2 with α equal to 40.
\n\t\t\tDoping with REO significantly improves the energy characteristics of samples. The low energy-absorption capacity of sample S1 can be attributed to the large amount of spinel phase in this sample.
\n\t\t\tThe spinel phase forms large grains along the grain boundaries of ZnO, and so insulating chains of spinel phase significantly interrupt the current flow by narrowing the effective conduction section of the varistor. This leads to current localization and local overload, and hence a low energy-absorption capacity due to a non-uniform energy distribution.
\n\t\t\tThe analysis of the whole results obtained during the tests of the samples, manufactured with various percentages of praseodymium and neodymium oxides makes it possible to suggest that:
\n\t\t\tThe presence of rare earth oxides improves the homogenization of the size of the grains in material.
The increase of potential barrier height in the grain boundary supports a rise in the threshold electric field of the varistor.
A good capacity of energy absorption is resulted compared to the traditional varistors.
Doping with Pr6O11 and Nd2O3 appears to be promising for the preparation of ZnO-based varistors with a high breakdown voltage and also high energy-absorption capacity. This can be a successful step because our objective is to have smaller and lighter surge arresters in power network. This aim involves such varistors, which have high conduction threshold voltage, while their energy absorption capacity remains enough high. In such conditions we will be able to use a smaller number of varistors to make a high-voltage arrester. Consequently this will provide smaller and lighter arresters.
\n\t\t\tOf course we have to respect the necessary outer creepage distance of the arrester housing, according to the pollution level of the location where the arrester is to be used.
\n\t\tDifferent computational methods are used for investigation of the non-linear behaviour of zinc-oxide varistors. In a ZnO varistor, when a voltage is applied between the electrodes, the majority of the grain boundaries show a strong non-linear behavior, but a certain number of grains do not present, under the applied voltage, a high non-linear characteristic or are nonconducting. Under a known voltage level, several current paths occur from one electrode to the other, which are called as the current percolation paths. The number of grains on each path crossing by the current is a statistical parameter. It is shown that the distribution of this statistical number depend on the block thickness and percentage of nonconducting grains in the varistor.
\n\t\t\tUsing a Monte Carlo method in our research works, we have realized that the number of ZnO grains providing the percolation path fits a lognormal distribution especially in thin varistors. We have also proposed a binomial direct approach for this problem. It is found that the direct approach could be satisfying too.
\n\t\t\tBoth approaches show that the threshold voltage and the nonlinearity coefficient of the varistors can be controlled, to some degree, by the fraction of nonconducting grains. These results help us to have a better understanding of the varistors’ behavior and enable us to make more realistic electric models for these elements.
\n\t\t\tFew works can be found, which have experimentally studied the individual grain boundaries in the varistor. Most of the Schottky junctions give a nonlinearity coefficient which is normally in the range of 30-70 for a normal varistor, whereas the actual α of a good ZnO material junction can be in the range of 150. Even it can attain values greater than 200 in certain grain to grain microvaristors. Figure 6 shows the typical variation of the current density as a function of the barrier voltage, for a single barrier in a varistor.
\n\t\t\tThe grain boundary current density vs. grain boundary voltage.
In Figure 7 the variation of the non-linearity coefficient α as a function of the varistor barrier voltage, for a single potential barrier is observed. This curve is deduced computationally, using Maple software, from the slope of the current-voltage characteristic of a single grain boundary as in Figure 6.
\n\t\t\tNon-linearity coefficient α as a function of the varistor barrier voltage, for a single potential barrier.
In Figure 8 a simplified model of the varistor\'s microstructure is observed. We use this model for computer simulation.
\n\t\t\t\tIf the ZnO element thickness is D and the average grain thickness is d, then the minimum number of grain boundaries between the electrodes is L=D/d.
\n\t\t\tAs we read in the literature, the Monte Carlo is a technique that provides approximate solutions to problems expressed mathematically. Using random numbers and trial and error, it repeatedly calculates the equations to arrive at a solution. Then using random numbers or more often pseudo-random numbers, as opposed to deterministic algorithms, uses this algorithm for solving various kinds of computational problems.
\n\t\t\t\tMonte Carlo methods are extremely important in computational physics and related applied fields. Interestingly, the Monte Carlo method does not require truly random numbers to be useful. Much of the most useful techniques use deterministic, pseudo-random sequences, making it easy to test and re-run simulations. The only quality usually necessary to make good simulations is for the pseudo-random sequence to appear "random enough" in a certain sense. They must either be uniformly distributed or follow another desired distribution when a large enough number of elements of the sequence are considered. Because of the repetition of algorithms and the large number of calculations involved, Monte Carlo is a method suited to calculation using a computer, utilizing many techniques of computer simulation.
\n\t\t\t\tSimplified micro-structural model of varistor for computer simulation.
Using a Monte Carlo algorithm, we follow a stochastic procedure to compute the number of the conducting grains on the current path in the varistor model as a statistical parameter. The flowchart of the used program is observed in Figure 9. In this diagram the letters K and N denote, respectively, the iteration number and the variable for the number of each layer in micro-structural model of the varistor. B is the number of active grain boundaries through which the current passes in going from one electrode to the other. As well, we define the probability of a non-conducting grain boundary as P. For P=0, all grain boundaries are always active. It is obvious that the existence of non-conducting grains results in a longer path for current across the ZnO element, which depends on the fraction of non-conducting grains. We undertake a statistical analysis of the effect of L (the number of ZnO grain layers across the varistor) and P (the probability of a non-conducting grain boundary) on the nonlinear characteristics of the varistor as characterized by α.
\n\t\t\t\tFlowchart of Monte Carlo algorithm, for computation of the number of grains on the current path through the varistor.
As said above, for P=0, there is no non-conducting grains and all path lengths are the same, equal to L. With increasing fraction of non-conducting grain boundaries P, the conducting grains number B, augments substantially, which will increase the voltage per unit thickness of the ZnO element. P can also be augmented by increasing the amount of non-conducting inter-grain material, often as a by-product of attempting to reduce grain size. This non-conducting phase can be a spinel phase. Obviously increasing the number of non-conducting grain boundaries increases the current density in the remaining grain boundaries and results in greater grain boundary power dissipation and temperature rise.
\n\t\t\t\tBy running the Monte Carlo program with different values of L, the number of ZnO grain layers across the varistor, and P, the probability of non-conducting grain boundaries in varistor, we obtain statistical sets of data for B, i.e. the number of grains crossed by the current.
\n\t\t\t\tAs an example, a probability density histogram of B\'s data for the case of a very thin varistor with L=10 and P=0.3 is seen in Figure 10, which is related to a varistor of about 0.1 mm thick.
\n\t\t\t\tA probability density histogram of the number of grains crossed by the current, obtained for special case of a very thin varistor of about 0.1 mm thick with probability of non-conducting grains equal to 30%.
Analyzing the statistical distribution of B by fitting different distribution curves on it, several distributions such as Normal, Lognormal, Weibull, Logistic, Loglogistic and Exponential were used for fitting our computational data. The best fitness was seen to be for the three distributions of Normal, Weibull and Lognormal (identically for LogeNormal and Log10Normal), comparing to the others.
\n\t\t\t\tIn Figure 11 we can observe the fitted curves for these three distributions concerning the special case of Figure 10.
\n\t\t\t\tThe Anderson-Darling statistic is a measure of how far the plot points fall from the fitted line in a probability plot. Using the Anderson-Darling measure to calculate the fit goodness of these distributions, we obtain the curves of Figure 12.
\n\t\t\t\tThe statistic is a weighted squared distance from the plot points to the fitted line with larger weights in the tails of the distribution. In this method, a smaller Anderson-Darling (AD) measure indicates that the distribution fits the data better.
\n\t\t\t\tAs can be observed in Figure 12, the LogNormal distribution has the best fit for the B data concerning the thin varistors of this study.
\n\t\t\t\tIf the probability of a grain boundary to be non-conducting is P and as we supposed in our model that the grains are cubes, then it can be shown that to a first approximation, the mean number of active grain boundaries through which the current passes between electrodes is:
\n\t\t\t\t Curve fitting of the number of grains crossed by the current, on three different distributions for the data of
Comparing the fitness of three different distributions on our data, concerning varistors of 0.1 to 1 mm thick.
And as we realized in our research work that the percolation number data for thin varistors obey the lognormal distribution, we deduce, using the Maple software, the relation (8) as an analytical formula for the standard deviation, s, of thin varistors data, having the lognormal distribution.
\n\t\t\t\tBy plotting this equation for different values of L and P in Maple software, we obtain Figure 13. As it is seen, the standard deviation is not high for amounts of P less than 0.5, while it is great for bigger P’s in thinner varistor blocks.
\n\t\t\t\tStandard Deviation for percolation number B of thin varistors, having lognormal distribution.
We assumed that each current path is independent of every other path. In fact, at large P, the number of non-conducting grain boundaries would reduce the likelihood of interconnection of paths. But in P less than 0.5, especially in thin varistors even if two paths of differing length are near each other, the probability of their having substantially differing potentials is not great.
\n\t\t\t\tWe realize from the form of the statistical distribution for B, that as P increases, the varistor conduction turn-on will be more rapid. This can be deduced from the low B tail of the statistical distribution. For small L, the average value of B increases with P, but the minimum value of B, which is L, remains the same. Thus the ratio of the mean to minimum possible value of B increases. The turn-on characteristics are determined mainly by the first few paths to conduct. Thus the number or fraction of completed paths for various P must be considered in addition to α.
\n\t\t\t\t\n\t\t\t\t\tFigure 14 compares lognormal and Normal distributions with the same mean (200) and with variances selected to give the same minimum value (~100) in a population of 600 random numbers. This figure indicates that the probability density of B increases much more rapidly at low values of B for the lognormal than for the Normal distribution. Thus conditions, which drive the statistical distribution for B toward the lognormal distribution, are likely to result in more rapid turn-on of the varistor element. The lognormal distribution also has a long tail at high values, which will cause a long tail in α.
\n\t\t\t\tComparison of Lognormal (black) and Normal distribution data with same mean (200) and with variances set to give about the same minimum value (100) in a population of 600.
Based on the numerical computations and the distributions thereof, we believe that the more rapid turn-on as a function of increased P for large L (thick elements) is probably associated with a transition from a Normal distribution at P=0 toward a lognormal distribution with increasing P.
\n\t\t\t\tThis transition can be rationalized from the probability density of B for a thin varistor with a reasonable probability of non-conducting grains, for which the distribution is clearly asymmetric with a rapid turn-on, when the shortest path across the arrester becomes conducting, followed by a rapid increase in the number of conducting paths with increasing voltage.
\n\t\t\t\tThe Lognormal distribution increases more rapidly in the low end tail of the distribution which would result in a more rapid turn-on of an arrester element.
\n\t\t\t\tIn Figure 15, the variation of α as a function of the applied voltage is seen for a non-conducting grain probability 0.5 for a thick varistor of 10 mm thickness (L=1000) and a thin varistor of 0.5 mm (L=50) thickness.
\n\t\t\t\tBoth cases result in asymmetrical α characteristics, while the varistor thickness has an obvious influence on the shape of the curve. We accomplished the same analysis for varistors with different thickness (Number of grain layers L) and probability of non-conducting grains (P).
\n\t\t\t\tVaristor nonlinearity coefficient α as a function of the applied voltage for thin and thick varistors at a non-conducting grain probability of 0.5.
To provide a basis for comparison of the α(V) curves, we define the parameters FWHH and β as measures for broadness and rate of rise of the α(V) curve (Figure 16). We define FWHH as the Full Width at Half Height of the curve and β is defined as:
\n\t\t\t\tAccording to this definition for β, the small β means large slope of α(V) curve.
\n\t\t\t\tIn Figure17, we see the variation of the (FWHH) of the α(V) curves as a function of L, which is linear as might be expected.
\n\t\t\t\tDefinition of parameters FWHH and β.
Full Width at Half Height (FWHH) of the α(
For P=0 and large L, the characteristics are just a multiple of the grain boundary characteristics which are modeled as symmetric. As the fraction of non-conducting grains increases, the mean percolation path increases and the probability of a short percolation path decreases.
\n\t\t\t\tHowever the minimum possible path remains the same (L) and above the minimum path, the number of conducting paths appears to increase rapidly which results in an asymmetric α with more rapid turn-on.
\n\t\t\t\tAs both the threshold voltage and width of α are the sum of the contribution from each grain, i.e. a 10 mm thick varistor (L=1000) is equal to twenty 0.5 mm thick (L=50) varistors in series, so that the I(V) curve of the former will be sum of the I(V) curves of the latter. Thus FWHH(L) should increase linearly with L, and the result of Figure 16 can be taken as a verification of the computational methods.
\n\t\t\t\t\n\t\t\t\t\tFigure 18 shows the variation of β with non-conducting grain probability (P) for a thin (L=50) and a thick (L=1000) ZnO element. As we can see in this figure, the rate of rise of the α(V) curve depends on both the non-conducting grain probability and the element thickness.
\n\t\t\t\tVariation of β as a function of P, for L=50 (0.5 mm thick) and L=1000 (10 mm thick) varistors. Smaller β tends to indicate more rapid “turn-on” of the varistor with applied voltage.
For P>0.5 and large L (thick varistor), β remains constant, which means that increasing P has little effect on the varistor turn-on characteristics. This is probably a result of the fact that for large L, the standard deviation in B decreases as a fraction of L, so that the extreme value in B decreases relative to the mean.
\n\t\t\t\tWith increasing P, the mean number of grain boundaries increases, as does the distribution of the number of grain boundaries through which the current passes from one electrode to the other. As the grain boundary characteristic is highly nonlinear, the conductivity of the ZnO element rises rapidly once the first few current paths become conductive. This probably accounts for the increasing asymmetry in α for the whole varistor, and increasingly rapid current onset with increasing P, as the statistical distribution of path lengths broadens with P.
\n\t\t\t\tFor large L, β decreases with increasing conducting grain probability, P, but for small L this is not the case. As well, the peak value of α increases with increased P for thick elements but not for thin elements. This must result from competition between the larger variance in B, the percolation path for small L, with the nature of the tail of the statistical distribution at low values of B, which determines the turn-on characteristics.
\n\t\t\t\tOne would prefer the ZnO “turn on” (become substantially conductive) to be very rapidly so that the AC operating voltage can approach more closely the protection level of the varistor without causing excessive power dissipation. On the other hand, how the varistor approaches its ultimate conductivity with voltage is less important. As we observed, the probability, P, of non-conducting varistor grains has an influence on parameters such as the rate of rise of α(V) curve.
\n\t\t\t\tAs a conclusion, we say that the characteristics of the thin ZnO varistors were statistically studied. The number of ZnO grains on each conducting path through a ZnO varistor, crossing by the current, is a statistical parameter (B).
\n\t\t\t\tIt was shown that the nonlinearity of ZnO ceramics can be controlled, to some degree, by the fraction of non-conducting grains. Thus we can choose the best value for P to have the maximum rate of rise of the α(V) curve. This will result in a rapid “turn on” of the ZnO element, which allows the circuit being protected to operate more closely to the protection level without excessive power dissipation in the arrester element. This optimum value of P certainly depends L, which is related to the thickness of the varistor.
\n\t\t\t\tWith increasing fraction of non-conducting grain boundaries P, the percolation number B, increases substantially, which will increase the voltage per unit thickness of the ZnO element. This can be exploited commercially in order to increase the percolation number.
\n\t\t\t\tThese results can help us to have a better understanding of the behavior of these varistors, and the dependence of this behavior on their geometrical dimensions and the constituting materials. This will also enable us to have more realistic electric models for these ceramic elements.
\n\t\t\tWe propose also that the Binomial distribution can be used directly to explain the conduction phenomena in ZnO varistors. Here is a Maple program using the Binomial Distribution for computation of the current in the varistor and calculation of its α, to predict directly the turn-on characteristics. We use the Binomial Distribution formula to calculate the probability function of the L success in B trials (B=Percolation Number & L=Number of Layers).
\n\t\t\t\tFor example we consider a varistor block with a diameter of 4cm (Dvaristor = 0.04).
\n\t\t\t\tThen we calculate the number of grains in the first layer, from which a current can be started, as follows:
\n\t\t\t\tThe number of the expected conducting grains just next to the upper electrode is:
\n\t\t\t\tNow we compute the probability for the current to advance L layers in crossing B grains, from one electrode to the other, for a given L & P (in the example here L=100 and P=0.3):
\n\t\t\t\tNow we can have the number of current paths in the varistor as a function of B, i.e., we know that how many paths there are for each B:
\n\t\t\t\tHere we plot the number of the conduction paths, in a varistor with 100 layers and 30% of non-conducting grains, as a function of the percolation number B:
\n\t\t\t\tVariation of P as a function of
Now we compute the current per grain boundary, using the relation of J(VG) for one single grain boundary :
\n\t\t\t\tThen we substitute VG by V/B, as the total voltage V which is applied on the whole varistor is distributed on B grains en series :
\n\t\t\t\tIn L trials, the mean number of cases without a non-conducting grain is L(1-P) and the mean number with a non-conducting grain is L.P. We can ignore the cases, which are "dead ends", as they do not count:
\n\t\t\t\tSince these are the cases which move us forward. Thus for a probability of a non-conducting grain, P, the mean percolation number is:
\n\t\t\t\tBM is the number of active grain boundaries through which the current passes between electrodes.
\n\t\t\t\tNow we compute the current flowing per each single path, from which the total current through the varistor can be obtained as follows:
\n\t\t\t\tThis is because the total current will be the sum of the number of paths multiplied by the current in each path. Using this second formula for calculating the current in whole range of B, the α will be obtained. We substitute V by V+1 in IV to obtain the derivative and calculate the Alpha as follows:
\n\t\t\t\tFrom Figure 20, it is obvious that how asymmetric the α(V) curve is. For large L, β decreases with increasing conducting grain probability, P, but for small L this is not the case. As well the peak value of α increases with increased P for thick elements but not for thin elements. This must result from competition between the larger variance in B, the percolation path for small L, with the nature of the tail of the statistical distribution at low values of B, which determines the turn-on characteristics. Previous works indicated the statistical distribution of the percolation number, B, for large L (thick varistors) is Gaussian. We used the Monte Carlo computations for thin varistors (L<100). For L<100, histograms of percolation number fit a Lognormal distribution better than a Normal distribution. We realize from the form of the statistical distribution for the percolation number, B, that as P increases, the turn-on will be more rapid. This can be deduced from the low B tail of the statistical distribution. For small L, the average value of B (percolation number) increases with P, but the minimum value of B, which is L, remains the same.
\n\t\t\t\tVariation of α as a function of
4 OPEN "D:\\Mohammad\\B data\\L200_1000. K5000. P 0.5\\L200_K5000_P0.5.DAT" FOR APPEND AS #1
\n\t\t\t\t\'P = Probability of nonconducting grain
\n\t\t\t\t5 P = 0.5
\n\t\t\t\t\'LMAX = Number of the layers across the varistor
\n\t\t\t\t6 LMAX = 200
\n\t\t\t\t\'KMAX = Number of iterations (Current injection to the upper electrode)
\n\t\t\t\t7 KMAX = 5000
\n\t\t\t\t10 B = 0 : L = 0: K = 0
\n\t\t\t\t15 B = 0 : L = 0
\n\t\t\t\t20 R = RND(1)
\n\t\t\t\t30 IF R > P THEN GOSUB 100
\n\t\t\t\t40 IF P >= R AND R > P^5 THEN GOSUB 200
\n\t\t\t\t50 IF R <= P^5 THEN GOSUB 300
\n\t\t\t\t60 IF L >= LMAX THEN GOSUB 400
\n\t\t\t\t70 IF K >= KMAX THEN GOTO 450
\n\t\t\t\t80 GOTO 20
\n\t\t\t\t100 B = B + 1
\n\t\t\t\t110 L = L + 1
\n\t\t\t\t120 RETURN
\n\t\t\t\t200 B = B + 1
\n\t\t\t\t210 RETURN
\n\t\t\t\t300 B = B - 1
\n\t\t\t\t310 L = L - 1
\n\t\t\t\t320 IF L <= 0 THEN GOTO 15
\n\t\t\t\t330 R1 = RND(2)
\n\t\t\t\t340 IF R1 > P ^ 4 THEN B = B + 1
\n\t\t\t\t350 IF R1 <= P ^ 4 THEN GOTO 300
\n\t\t\t\t360 RETURN
\n\t\t\t\t400 PRINT #1, B
\n\t\t\t\t410 K = K + 1
\n\t\t\t\t420 PRINT B, L, K
\n\t\t\t\t430 B = 0: L = 0
\n\t\t\t\t440 RETURN
\n\t\t\t\t450 STOP
\n\t\t\t\t500 END
\n\t\t\trestart;
\n\t\t\t\tB:=L*(1+sum(P^n, n=1..infinity)); # B=The mean number ofactive grain boundaries through which the current passesbetween electrodes.
\n\t\t\t\ts1:=(log(B) - (1/L)*(log(B))) / (sqrt(L));
\n\t\t\t\ts := (((exp(1))^(s1^2))*(((exp(1))^(s1^2))-1))^(1/2);
\n\t\t\t\treadlib(log10);
\n\t\t\t\tplot3d(s,P=0.1..0.9, L= 10..1000,axes=boxed, title="Standard Deviation = f(P & L), P=Nonconductivity Probability, L=Number of varistor grain layers");
\n\t\t\t\tJ:=10^((tanh(50*log10(VG)-28))*4.5-5.5)*10^((500*log10(VG)+1000)/450)*10^4;
\n\t\t\t\tplot([log10(VG),log10(J),VG=.1..10]);
\n\t\t\t\tJ1:=subs(VG=VG+.01,J);
\n\t\t\t\talpha_grain := ((log10(evalf(J1)*10^(-2))-log10(evalf(J)*10^(-2)))/(log10(VG+.01)-log10(VG)),VG=2.8..4.5);
\n\t\t\t\tplot(alpha_grain, title="Alpha of a ZnO grain versus Grain Boundary Voltage");VG:=(V/N);
\n\t\t\t\tJJLN:=sum(N*J*(sqrt(s*2*Pi))^(-1)*(exp(-1/2*((log(N)-B)/sqrt(s))^2)),N=round(B-5*sqrt(s))..round(B+5*sqrt(s))); L:=20; P:=0.1; JJLN;
\n\t\t\t\tJJ1LN:=subs(V=V+1,JJLN);
\n\t\t\t\tALN201:=(log10(evalf(JJ1LN)*10^(-2))-log10(evalf(JJLN)*10^(-2)))/(log10(V+1)-log10(V));
\n\t\t\t\tP; L; plot(ALN201,V=0..1000, title="Alpha LogNormal of the ZnO varistor versus the Applied Voltage");
\n\t\t\t\tB:=L1*(1+sum(P1^n, n=1..infinity));
\n\t\t\t\tsn:=B*sum(P1^n,n=1..infinity);readlib(log10);
\n\t\t\t\tJ:=10^((tanh(50*log10(VG)-28))*4.5-5.5)*10^((500*log10(VG)+1000)/450)*10^4;
\n\t\t\t\tJ1:=subs(VG=VG+.01,J);VG:=(V/N); J;
\n\t\t\t\tJJN:=sum(J*(sqrt(sn*2*Pi))^(-1)*(exp(-1/2*((N-B)/sqrt(sn))^2)),N=round(B5*sqrt(sn))..round(B+5*sqrt(sn)));L1:=20; P1:=0.1;JJN; JJN1:=subs(V=V+1,JJN);
\n\t\t\t\tAN201:=(log10(evalf(JJN1)*10^(-2))-log10(evalf(JJN)*10^(-2)))/(log10(V+1)-log10(V));
\n\t\t\t\tP1; L1; plot(AN201,V=0..1000, title="Alpha of the Normal ZnO varistor versus the Applied Voltage");
\n\t\t\tP;L;plot([ALN201,AN201], V=40..120, color=[red,green ], style=[line]);
\n\t\tInnovative technologies that enable production of new materials and complex composites unfortunately do not find their way into engineering application. This is due to a widespread, fundamental lack of trust in new materials or materials produced via non-conventional production processes [1]. With growing awareness for limited energy and environmental resources, paired with cost aspects, innovative near net shape technologies gain interest [2]. The effects of these processing technologies on microstructural details and furthermore on friction and wear are not sufficiently understood to predict material behaviour in a sliding contact to regulate wear rates and or frictional levels. Apart from classical structural mechanical properties, these tribological characteristics have to be known for a proper design with a new material.
\nIn the present chapter two alternative production routes are evaluated for three copper based alloys. The near net shape technologies metal (powder) injection moulding (MIM) and lost foam casting (LF) are described in part 2, both are known for steel [3] and aluminium [4] but have not been commercialised for copper-based alloys and lack substantial basic knowledge in published literature. Yet, near net shape technologies are especially interesting for copper-based alloys due to the high raw metal costs. Here, a special focus is put on the characterisation of wear and friction in a lubricated sliding contact of representative alloys shown in part 3.1. The chosen experimental set-up is depicted in part 3.2 and their tribological and analytical results in part 4. Based on the observations of a formed tribolayer and the nano-crystalline zones forming a tribologically transformed layer (TTL) as described in part 4. Part 5 forms the discussion that links the findings to literature and suggests a hypothesis for the formation of tribolayer and TTL and their effect on wear and friction levels. Finally, the chapter finishes with a conclusion in part 6.
\nCopper alloys are materials with a good track record for tribological applications comprising pronounced sliding. However, the demand of alloys is limited by high raw material costs and traditional energy intensive production routes; such as melt metallurgy, casting, hot and/or cold forming; the subsequent machining processes lead to large amounts of chips that have to be collected and re-melted to be recycled. As consequence, the production of components made of copper-based alloys demand excessive energy and consequently result in high ecological impact and costs.
\nEnergy-efficient technologies offer economical production methods and additionally large options for complex component shapes. Currently, additive manufacturing and near net shape manufacturing are the main avenues towards achieving these goals. Figure 1 depicts a schematic of the processes involved: MIM and LF production technologies; the basic principles are described in [5]. Such innovative production technologies usually result in different material properties compared to traditional production methods; the functionality of the component produced by alternative processes are often perceived to be inferior. For a more widespread application of new production technologies, a prediction of the target properties would be necessary. Since a correlation between the material structure and the resulting properties are not trivial, the material selection process is still mainly based on empirical knowledge of engineers. For tribologically loaded components the material selection usually relays on bulk material properties and the assumption that the harder material is the more wear resistant, which is conformed in many cases. This material rating is based on the well-known Archard’s wear law [6], which correlates wear volume inversely with hardness.
\nTechnological paths of the analysed materials.
Tribological studies in the nineties, have revealed that ductile alloys form an altered, fine-grained microstructure in the volume just beneath the sliding contact area as published by Rigney in [7]. Following classical approaches of material hardening, the material increases in strength as the grain size decreases and according to Archard’s wear law this results in lower wear.
\nMost published tribological sliding experiments studying material changes were carried out on pure metals, such as Au or Cu, in a ball on plate configuration in dry contact [8, 9, 10]. They show the formation of a microstructure with much finer grain sizes compared to the initial grain size, which is only preserved in the bulk. Some studies can be found on alloys, e.g. steel [11, 12] or Co-based alloys [13, 14], which revealed grain refinement beneath the surface, too. Recent tribological studies focus on the evolution of microstructural changes such as [9, 13], but correlations of grain-size to properties e. g. strength or work-hardening are treated by material science studies: Studies on work-hardening behaviour due to grain size changes, as described for copper by [15], do not deal with grain refinement processes exhibited by tribological contacts. Besides, the strength increasing effect of refinement according to Hall–Petch relations was reported to apply until a gain size of approximately 10 nm is reached for copper [16].
\nUsually, superior wear resistance is attributed to those newly formed structures [13, 14], but often no explicit correlation to wear or friction is given [9]. Thus, the effect of microstructural changes in subsurface regions in a sliding contact on wear and also friction levels is not really understood and often underrated. Classical material selection strategies follow the idea that material with higher strength exhibit higher wear resistance.
\nThe current study compares three copper-based alloys, each produced via two different process technologies – one conventional technology route via casting, forming and machining and a near net shape new technology. The alloys are listed in Table 1 together with their hardness values and the range of grain sizes observed in the cross sections. The microstructures resulting from different production routes are shown in Figure 2. The wrought alloys CuSn8 and CuNi9Sn6 are typically continuously cast, followed by forming (pressing, drawing, rolling) and, for CuNi9Sn6, heat treatment through spinodal decomposition [17]. Apart from the spinodally forming precipitations, which cannot be observed in SEM images, this alloy also forms γ precipitations (in different crystallographic structures as described in [18]) that are several μm large and are visible in SEM pictures. In light microscopy images, their effects on the microstructure can be observed by grain boundary faceting as well as formation of violet lamellar phases during annealing.
\nMaterial | \nTechnological route | \nGrain size | \nHardness | \n
---|---|---|---|
CuSn8 conv. | \nContinuous cast, drawing, heat treatment, chipping | \n< 20 μm | \n238 HV1 | \n
CuSn8 MIM | \nInjection moulding, debindering, sintering | \n100–150 μm | \n70 HV10 | \n
CuNi9Sn6 conv. | \nContinuous cast, rolling, heat treatment, chipping | \n~ 50/150 μm | \n180 HV1 | \n
CuNi9Sn6 MIM | \nInjection moulding, debindering, sintering | \n~ 200 μm | \n171 HV10 | \n
CuNiSn6 MIM, heat treated 1 h@450°C | \nInjection moulding, debindering, sintering, heat treatment | \n~ 200 μm | \n266 HV1 | \n
CuSn12Ni2 conv. | \nContinuous cast, chipping | \n~ 50 μm | \n115 HB30 2.5 | \n
Cuni12Ni2 LF | \nLost foam cast, chipping | \nDendrite length > 500 μm | \n90 HB30 2.5 | \n
Characterised copper-based alloys and the grain sizes and hardness for different production routes of nominal identical alloys.
Microstructures of studied alloys: Conventional: a) CuSn8 c) CuNi9Sn6 e) CuSn12Ni2; MIM: b) CuSn8, d) CuNi9SN6; LF: f) CuSN12Ni2.
Alternatively, these two alloys can be produced by a metal injection moulding (MIM) process from pre-alloyed feedstocks. The MIM production route inherently lacks a forming step and thus any possibility for grain refinement. As a result, the average grain size of the materials produced through MIM is much coarser than the one obtained via conventional routes. Figure 2 shows representative microstructures of all studied materials. The MIM version of CuNi9SN6 can be heat treated in the same way as the conventional version in order to increase the mechanical strength by precipitation hardening.
\nContinuous casting (CC) followed by machining is a typical production route for the cast alloy CuSn12Ni2. An alternative, innovative production route, which can help to save energy is a sand casting process called lost foam (LF) casting [4]. The dendritic structure resulting from LF casting is pronouncedly coarser than the CC variant as it is basically a sand-casting process with correspondingly long cooling times and lacking deformation during solidification.
\nAll production variants of the advanced technologies, MIM and LF, lead to larger grain sizes with hardly any defects such as twins. Therefore, these material variants are significantly softer than their counterparts from the conventional production route. As often higher wear resistance is associated with high mechanical strength, the small-grained, conventionally produced alloys are expected to exhibit better wear resistance. This assumption, which is a commonly used basis for design and material choices in mechanical engineering, is verified in the current study for three alloys and two innovative production routes.
\nSamples of the different alloy variants were studied in a lubricated reciprocal sliding contact with a modified SRV® test rig. The setup is described in more detail in [19, 20, 21]. Table 2 summarises the main test conditions. Each set of parameters was repeated at least twice. The base oil SN150 was used for CuSn8 and CuNi9Sn6, for CuSn12Ni2 a fully formulated mineral oil, a commercial gear oil, was applied. Thus, one has to be aware of the effect of different viscosities. In order to increase wear of the fully formulated system, CuSn12Ni2 was also examined at a normal load of 240 N. These external conditions differ because the potential applications of the two technologies are different.
\nCylinder | \nDisc | \nStroke | \nFrequency | \nNormal force | \nTest duration | \nSliding distance | \nLubrication | \nDisc temperature | \n
---|---|---|---|---|---|---|---|---|
mm | \nmm | \nmm | \nHz | \nN | \nh | \nm | \n— | \n°C | \n
∅ 4.2 l = 12 | \n∅ 24 l = 7.8 | \n3 | \n30 | \n100 or 240 | \n2 | \n1296 | \noil drop | \n50 | \n
SRV test parameters.
The friction coefficients were continuously recorded at a rate of 1 Hz and the data of individual test runs were averaged. Wear scars were characterised after the tests by topographic analysis using confocal microscopy with a Leica DCM3D at 20× magnification, which allowed the measurement of the wear track width. Afterwards, the wear volume was calculated as described in [20].
\nA wear map is used to illustrate the performance of the individual materials and their variants. It shows the wear volume measured at the end of the test run versus the coefficient of friction at a test time of 90 min, which corresponds to SRV standards [22]. This kind of diagram enables a simple but informative tribological rating, as both friction and wear behaviour are usually relevant. The desired low friction sliding material with a high lifetime can be found in the left lower corner.
\nIn order to understand modification processes deriving from tribological interactions selected samples were investigated further with light microscopy, nanoindentation, scanning electron microscopy (SEM) and focused ion beam (FIB) cross sections or electron back scatter diffraction (EBSD). EBSD was performed on cross sections normal to the sliding plane with a Zeiss Supra 40VP instrument equipped with an EBSD system from EDAX. The samples were chemically etched prior to the scan. The step sizes (0.20 μm for CuSn8 MIM; 0.10 μm for conventional CuSn8) and the scan areas (100 × 250 μm/19 × 30 μm) were adapted to the grain sizes of the respective samples. Scan sizes of 200 nm were chosen for CuSn8 MIM and 100 nm for conventional CuSn8. The position of the scan along the contact area was pre-selected in SEM images. Three scans were performed on a total area large enough to be representative for the largest grain sizes found in CuSn8 MIM. For microstructures like the conventionally produced CuSn8 a scan directly at the surface was not possible due to the high local deformation. A sufficient Kikuchi pattern quality was only detectable in a depth of 1 μm below the surface. The scans were analyses based on pattern quality images overlaid with the small (SAGB) and large angle grain boundaries (LAGB) as well as on the inverse pole figure (IPF) pictures with the projection direction <100>, which is the surface-normal direction to the plane of the cross section. Numerous precipitations in the MIM version of CuNi9Sn6 complicated the pattern recognition and therefore EBSD scans were omitted. FIB cuts were employed for the deeper analysis of the cast alloy CuSn12Ni2. The FIB cuts were oriented normal to the sliding direction as a cut in the sliding direction would either be located on top or at the bottom of a groove produced by wear debris.
\n\nFigures 3 and 4 show the frictional behaviour over test time for all studied materials. All friction coefficient curves – except CuSn8 – are at their maximum during running-in, which ranges from 5 to 30 min. All curves end in an almost constant steady level for the chosen test duration and have thus reached steady-state conditions.
\nFriction coefficient of CuSn8 and CuNi9Sn6 both conventionally produced and via the MIM route.
Friction coefficient of CuSn12Ni2 produced via conventional casting (CC) and via lost foam cast (LF) for 100 N and 240 N normal load.
The observed friction coefficient values lie between 0.20 and 0.42. These values represent the arithmetic mean of the friction coefficients of different test runs. The data of each run was averaged for each recorded friction value, these mean values are shown as the friction curves in Figures 3 and 4. In order to add the information of the scattering of the individual runs for a better interpretation of the friction behaviour, the standard deviation of the friction signal of each run was calculated. The error bars in Figures 3 and 4 represent the mean standard deviations of individual test runs with the same material, shown only every 5 min for the sake of readability,
\nThe levels of the friction coefficients as well as the friction behaviour differ distinctly for the two materials CuSn8 and CuNi9Sn6. Their sensitivity to variations in the process route seems to be pronouncedly different, as well.
\nFor both materials, MIM manufacturing leads to lower friction coefficients after the run-in phase. For CuSn8 the reduction is roughly 0.08, for CuNi9Sn6 it is less pronounced, namely 0.02–0.03. A heat treatment, which increases the structural strength of the CuNi9Sn6 MIM variant, further reduces the friction coefficient by about 0.07 compared to the conventionally produced CuNi9Sn6.
\nThe MIM variant of CuSn8 shows a high variability throughout the whole test time and after an initial increase to 0.40 smoothly decreases to 0.30 between 30 min and 100 min. The values of the conventional CuSn8 lie within the variability of the MIM variant, but do not show the same characteristics over time. After an initial increase, the friction reaches an almost constant level at 0.40, which is on the upper bound of the scatter of the CuSn8 MIM data. As, the noise of conventional CuSn8 is much smaller than the MIM sample, the different levels of friction of the two CuSn8 variants are regarded as significant.
\nThe error bars largely overlap for the conventional and the MIM version of CuNi9Sn6, indicating just a small tendency for higher average friction levels for the conventional variant. However, the run-in period is distinctly different with a pronounced increase for the MIM version, but lasting only 30 min, whereas the conventional material increases and decreases smoothly, reaching a steady state level just before the test ends. The characteristics of the friction curve over time seems to be unaffected by the microstructural changes during heat treatment, as there is again a steep but short increase during run-in. However, the scatter of the friction data is reduced by the heat treatment.
\nThe averaged friction values at a test time of 90 min are depicted in Figure 5 together with the averaged wear volume at the end of the test in a wear map. The error bars of the coefficient of friction are equivalent to those in the friction curves (Figure 3), the uncertainty of the wear volume is the standard deviation of the wear volume at the end of the test. The measured uncertainty is in some cases so small that it is nearly invisible in an appropriately scaled wear map.
\nWear map of CuSn8 and CuNi9Sn6 in two production variants: Conventional and MIM. The depicted friction coefficient was taken at a test time of 90 min and the error bars are identical to those depicted in
The results illustrate a pronounced decrease of wear when the CuSn8 is produced via MIM instead of conventionally. There is also a reduction in wear for CuNi9Sn6 when comparing conventional to MIM in the heat-treated condition, but this decrease is far less pronounced. Between MIM and conventional samples there is no significant change in wear results.
\nFor CuSn12Ni2 the friction coefficient levels are lower, ranging between 0.11 and 0.14 (Figure 4). This can mainly be attributed to the fully formulated gear oil used in this study, which was chosen because the tribosystem should be as close to the real application as possible. Therefore, a direct comparison between all the materials discussed before is not permissible. The differences in terms of friction between continuous and lost foam cast variant are little and judged to be irrelevant for applications. Still, the level of coefficient of friction is significantly lower for LF if the contact pressures are increased. Nevertheless, none of the two variants seems to be more sensitive to normal pressure changes than the other. More pronounced differences can be observed in the wear behaviour (Figure 6) of CuSn12Ni2, where the LF microstructure shows significantly lower wear volume.
\nWear map of CuSn12Ni2 produced via conventional casting (CC) and via lost foam cast (LF). The depicted friction coefficient was taken at test time of 90 min and the error bars are identical to those depicted in
For all investigated materials, the innovative production routes lead to lower wear despite weaker mechanical properties, certainly the degree of improvement depends on the alloy, Figures 5 and 6. There are large differences in the final wear volume between the two production routes for CuSn8, which exceeds by far the measurement uncertainty of wear. For CuNi9Sn6 the effect of the production route is much smaller but the heat-treated MIM version shows a distinct wear reduction compared to the conventionally produced sample. Consequently, the increase in mechanical strength due to precipitations does not impair the tribological performance, as it does reduce wear and friction levels. Heat treatment reduced the scatter of the observed friction coefficient level. The results of other samples treated at other temperatures or for different times are not shown here as the wear and friction results nearly coincide for all established heat treatment cycles. In any case, the heat-treated MIM CuNi9Sn6 version offers the lowest wear and friction among the investigated materials lubricated with mineral base oil. The initial microstructure affects the measured wear volume even within systems using additivated oils, which reduces the measured wear volumes due to a tribolayer, forming also in bronze surfaces. Again, the new casting technology (LF) variant of CuSn12Ni2 shows a better performance than the continuous cast one. The effect of different material structures becomes more pronounced for higher normal loads as can be seen in Figure 6 for 100 N and 240 N. The effects on friction levels are lower than for the systems shown in Figure 3, but interestingly the noisiness of the friction coefficient reduces if the structure gets coarse and more dendritic. The latter observation seems to be independent of the applied normal load.
\nThe higher wear resistance of softer variants of the respective alloys was initially not expected as hardness is often associated with wear resistance. As the testing conditions were identical, we investigated the microstructure in order to examine the different wear behaviour observed. The idea was that the grain and/or defect structure evolving during the sliding contact is different for these samples. As all other factors were identical, the near surface microstructure behaviour can potentially explain the differences in the macroscopic wear behaviour as well as the observed frictional levels. The light microscopy images in Figure 7 reveal that no resolvable changes in the microstructure beneath the contact surface occur. Only the MIM CuSn8 sample exhibits local increase of twins and shear bands in the first layer of grains up to a depth of 20 μm beneath the contact plane.
\nLight microscopy images of cross sections of the cylindrical samples normal to the sliding direction. a) CuSn8 conventional b) CuSn8 MIM c) CuNi9Sn6 conventional d) CuNi9Sn6 MIM; the 50 μm scale applies to a) and b), the 100 μm scale to c) and d).
The LF cast structure does show defects in the light microscopy images (Figure 8), such as bent slip lines as well as deformation twins, which are bent additionally. The deformation is very inhomogeneous and many dendrites show no or hardly any changes visible in light microscopy images. The details in Figure 8b illustrate pronounced effects in the vicinity of pores. If features of plastic deformation can be observed by light microscopy images, they will reach up to depths of at least 50 μm.
\nLight microscopy images of cross sections of the cylindrical CuSn12Ni2 LF samples normal to the sliding direction a) and b) show two position within the same sample, in a) slip lines and in b) deformation twins in the first row of grains are visible.
The higher resolution SEM images of the cross sections shown in Figure 7 are given in Figure 9a and b for the conventional variants and in Figure 9c and d for the MIM samples. The CuSn8 is highly twinned and some twins are bent in sliding direction. This occurs not only in the surface grains but also in grains located in deeper zones. Still, the SEM picture is not sufficient to be able to judge if these features are caused by the sliding contact as the initial structure is highly twinned already. In the higher alloyed CuNi9Sn6 the grains are larger compared to CuSn8 conventional and no features indicating deformation close to the surface are visible. The edge is very rounded due to polishing and etching prior to EBSD measurements.
\nSEM image of the edge zone on cross-sections oriented parallel to the sliding direction: a) CuSn8 conventional, b) CuNi9Sn6 conventional, c) CuSn8 MIM (mechanically mixed layer, 2 visible nano-indenter marks), CuNi9Sn6MIM (mechanically mixed layer lying and facetted grain boundary with γ precipitations is vertically inclined on the left side of the image).
In contrast to the two conventionally produced samples, both MIM alloys revealed the formation of a layer on top of the initial microstructure with a thickness up to 2–3 μm. This feature was only observed on the MIM microstructures of the studied alloys.
\nThe observed layer seemed to have a patch-like structure as it did not cover the whole length of the cross section. On top views of the contact area these layers were not clearly distinguishable from the rest of the microstructure, thus their size and extent of coverage cannot be given. The EDX measurements (Table 3) shows increased contents of oxygen and carbon and some iron. This indicates a mixture with debris from the counter-body, even though the structure appears amorphous or extremely fine-grained. However, no grain structure was resolvable with SEM. What is observable in the SEM images are pores within this layer, which are more pronounced for the layer observed on CuNi9Sn6. In the following, we refer to it as a mechanically mixed layer in order to distinguish it from the TTL, a zone comprising of defects like the slip lines and deformation twins extending over up to 20 μm as seen in Figure 7c.
\n\n | C | \nO | \nSi | \nS | \nSn | \nFe | \nNi | \nCu | \n
---|---|---|---|---|---|---|---|---|
At % | \n54.58 | \n6.16 | \n1.07 | \n1.90 | \n0.62 | \n0.31 | \n3.33 | \n32.03 | \n
EDX analysis of the layer observed on CuNi9Sn6 in the MIM version as shown in Figure 9c.
With the EBSD technique grain orientations and local misorientations can be revealed. Thus, microstructural defects like internal stresses stored in grains or the formation of subgrains and local misorientations can be detected, which cannot be observed with SEM images [23, 24]. As the changes in wear volume were most pronounced for CuSn8, the EBSD analysis focused on this alloy.
\nFor the conventionally produced CuSn8 the forming in the production process resulted in a highly deformed and small-grained structure and any further deformation caused due to the tribological contact is not clearly visible in the SEM images. The pattern quality picture and the IPF picture of an EBSD scan with 1 μm distance to the surface are given in Figure 10, with the upper edge of the scan in sliding direction. The quantitative analysis of the grain boundary lengths of large angle grain boundaries (LAGB) and small angle grain boundaries (SAGB) overlaid onto the pattern quality image indicate no increase closer to the surface and so they are not shown. However, the twin density within the first 15–20 μm appears to be higher as numerous micro-twins form. Especially in the first 5 μm, the grains are strongly elongated parallel to the sliding direction. Generally, the grain size is easier to see in IPF images and they reveal high intragranular strains by different shades of colours in nearly all grains, independently of their location with respect to the surface. Hence, internal lattice rotations do not seem to increase close to the contact but are a result of the high deformation during production. Comparing the IPF image of the initial structure to the one at the surface indicates a tendency to slightly smaller grains sizes in the first 15 μm. However, the change in size is far less pronounced than the change in shape.
\nCuSn8 conventional samples with the sliding direction is parallel to the upper edge. EBSD scan at a depth of 1000 μm beneath the surface: a) pattern quality image with LAGB in blue, SAGB in red and b) IPF image EBSD scan at a depth of 1 μm beneath the surface: c) pattern quality image with LAGB in blue, SAGB in red d) IPF image.
Thus, the microstructure of conventional CuSn8 can hardly adapt to the stresses accumulated during sliding and has little work-hardening capability. So presumably the microstructure is just worn off soon after the initial contact during the sliding process and cannot form a wear resistant subsurface zone.
\nThe MIM version of CuSn8 was very large grained and therefore investigated by two closely situated EBSD scans situated close together, shown in Figure 11. The initially undeformed MIM structure of CuSn8 shows some grains like the green coloured ones the left with no deformation features, but the grain on the right changes its orientation from yellow to pink which indicates a partial lattice rotation and an internal stress field. The respective stress field does not extend over the whole grain but is localised in the first 20 μm beneath the contact and does not affect the twin. In the middle, there is a pore close to the surface and the remaining grain located above this pore seems to be most suitable for storing plastic strain generated by the tribological sliding process. It has various shades of pink and is not as uniformly coloured as non-affected grains. The analysis of small- and large-angle grain boundaries showed no additional information and a figure was therefore omitted. Based on our observations there is no indication for an interrelationship between local lattice rotations and the position of the mechanically mixed layer lying on top of the tribo-contact area.
\nCuSn8 MIM EBSD scan.
In addition to SEM and EBSD scans, some apparently affected grains were investigated with nanoindentation, the results of which are given in Figure 12. The defect structure shows a high local concentration of defects that lead to an increase in hardness by 1–1.5 GPa over the first 20 μm beneath the surface. However, grains that show no orientation changes in the EBSD scan also showed no hardening in the nanoindenter measurement.
\nLocation and gradient of the nano-hardness measurements performed on the MIM version of CuSn8.
The modulus of elasticity was quantified during nanoindentation and turned out not to vary with distance from the contact surface and is thus not given in Figure 12. Altogether, the results of the MIM sample suggest that if grains are modified due to the sliding process they have to be located directly at the surface, grains further away from the contact are unaffected in any case, but even at the surface many grains do not adapt by e.g. lattice rotations.
\nThe cast alloy CuSn12Ni2, though tested with additivated oil, showed smaller but significant differences in the wear volumes of CC and LF cast samples. The tribo-surfaces had different characteristics, which are illustrated in Figure 13a and b. The surface exhibits grooves, presumably formed by wear debris, but resembles a metallographic cross-section as it clearly reveals all phases. The LF surface appears even smoother than the CC surface. As the conventional cross sections indicated some edge effects, but did not enable a proper identification of the structure, only the respective FIB cuts, normal to the sliding direction, are given in Figure 13c and d. Here samples tested at a normal load of 240 N are examined as the wear volume differences are larger than for 100 N normal load. Both samples form a nanocrystalline layer during the sliding process. However, the thickness of this layer is clearly larger for the CC version with about 8 μm compared to 1.2 μm for LF. As the CC samples had higher wear volumes, this layer does not seem to have wear protective effect, at least under the conditions investigated here. The nanocrystalline layer is not homogeneously thick, but rather shows a wavy interface in both studied cast structures. The average grain size within the nanocrystalline layer with about 250 nm seems to be constant over the thickness and in both samples. Potentially there are even smaller grains, but they cannot properly be resolved in SEM pictures.
\nWear tracks on CuSn12Ni2: Top view SEM image - sliding direction going parallel to the vertical image edge a) conventional sample, b) LF sample; sub-surface microstructure in field ion image mode - sliding direction normal to the FIB cutting plane, c) conventional sample d) LF sample.
In lubricated reciprocating sliding contacts, all the studied copper alloy variants exhibited different tribological performance, if the microstructure was modified by changes in the production route. For the chosen loading conditions, the coarser and softer microstructures showed superior tribological performance compared to the finer grained and harder materials. Wear was pronouncedly reduced and friction coefficient levels of the run-in friction pairs were lower too for the softer variants of each alloy. This basic finding applies to dendritic cast structures as well as to globular structures.
\nIn this study we compare the production routes MIM versus continuous casting followed by a massive forming step and lost foam casting versus continuous casting. MIM and LF both result in microstructures that are significantly coarser than the ones obtained through conventional processes. Thus these larger grained microstructures exhibit lower mechanical strength. Nevertheless, they showed to be more wear resistant than their smaller grained version from conventional processes. The effect was most pronounced for CuSn8 lubricated with non-additivated base oil SN150. This observation does not follow the conventional assumption that harder materials result in higher wear resistance, which follows the most common wear law of Archard and relates wear volume inversely proportional to macroscopic hardness [6]. As a consequence, small grained microstructures which result in higher tensile strength according to the Hall–Petch relation [16] are usually expected to be more wear resistant. Typically, only the initial or bulk material properties and microstructures are measured and taken into account [1].
\nIn the present sliding systems, the detected wear volume of the initially coarse-grained structure and thus softer version of the alloy was significantly lower for both studied alloys and differed by factor of 2 for CuNi9Sn6 and by a factor of 2.5 for CuSn8. For the dendritic cast structures of the alloy CuSn12Ni2 the same effects on wear could be observed, but reduction was less pronounced. This can be attributed mainly to the use of additivated oil in order to be closer to the real application, yet, additives may contribute to a formation of a chemical wear protective tribofilm.
\nThe analysis of the near-surface zones revealed the formation of a tribologically transformed layer and a mechanical mixed layer lying on top of the contact surface. Such a behaviour is well-known in tribology, but phenomenological descriptions of such microstructural changes often prevail [7, 25]. Few studies treat the effect of these layers on wear [26], or friction [27] More recent studies focus on the internal structure of the subsurface zone like the formation of a fibre texture [27] and/or the development of multiple grain structures, which split up in nano and micrometre sized grains [28]. Kapoor et al. [26] attribute the modified wear rates observed for different loading conditions to the different mechanical properties of these layers, independent of their genesis. Following Hall–Petch and Archard, a formation of a more fine-grained microstructure in the subsurface zone is expected to lead to local hardening and consequently to an increase of wear resistance.
\nHowever, the current study showed that the samples forming a thicker and more pronounced fine-grained zone exhibited higher wear than their chemically identical equivalents produced by MIM and LF, respectively. Typically, literature does not cover the role of the material itself or different initial microstructures on wear and friction. As the most prominent difference between the chemically identical samples characterised here can be seen in the initial grain sizes, it was presumed to be a key influencing factor.
\nFor the MIM version of CuSn8 the EBSD scans showed that the large grains react individually to the stress imposed during sliding and some accumulate plastic strain by an extremely localised work hardening processes. The local extension of the hardening is below or equal the single grain size. According to the EBSD results, the lattice orientation of grains exhibiting hardening is substantially different from neighbouring orientations. Thus, the number of their potential gliding planes with low Schmidt factors [29] differs a lot. The number of available slip planes is regarded as the main reason for the inhomogeneity of strain accumulation along the contact surface. Grains showing internal strains in the EBSD scans are assumed to have offered more slip systems with low Schmidt factors, with respect to the sliding direction, than the grains showing no enhanced defect density. If dislocation gliding is possible, the crystallographic lattice can take up strain and will partially rotate. If not, parts of the grain will be abraded directly with hardly any defect formation. The resulting enhanced hardening over a depth of 20 μm below the surface reaching up to 1.5 GPa was proven with nano-indentation measurements (Figure 12) for CuSn8. The EBSD scan illustrates that some grains take up plastic strain via local lattice rotations but form no new grain boundary. Quantitative analysis of SAGB or kernel average misorientation (KAM) are not depicted, because the large grains resulting from the MIM process revealed no substructure formation during sliding. The mechanically mixed layer found in patch-like structures on the surface could not be studied by EBSD as the misorientation was too high to identify grain structures. The nature of this layer would have to be verified in detailed TEM analyses in the future.
\nThe TTL and the mechanically mixed patches are considered as two independently forming features (Figure 14), which both have the capability to reduce wear. The mechanically mixed layer presumably distributes the normal load and thus reduces the contact stress. The local hardening – though inhomogeneous – may postpone the detachment of wear particles as described in [30]. Surprisingly, we found no mechanical mixed layer on the small-grained structures with high initial twin densities (Figure 9a) after identical loading.
\nPrincipal sketch of tribologically transformed zone beneath the surface a) partial grain rotation and tribolayer patches of mixed material b) formation of a thin layer of nanocrystalline grains accompanied by slip lines and twins, c) thicker nanocrystalline zone in vortex shape together with higher density of slip lines and twins formed also in larger depths.
In contrast to the MIM version the initial grain structure of the conventional CuSn8 was fine-grained and exhibits very high twin densities. Such a highly deformed microstructure can hardly take up further plastic strain and the local work-hardening potential is low. Consequently, no TTL could be observed in the EBSD scans and the microstructure does not appear to adapt during sliding, but seems to be worn off immediately during sliding.
\nThe dendritic cast alloy CuSn12Ni2 forms a nanocrystalline surface layer, which is similar to the ultra-nanocrystalline layer described for a Ni-alloy by [27]. We again refer to it as TTL. However, under loading conditions as those in the present study, this nanocrystalline layer was thicker for the finer structure resulting from continuous casting and very thin for the softer LF structure with large dendrites ranging over several hundred μm. Therefore, the nanocrystalline layer appears to be not beneficial for higher wear resistance.
\nBased on these observations we set up the following hypothesis for increased wear resistance of chemically equal, but softer, microstructures. Large grains without or with low initial defect densities can work-harden if the surface grains are appropriately oriented, i.e. so that the Schmidt factor of easy slip planes is low – (111) in the cubic systems of the study. For increased wear resistance, a near-surface layer has to take up plastic strains originating from the external stress state during the sliding process. This layer can form through local rotation of surface grains, as illustrated in Figure 14. For large grains, such as in the present study, where the grains have an average equivalent diameter of up to 250 μm, only partial rotation of grains occurs. Although the local grain rotations are rather inhomogeneous over the contact area, as many grains do not participate in local lattice rotations, this zone is relevant for the macroscopically observed wear volume and thus referred to as TTL. If the individual grains are saturated in defect densities, the surface near zone cannot work-harden and, as a consequence, the critical stresses for delamination or detachment of wear particles are much lower, wear occurs earlier and wear rates are higher.
\nClassical hardening through annealing processes and spinodal precipitations, which are found in CuNi9Sn6 can further reduce the wear volume compared to the non-hardenable CuSn8 as well as compared to the MIM version as shown in Figure 5. In the MIM version, the grain boundaries are facetted due to γ precipitation of length up to 2 μm at the grain boundaries, which is accompanied with a matrix depletion in the vicinity of the boundary. During heat treatment these precipitations at the boundaries dissolve and only lamellar phases within the grains and small intragranular precipitates form evenly distributed in the grains. The wear and friction result indicate that the latter structure is superior to adapt to the sliding process. Yet, the precipitation structure and distribution within the grains proved to be irrelevant for wear as well as friction. This indicates that the matrix composition and its element distribution determine the ability of a material to resist abrasion.
\nIn the case of dendritic structures, the TTL comprises a nanocrystalline zone (Figure 14) with grain sizes ranging from 100 nm to 300 nm. Again, the thickness of this zone is inhomogeneous with “pockets” of nanocrystalline zones with an extension of about 20 μm below the surface (Figure 13c and d). As this characteristic is the same for the large grained LF and the finer grained CC sample, the wavy interface could be either a result of the large dendrite thickness and coarse grains or an effect of a periodic pattern of the contact stress state.
\nStill the differences of the respective maximum thicknesses are significantly different with 8 μm for CC and 1.2 μm for LF. The formation mechanism of such nanocrystalline grains is not entirely clear. [27, 30] compare the sliding process to rolling and torsion processes and claim that the near-surface textures are typical rolling textures. As the matrix of the MIM CuSn8 as well as the CuSn12Ni2 matrix, are both bronze metal matrix lattices, the local rotation found in CuSn8 could be an early status followed by formation of nanocrystalline grains.
\nThe nanocrystalline layer found in LF samples resembles the non-continuous layer described in [28], referred to as nanostructured mixing layer (NML) and dynamic recrystallised layer. They show that these small grains form during the sliding contact and that the kinetics are different for different initial microstructures. The coarse-grained structures show higher wear rates which is not in line with our results on copper-based alloys. We found that the nanocrystalline layer is thicker for smaller-grained initial structures. However, the idea that not the hardness of the initial microstructure determines wear resistance, but the ease with which the nanocrystalline layer can be peeled-off during sliding, seems to be an appropriate explanation for the wear behaviour of the conventional CuSn12Ni2 version forming thick nanocrystalline layers.
\nInnovative material production processes are usually associated with lower mechanical strength and with inferior performance. They lack trust by designers as they suffer comprehensive characterisation, especially tribological performance properties. In the current study, the technology route metal-injection moulding (MIM) and lost foam casting (LF) are applied to well-known commercial bronze alloys. The alternative routes resulted in more ductile and softer materials, but proved to be superior in terms of wear and even showed tendencies for lower friction levels under equal configurations and loadings.
\nA detailed characterisation executed using SEM, nanointendation as well as EBSD techniques revealed that all samples formed a tribologically transformed layer (TTL) beneath the contact surface, but that the extent of this layer was pronouncedly different. The following observations can be summarised for the current study:
The thickness of the formed nanocrystalline layer depends on the material production route
Thicker nanocrystalline layers do not enhance wear, but increase wear.
Thus, grain refinement as a hardening mechanism does not go along with classical Archard approaches for increasing wear resistance via increasing hardness. The found nanocrystalline layer requires lower shear forces on the grains to be removed and this results in decreased macroscopic friction coefficients.
The tribolayer, which showed to be a presumable amorphous mechanically mixed layer, forms independently of the TTL on MIM samples and was observed on samples showing lower wear than equivalently loaded conventionally produced variants.
Large grained initial microstructures, produced by e. g. MIM, exhibit partial lattice grain rotation, which increases hardness very locally and were only observed on samples with lower wear.
Finally, we present a hypothesis for the formation timeline of nanocrystalline zones. Starting with local lattice grain rotation, followed by a monolayer on nanocrystalline grain layer accompanied with slip band and twin formation beneath and finally a thick nanocrystalline layer with a vortex structure. Based on the observation, we postulate that higher alloyed materials are more prone to local lattice rotation and defect formations such as twins and thus form a nanocrystalline zone more easily and quicker under the same loading conditions.
\nTopics presented are based on research projects with support by the Austrian COMET program (project XTribology, no. 849109, project InTribology, no. 872176) and by the respective program management institutions, viz. the Austrian Research Promotion Agency (Österreichische Forschungsförderungs-Gesellschaft mbH – FFG), on behalf of the Federal Government, as well as proportionately by the Governments of Niederösterreich, Vorarlberg, and Wien. We are as well grateful to the involved company partners and scientific partners for their financial support and for the fruitful cooperation.
\nGeneral requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed.
',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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\n\nIn other words, publishing with IntechOpen guarantees compliance.
\n\nRead more about Open Access in Horizon 2020 here.
\n\nWhich scientific publication to choose?
\n\nWhen choosing a publication, Horizon 2020 grant recipients are encouraged to provide open access to various types of scientific publications including monographs, edited books and conference proceedings.
\n\nIntechOpen publishes all of the aforementioned formats in compliance with the requirements and criteria established by the European Commission for the Horizon 2020 Program.
\n\nAuthors requiring additional information are welcome to send their inquiries to funders@intechopen.com
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