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The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group.

Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied.

In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group.

The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.

The purpose of this paper is to propose a method of qualitative ferrographic analysis by quantitative parameters of wear debris characteristics.

The amount of the wear debris needed for analysis on the ferrogram made by rotary ferrograph is discussed based on the theory of debris group. Quantitative parameters are constituted to express the characteristics of wear debris group, and correlation coefficients are employed to establish the relationship between wear debris and wear condition. The reliability of the method was verified by wear test experiments and ferrographic analysis.

The wear condition of machines should be determined by studying all the debris together as a group rather than by focusing on individual debris. In the proposed method, the qualitative analysis result is obtained by synthetic analysis of quantitative parameters of wear debris characteristics using a computer program, which makes the judgment of the wear system condition more objective and precise.

In the procedure of wear condition monitoring by the proposed method, because the weight factors and correlation coefficients introduced in this paper are determined according to the experiences deriving from practice among mining machinery, further rectifications may be needed if they are applied to other industrial field.

The paper illustrates a more objective and precise ferrographic analysis method for wear condition monitoring.

The purpose of this study is to provide a new convolutional neural network (CNN) model with multi-scale feature extractor to segment and recognize wear particles in complex ferrograph images, especially fatigue and severe sliding wear particles, which are similar in morphology while different in wear mechanism.

A CNN model named DWear is proposed to semantically segment fatigue, severe sliding particles and four other types of particles, that is, chain, spherical, cutting and oxide particles, which unifies segmentation and recognition together. DWear is constructed using four modules, namely, encoder, densely connected atrous spatial pyramid pooling, decoder and fully connected conditional random field. Different from the architectures of ordinary semantic segmentation CNN models, a multi-scale feature extractor using cascade connections and a coprime atrous rate group is incorporated into the DWear model to obtain multi-scale receptive fields and better extract features of wear particles. Moreover, fully connected conditional random field module is adopted for post-processing to smooth coarse prediction and obtain finer results.

DWear is trained and verified on the ferrograph image data set, and experimental results show that the final Mean Pixel Accuracy is 95.6% and the Mean Intersection over Union is 92.2%, which means that the recognition and segmentation accuracy is higher than those of previous works.

DWear provides a promising approach for wear particle analysis and can be further developed in equipment condition monitoring applications.

In the process of conveying the solid–liquid two-phase medium of the centrifugal slurry pump, the wear of the flow-passing parts is an important problem affecting its life and safe operation. Therefore, a numerical investigation on the wear characteristics of the centrifugal slurry pump under different particle conditions was conducted.

A solid-liquid two-phase model based on CFD-DEM coupling is established and used to analyze the flow field and the wear characteristics of the flow-passing parts with different particle densities, volume fractions and sizes.

Particle conditions will affect the pump flow field. To analyze the pump wear characteristics, the wear distribution, wear value and cumulative force laws of flow-passing parts under different particle conditions are obtained. In each flow-passing part, with the increase of particle density, volume fraction and size, the wear area is concentrated and the wear depth increases. Under different particle conditions, the wear is mainly on the volute chamber and the blade pressure surface, and the tangential cumulative force of flow-passing parts is much larger than the normal cumulative force.

An accurate model and a coupled simulation method for predicting the wear of the slurry pump are obtained, and the wear characteristic law can provide a reference for the design of the slurry pump to reduce friction.

This paper aims to simulate the effect of counterface roughness on the friction transfer and wear of the polymer material sliding against steel.

The dynamic process of friction transfer and wear of polytetrafluoroethylene (PTFE) sliding against steel 45 was simulated by the software of particle flow code in two dimensions and a discrete element method. The effect of the counterface roughness was considered in the simulation. The definitions of the transferred particle and worn particle were given.

The simulation results showed that a transferred particle layer was formed on the surface of steel 45 during friction. The wear rate of PTFE can be effectively reduced by the formation of the transferred particle layer. The formation and stability of this particle layer is certainly affected by the counterface roughness (Rz). In this paper, the transferred particle numbers increased with Rz increase. And so did the worn particle numbers. However, there was little effect of Rz on the wear rate of PTFE.

The dynamic process of the friction transfer and wear of the PTFE/ steel 45 friction pair was reproduced at the micro-level. Then, the transfer and wear were quantitatively exhibited. The relations between the transfer or wear and counterface roughness was simulated and discussed. It will be meaningful for the optimization and effective control of friction and wear of polymer/metal sliding system.

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

This paper aims to improve the tribological properties of lithium complex greases using nanoparticles to investigate the tribological behavior of single additives (nano-TiO₂ or nano-CeO₂) and composite additives (nano-TiO₂–CeO₂) in lithium complex greases and to analyze the mechanism of their influence using a variety of characterization tools.

The morphology and microstructure of the nanoparticles were characterized by scanning electron microscopy and an X-ray diffractometer. The tribological properties of different nanoparticles, as well as compounded nanoparticles as greases, were evaluated. Average friction coefficients and wear diameters were analyzed. Scanning electron microscopy and three-dimensional topography were used to analyze the surface topography of worn steel balls. The elements present on the worn steel balls’ surface were analyzed using energy-dispersive spectroscopy and X-ray photoelectron spectroscopy.

The results showed that the coefficient of friction (COF) of grease with all three nanoparticles added was low. The grease-containing composite nanoparticles exhibited a lower COF and superior anti-wear properties. The sample displayed its optimal tribological performance when the ratio of TiO₂ to CeO₂ was 6:4, resulting in a 30.5% reduction in the COF and a 29.2% decrease in wear spot diameter compared to the original grease. Additionally, the roughness of the worn spot surface and the maximum depth of the wear mark were significantly reduced.

The main innovation of this study is the first mixing of nano-TiO₂ and nano-CeO₂ with different sizes and properties as compound lithium grease additives to significantly enhance the anti-wear and friction reduction properties of this grease. The results of friction experiments with a single additive are used as a basis to explore the synergistic lubrication mechanism of the compounded nanoparticles. This innovative approach provides a new reference and direction for future research and development of grease additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0291/

The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images.

The wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances.

Parameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system.

One major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time.

A new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.

Reports on the study of wear debris in the early 1980s organized by the Institution of Mechanical Engineers. Describes the use of Ferrography, spectrographic oil analysis, X‐ray fluorescence, inductive, magnetic and ultrasonic methods of debris detection with some examples of wear limits.

Sliding wear is a common phenomenon in the iron ore handling industry. Large-scale handling of iron ore bulk-solids causes a high amount of volume loss from the surfaces of bulk-solids-handling equipment. Predicting the sliding wear volume from equipment surfaces is beneficial for efficient maintenance of worn equipment. Recently, the discrete element method (DEM) simulations have been utilised to predict the wear by bulk-solids. However, the sensitivity of wear prediction subjected to DEM parameters has not been systemically investigated at single particle level. To ensure the wear predictions by DEM are accurate and stable, this study aims to conduct the sensitivity analysis at the single particle level.

In this research, pin-on-disc wear tests are modelled to predict the sliding wear by individual iron ore particles. The Hertz–Mindlin (no slip) contact model is implemented to simulate interactions between particle (pin) and geometry (disc). To quantify the wear from geometry surface, a sliding wear equation derived from Archard’s wear model is adopted in the DEM simulations. The accuracy of the pin-on-disc wear test simulation is assessed by comparing the predicted wear volume with that of the theoretical calculation. The stability is evaluated by repetitive tests of a reference case. At the steady-state wear, the sensitivity analysis is done by predicting sliding wear volumes using the parameter values determined by iron ore-handling conditions. This research is carried out using the software EDEM^® 2.7.1.

Numerical errors occur when a particle passes a joint side of geometry meshes. However, this influence is negligible compared to total wear volume of a wear revolution. A reference case study demonstrates that accurate and stable results of sliding wear volume can be achieved. For the sliding wear at steady state, increasing particle density or radius causes more wear, whereas, by contrast, particle Poisson’s ratio, particle shear modulus, geometry mesh size, rotating speed, coefficient of restitution and time step have no impact on wear volume. As expected, increasing indentation force results in a proportional increase. For maintaining wear characteristic and reducing simulation time, the geometry mesh size is recommended. To further reduce simulation time, it is inappropriate using lower particle shear modulus. However, the maximum time step can be increased to 187% T_R without compromising simulation accuracy.

The applied coefficient of sliding wear is determined based on theoretical and experimental studies of a spherical head of iron ore particle. To predict realistic volume loss in the iron ore-handling industry, this coefficient should be experimentally determined by taking into account the non-spherical shapes of iron ore particles.

The effects of DEM parameters on sliding wear are revealed, enabling the selections of adequate values to predict sliding wear in the iron ore-handling industry.

The accuracy and stability to predict sliding wear by using EDEM^® 2.7.1 are verified. Besides, this research accelerates the calibration of sliding wear prediction by DEM.