Dry sliding wear behaviour of an aluminium alloy–granite particle composite

In the present study, the effect of granite reinforcement on the dry sliding wear behaviour of an aluminium–silicon alloy (BS:LM6) was investigated using a pin-on-disc machine. The composite was prepared using liquid metallurgy technique wherein 10 wt.% granite particles were incorporated in the matrix alloy. Sliding wear tests were conducted at applied loads in the range 0.2–1.6 MPa and speeds of 1.89, 3.96 and 5.55 m/s. The matrix alloy was also prepared and tested under identical conditions in order to see the influence of the dispersoid phase on wear behaviour. It was observed that the composite exhibited lower wear rate than that of the matrix alloy. Increasing applied load increased the wear rate. In the case of the composite, the wear rate decreased with speed except at higher pressures at the maximum speed; the trend reversed in the latter case. On the contrary, the matrix alloy exhibited minimum wear rate at the intermediate test speed. Seizure pressure of the composite was significantly higher than that of the matrix alloy, while temperature rise near the contacting surfaces and the coefficient of friction followed an opposite trend. SEM examination of the worn surfaces, subsurface regions and debris enabled to understand the operating wear mechanisms.     

Development of advanced quantitative analysis methods for wear particle characterization and classification to aid tribological system diagnosis

Classification of wear particles is performed in two steps, i.e. first a particle to be classified is characterized by surface feature parameters such as roughness, directionality, homogeneity, periodicity, etc. and then, the particle is assigned to a specific class using these parameters. However, a significant limitation of this approach is that surface parameters are often not unique to a specific surface topography and their values may change significantly with scale, orientation angle and position at which the particle data was acquired. Various attempts were made to overcome this limitation by selecting a core set of parameters which ensures that wear particles are accurately classified. However, the parameter selection is usually cumbersome and requires lengthy computation. Furthermore, there is no guarantee that parameters selected are sensitive enough to separate particles belonging to different classes. Thus, a new classification technique based entirely on dissimilarity measures (e.g. Euclidean, Baddeley's distances), calculated between surface images of an unclassified particle and classified particles, was developed. The classification process is based on assigning a particle to a class of particles with the smallest dissimilarity measure. This idea arises naturally from the two facts: (i) compactness, similar objects are in close proximity to each other in their representation space, while different objects are far apart, and (ii) true representation, if objects are close to each other in their representation space they belong to the same class. In this paper, an overview of recent advances and developments in the area of particle classification based on dissimilarity measures is presented with a particular emphasis on constructing a simple and accurate classifier.     

Automatic wear‐particle classification using neural networks

Although the study of wear debris can yield much information on the wear processes operating in machinery, the method has not been widely applied in industry. The main reason is that the technique is currently time consuming and costly due to the lack of automatic wear particle analysis and identification techniques. In this paper, six common types of metallic wear particles have been investigated by studying three‐dimensional images obtained from laser scanning confocal microscopy. Using selected numerical parameters, which can characterise boundary morphology and surface topology of the wear particles, two neural network systems, i.e., a fuzzy Kohonen neural network and a multi‐layer perceptron with backpropagation learning rule, have been trained to classify the wear particles. The study has shown that neural networks have the potential for dealing with classification tasks and can perform wear‐particle classification satisfactorily. This revised version was published online in June 2006 with corrections to the Cover Date.     

Wear particle analysis—utilization of quantitative computer image analysis: A review

This paper provides a general overview of developments and progress in quantitative computer image analysis as applied to wear particle identification/classification technology, over the last two decades. Since many technical disciplines are involved in this ‘infant-stage’ technical area, an attempt is made to put into perspective mechanical failure prediction/diagnosis and prevention through quantitative wear particle morphological analysis. The problems experienced with applying conventional wear particle analysis methods in machinery condition monitoring, notably the employment of wear debris morphological diagnostic systems, revealed that it is not prudent to rely solely on human interpretation in the analysis of ‘filtergram’ slides. This has highlighted the need for improving the provision of ‘intelligent’ objective methods for performing this type of analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image analysis are reported and examined as a basis for establishing improved methods of diagnostic analysis.     

Research on an on-line wear condition monitoring system for marine diesel engine

This paper introduces a kind of on-line wear condition monitoring system for marine diesel engines. The system consists of three functions i.e. particle detecting, lubricant quality detecting and shaft torque moment and instantaneous rotation velocity detecting. The system detects wear particles in lubricant with an on-line ferrograph so as to judge wear condition of tribo-pairs of the diesel engine. A vertical detector fixed is used for environment of the marine diesel engine in this system, and the rule of distribution of particles in the vertical detector fixed and the horizontal detector fixed are alike in substance. The system detects the relative variation of lubricant quality by the grid capacitance sensors in an on-line way, which consists of an upper capacitance and a lower capacitance and can distinguish the relative variation of the dielectric constant of lubricant caused by pollutants such as water, metal particles etc. The system detects the shaft torque moment and the instantaneous rotation velocity of the diesel engine with photoelectric sensors, and corresponds the wear condition with the power condition by the change of instantaneous rotation velocity due to burning pressure change, which is helpful to judge cylinder wear.     

Quantitative estimation of wear amounts by real time measurement of wear debris in lubricating oil

A diagnostic technique that can estimate quantitatively wear amounts under lubricated condition was developed using our developed on-line particle counter. Wear tests were carried out by rubbing a bearing metal against a carbon steel in paraffin oil. The size and number of wear debris in the circulating oil could be measured in real time. The volume of each debris was calculated and, additionally, the total wear amount during a given duration was calculated by accumulating all debris volume. The wear amounts obtained by the quantitative estimation were fairly similar to the measured values of mass loss of the specimen.     

Wear and wear particles—some fundamentals

Each of the various processes by which material can be lost from a surface in service leaves its fingerprint both in the topography of the worn surface and in the size, shape and number of the particles which make up the wear debris. To use debris examination as a diagnostic aid in assessing the health of operating plant, which may contain many tribological contacts, requires not only careful and standardised procedures for debris extraction and observation but also an appreciation of the mechanisms by which wear occurs and the regimes in which each of the contacts of interest operates when displayed on an appropriate operational map.     

Scale-invariant analysis of wear particle morphology—a preliminary study

The importance of wear particle characterization is continuously growing, as the need for prediction and monitoring of wear increases. Accurate analysis of wear particles can, however, be limited by problems associated with particle characterization, especially of the wear particles' surface morphology. Since the shape and surface topography of wear particles often exhibit a fractal nature, fractal (scale-invariant) methods are, therefore, used in their characterization. However, the methods used to date ignore the fact that all fractal objects can be described by a small set of mathematical rules; although finding those rules which describe a particular fractal image is a difficult problem. No general solution exists to date and this paper attempts to redress this problem. A new analysis method, ‘scale-invariant analysis’, which is based on a partitioned iterated function system (PIFS), is proposed for the characterization of wear particle morphology. PIFS is a collection of contractive affine transformations. Each affine transformation transforms one part of a wear particle image onto another part of the same image. PIFSs were constructed for both computer generated and SEM images of wear particles. Results obtained in this study clearly demonstrate that the morphology of wear particles can effectively be characterized using the PIFS method.     

Quantitative correlation of wear debris morphology: grouping and classification

Considerable interest has been expressed in the quantitative analysis of wear debris to minimize the subjectivity of visual assessment of debris morphology. Recent works involve the quantification of wear debris morphology using numerical parameters. In this paper, more comprehensive quantitative analysis of wear debris is performed: wear debris morphology is quantified with numerical parameters and, furthermore, quantitative correlation is performed to demonstrate how specific statistical data analysis techniques can be applied to carry out grouping and classification of debris. Grouping and classification are multivariate statistical techniques that can be used to find out morphological groups of wear debris and to classify wear debris into the predefined wear conditions, respectively. It is shown that statistical data analysis can provide systematic quantitative correlation of wear debris without subjective individual judgment.     

Two-dimensional fast Fourier transform and power spectrum for wear particle analysis

Statistical parameters, such as R_a and R_q, have been widely used to investigate the roughness of wear particle surfaces in the literature. It has been reported that wear particle analysis based only on numerical characterization is often insufficient to distinguish certain types of wear debris. In this study, two-dimensional fast Fourier transform, power spectrum and angular spectrum analyses are applied to describe wear particle surface textures in three dimensions. Laminar, fatigue chunk and severe sliding wear particles, which have previously proven difficult to identify by statistical characterization, have been studied. The results show that spectral analysis effectively identifies the surface texture pattern (e.g. isotropy or anisotropy) and can be applied to classify these three types of wear particles.     

Effect of friction cycles on wear particle generation of carbon nitride coating against a spherical diamond

The in-situ observations of wear particle generation of carbon nitride coating on silicon repeatedly sliding against a spherical diamond have been studied in terms of the critical friction cycles and normal loads. An environmental scanning electron microscope (E-SEM), in which a pin-on-disk tribotester was installed, has in-situ provided direct evidence of when and how the wear particle generation do occur during the repeated sliding of carbon nitride coating against a spherical diamond. The in-situ observations of non-conductive carbon nitride coating are therefore available free from surface charging with controllable relative humidity. The repeated sliding tests at a sliding speed of 50 μm/s have been carried out with the purpose of observing the ‘No wear particle generation’ region when varying normal load from 10 to 250 mN. It appears that until 20 friction cycles, the maximum Hertzian contact pressure P_max for ‘No wear particle generation’ can be improved from 1.39 Y to 1.53 Y if silicon is coated by carbon nitride with a thickness of 10 nm, where Y is defined as the yield strength of silicon. The applicable enlargement of the ‘No wear particle generation’ region of carbon nitride coating has therefore been comparatively discussed with the silicon substrate from the view points of the friction coefficient and the specific wear rate. The mode transition maps have also been summarized for the repeated sliding of carbon nitride coating in terms of ‘No wear particle generation’, ‘Wear particle generation by microcutting’ and ‘Wear particle generation by microcutting and microfracturing’ three typical modes.     

Dry sliding wear behaviour of PTFE filled with glass and bronze particles

Abstract

The wear behaviour of polytetrafluroethylene (PTFE) filled with 25% glass and 40% bronze particles was studied on a pin on disc test rig. Solid lubricant composite materials were prepared by compression moulding technique. The wear parameters considered for the study were applied load, sliding speed and sliding distance. The experimental results indicate that the weight loss increases with increasing load, sliding speed and sliding distance, as expected. Sliding distance has more effect on weight loss followed by applied load. The 40% bronze+PTFE composite exhibits better wear resistance compared to other types. The dominant interactive wear mechanisms during sliding of PTFE and its composites are discussed in this paper.     

The characterization of wear transitions in sliding wear process contaminated with silica and iron powder

When a machine is in operation, two moving surfaces interact to generate a large amount of wear particles. The wear debris generated inside the machine or contaminants from outside plays important roles in both two-body and three-body wear. For all mining and port machinery, their lubricants are very likely to be polluted by contaminants such as silica and other metallic debris such as iron and nickel. In order to seek a deeper understanding of the effects of different contaminants on wear process, this project investigated sliding wear processes when silica powder and iron powder exist in lubricants.Four sliding wear tests were conducted on a pin-on-disc tester with and without the contaminants. Visual inspection, ferrography analysis, particle quantity analysis using a particle analyzer, and numerical surface analysis using confocal laser scanning microscopy (CLSM) were conducted to study the wear particles and wear surfaces. Supported by the data generated from the comprehensive analyses on the wear particles and wear surfaces, the investigation of the effects of the added contaminants to the wear processes and wear mechanisms have been carried out and presented in this paper.     

Diamond particle shape: Its measurement and influence in abrasive wear

The classification of diamond particles in terms of their abrasive characteristics is addressed in this work. Specifically, diamond particles of different grades have been studied in terms of their shape to identify useful trends and correlations with experimental wear rate. Ten diamond types, typically used by the abrasives industry and exhibiting varied shape, were selected. They included highly geometric single crystals, crushed single crystals, and polycrystalline diamond particles, with nominal diameters of between 65 and 197 μm. Electronic boundary projections were obtained using a digital-camera-equipped optical microscope, which were then processed using proprietary software. The parameters calculated include: diameter (minimum, minor and maximum), aspect ratio, convexity and sharpness. Interesting correlations were found between convexity and sharpness that engendered both these parameters to be considered as useful measures of wear rate. This was reinforced by experimental wear tests, using grinding wheels manufactured from six of the 10 diamond types, which demonstrated excellent correlations of sharpness (0.991 correlation coefficient), and convexity (0.987 correlation coefficient), with the wear rate of a polyurethane workpiece.     

Dry sliding wear of fly ash particle reinforced A356 Al composites

In the present study aluminium alloy (A356) composites containing 6 and 12 vol. % of fly ash particles have been fabricated. The dry sliding wear behaviour of unreinforced alloy and composites are studied using Pin-On-Disc machine at a load of 10, 20, 50, 65 and 80 N at a constant sliding velocity of 1 m/s. Results show that the dry sliding wear resistance of Al-fly ash composite is almost similar to that of Al₂O₃ and SiC reinforced Al-alloy. Composites exhibit better wear resistance compared to unreinforced alloy up to a load of 80 N. Fly ash particle size and its volume fraction significantly affect the wear and friction properties of composites. Microscopic examination of the worn surfaces, subsurfaces and debris has been done. At high loads (>50 N), where fly ash particles act as load bearing constituents, the wear resistance of A356 Al alloy reinforced with narrow size range (53–106 μm) fly ash particles were superior to that of the composite having the same volume fraction of particles in the wide size range (0.5–400 μm).     

Filtration and coating effects on self-generated particle wear in boundary lubricated roller bearings

A specially designed test system involving boundary lubricated roller bearings was used to study wear at low particle concentration levels. A separate oil system circulated the oil through the test bearings. The effects of self-generated contaminants from the system were studied. Even at very low concentration levels, self-generated contaminants can cause significant wear. The concentration of self-generated particles was very high during the running-in period. It is therefore important that the filtration be very efficient during this period. The experimental results show that filtration during run-in for 1 h with a 3 μm filter can reduce both the mass loss and the number of self-generated particles by a factor of 10. Furthermore, the results also show that while the bearings with standard rollers can have significant wear, those with coated rollers are at the same time almost unaffected by wear. Also, the number of particles generated in the contact was significantly less when using coated rollers. There were twice as many self-generated particles when using a standard bearing as those compared with a coated bearing.     

Theory of material wear by solid particle impact — a review

Erosion of materials by solid particle impingement is discussed in terms of the joint effect of the friction and the deformation components in the wear process. Formulae for calculating wear have been obtained on the basis of the fatigue theory of solid body failure and corrected using experimental data. Relationships are presented which make it possible to estimate quantitatively how wear is influenced by the physico-mechanical properties of the materials of colliding bodies and by the parameters of a solid particle stream (concentration, size, and granulometric composition). A criterion for erosion evaluation in the surface layer of a mechanism has been derived from studies on the effect of the shape of solid particles on wear. An algorithm is given for calculating reliability at the design stage from wear characteristics.     

Computational simulations of delamination wear in a coating system

Delamination wear constitutes one relevant wear process in many materials. In particular, wear failure through delamination is relevant for material systems where a coating is present on a substrate material. A computational modeling approach is presented that aims to describe the processes of formation and growth of wear delaminations. The model is based on the cohesive zone model approach. An irreversible cohesive zone model is used in a parametric numerical study of delamination wear in a coating system. The model predicts trends in agreement with the trends emerging from Archard's law. The proposed modeling approach has the advantage that details of the delamination wear process can numerically be studied, and that a unified framework from delamination initiation and propagation is provided.     

Some aspects of solid particle erosion of cermets

The erosive wear resistance of cermets with different composition, structure and properties has been investigated. It was shown that the erosive wear resistance of cermets can not be estimated only by hardness, characterizing resistance to penetration. The differences in wear resistance between cermet materials with equal hardness levels can be attributed to differences in their resistance to fracture and fracture toughness. The mechanism of erosion depends first on testing conditions. The present paper discusses some features of the material removal process during particle-wall collision. Systematic studies of the influence of the impact variables on the collision process have been carried out.     

Shape and texture features in the automated classification of adhesive and abrasive wear particles

In this study the automated classification system, developed previously by the authors, was used to classify wear particles. Two kinds of wear particles, adhesive and abrasive, were classified. The wear particles were generated using a pin-on-disk tribometer. Various operating conditions of load, sliding time and abrasive grit size were applied to simulate adhesive and abrasive wear of different severity. SEM images of wear particles were acquired, forming a database for further analysis. The particle images were divided into eight groups or classes, each class representing different wear test conditions. All eight particle classes were first examined visually. Next, area, perimeter and elongation parameters were determined for each class and the parameters were statistically analysed. The automated classification system, based on particle surface texture, was then applied to all particle classes. The results of the automated particle classification were compared to those based on either the visual assessment of particle morphology or numerical parameter values. It was shown that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.