Wear Life Prediction Research Articles

Investigation of tribology and life prediction of polymer coatings on 7N21 aluminum alloy under dry sliding wear

To improve the tribological properties of the 7N21 aluminum alloy under dry sliding wear conditions, three E44-PI-PAI composite coatings filled with varying contents of MoS2, h-BN and VN were designed. The mechanical and tribological performances of these coatings were evaluated using scratch experiments, nanoindentation, and sliding wear experiments. After identifying the optimal coating, the engineering constants of the coating were determined using tensile experiments. Finally, a simulation analysis of the reciprocating wear life was performed using ABAQUS and a custom-developed UMESHMOTION subroutine. The results indicate that the optimal ratio of MoS2, h-BN and VN in the designed polymer coating is 4:3:4. The coefficient of friction (CoF) and wear rate of the optimal coating are 0.189 and 0.444×10−14 m3/Nm, representing a 67 % reduction in CoF compared to the 7N21 aluminum alloy. The L2 coating demonstrates excellent compatibility, featuring a surface roughness (Ra) of 0.630 μm, a uniform thickness of 10±2 μm, an optimal bonding strength at level 0, and a nanohardness of 0.391 GPa, meeting the requirements for engine bearings. The maximum deviation between the simulated wear life prediction and experimental results is only 8.11 %.

Cloud maps highlighting dynamic characteristics of surface signal to improve time-varying wear evaluation accuracy

This paper proposes a high accuracy time-varying wear evolution method with cloud maps highlighting dynamic characteristics of surface signal. Firstly, the cloud map method is established by arrangement of measured data, thus time-varying wear evolution process is visualized on a series of plane figures for preliminary qualitative analysis. Then, high accuracy recognition of time-varying wear states is realized by cloud map shape parameters, including kurtosis and 1-D kernel density function highlighting distribution information of surface signal. The relative recognition degree by cloud map shape parameters are higher than those of friction coefficient, Root-Mean-Square (RMS) deviation and fractal dimension. Finally, high accuracy time-varying wear life prediction is realized by cloud map size growing speed highlighting waviness information of surface signal. The relative prediction error is reduced from more than 20% to less than 10% compared with friction coefficient and roughness.

Wear life prediction of piston ring and cylinder liner based on integration of experiment and bench test data

The wear data of piston ring-cylinder liner (PRCL) of high-power diesel engines faces the problems of small samples, randomness, and underutilization, which leads to the wear life prediction of PRCL has been a difficult task in the engineering field. A wear life prediction method of PRCL based on the integration of experimental data and bench test data is proposed. First, a large amount of wear data is rapidly obtained through accelerated wear tests on specimens, and the distribution type of wear depth of PRCL is determined. Secondly, based on Rhee's wear equation, the nonlinear relationship between time, load, temperature, and wear depth is considered, and the acceleration models of temperature and load were introduced as the Arrhenius model and the inverse power law model, respectively. The wear life prediction model (WLPM) for specimens and parts with stochasticity is developed. The eigenvalues of WLPM’s parameters of specimens and parts were utilized to construct the mapping relationship from the specimen to the part. Finally, the Bayesian statistical method is used to merge the wear data of specimens and the parts, and the WLPM of specimens and the mapping relationship are taken as the a priori information, the bench data are taken as the new sample information, and the MCMC-Gibbs sampling method is used to estimate the mapping-based WLPM of parts. The results show that through the validation test, the method predicts the wear depth of PRCL with 100 % falling into the 95 % confidence interval better than the WLPM prediction results of a single specimen or part, and the errors of predicting the mean wear life of PRCL are all within 10 %. The method can provide a reference for the quality evaluation and engineering application of PRCL.

Wear Life Prediction of Sliding Bearings Based on Multitype Monitoring Data of Bridges

Wear Life Prediction of Sliding Bearings Based on Multitype Monitoring Data of Bridges

Intelligent prediction of wear life of automobile brake pad based on braking conditions

PurposeTo avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).Design/methodology/approachA simulated braking test bench for automobile disc brake was used. The correlation and mechanism between the three braking condition parameters of initial braking speed, braking pressure and initial braking temperature and the tribological performance were analyzed. The different artificial neural network (ANN) models of wear loss were discussed. Genetic algorithm was used to optimize the ANN model. The structure scheme of the online prediction system of BPWL was discussed and completed.FindingsThe results showed that the braking conditions were positively correlated with the wear loss, but negatively correlated with the friction coefficient. The prediction accuracy of back propagation (BP) ANN model was higher. The model was optimized by genetic algorithm, and the average deviation of prediction results was 4.67%. By constructing the online monitoring system of automobile braking conditions, the online prediction of BPWL based on the ANN model could be realized.Originality/valueThe research results not only have important theoretical significance for the study of BPWL but also have practical value for guiding the maintenance and replacement of automobile brake pads and avoiding the occurrence of braking accidents.

Wear calculation and life prediction model of disc brake based on elastoplastic contact mechanics

For the friction and wear of the braking surface of the disc brake, the mechanism of friction contact deformation of rough surface is analyzed. Considering elastoplastic contact mechanics of the friction surfaces, combined with Archard wear calculation theory, a wear calculation model of fractal rough surface is established, and a wear life prediction of the brake disc is proposed. Based on high-speed and heavy-load braking conditions, the influence of different braking parameters on the surface wear of brake disc is studied. Moreover, a pin-on-disc wear test is used to verify the validity of the wear calculation model, and the average relative error of test and simulation is 4.97%. The research results show that the fractal parameters affect the complexity of the rough surface, and surface wear is mainly caused by interface plastic contact. The friction coefficient is large, which can strengthen the surface shear effect. Large load and high speed would increase the real contact area of the surface and increase the wear volume per unit time. Furthermore, the wear life of the brake disc could be estimated, which is of great significance for raising the wear life of the brake disc, improving braking life and braking efficiency.

Study on Surface Adhesive Wear and Wear Life of Double Involute Gears Under Mixed Elastohydrodynamic Lubrication

Abstract To slow down the surface wear process of double involute gears (DIG), the surface adhesive wear model and wear life prediction model are established according to its meshing characteristics, combined with the modified Archard wear model and mixed elastohydrodynamic lubrication (EHL) model. This paper studies the surface wear and wear life of DIG, compares it with common involute gears (CIG), and discusses the influence of different factors on its surface wear. Results show that the adhesive wear distribution of DIG is similar to that of CIG. Properly reducing l* and increasing y* can reduce surface wear and prolong wear life. The reasonable selection of lubrication states and working conditions can be beneficial to enhance its tooth surface wear resistance.

Wear life prediction of vehicle mechanical parts based on Gray Markov chain

Wear life prediction of vehicle mechanical parts based on Gray Markov chain

Wear life prediction of vehicle brake pads based on image visual features

Wear life prediction of vehicle brake pads based on image visual features

Wear life prediction of vehicle mechanical parts based on Gray Markov chain

Wear life prediction of vehicle mechanical parts based on Gray Markov chain

Wear life prediction of vehicle brake pads based on image visual features

Wear life prediction of vehicle brake pads based on image visual features

Spline wear life prediction considering multiple errors

Splines in aero-engine are often excessively worn, seriously affecting flight safety. Manufacturing errors and parallel offset lead to unequal tooth loads, which is one of the main reasons to reduce the spline wear life. However, spline wear life with multiple errors is sometimes hard to predict due to technical or cost reasons. To obtain a more realistic spline wear life, a spline clearance calculation method considering manufacturing errors and parallel offset is proposed. The tooth contact pressure is developed based on the equivalent model of force carried by each tooth. Then, the relative sliding speed between the tooth and the tooth space is deduced by equating the two contact points on the spline as a crank-slider mechanism. Finally, a numerical model of spline wear is established by using the improved Archard’s wear model. The case results show that the maximum wear depth at torque of 240 N·m is 1.7 times the wear depth at torque of 80 N·m for the spline with a total of 2x105 motion cycles. For a spline with a parallel offset of 30 μm, the maximum wear depth is 66.8 μm, which is 3 times the offset of 5 μm. The wear life results show that the average life of the spline is 0.7 times that of parallel offset when considering multiple errors.

A modeling method for predicting the precision loss of the preload double-nut ball screw induced by raceway wear based on fractal theory

Precision double-nut ball screws are key functional parts in machine tools. The positioning accuracy of a ball screw can directly affect the machining accuracy of a machine tools. During actual use, the preload degradation and the large axial clearance of the double-nut ball screw are caused by the contact wear, which directly affects the transmission positioning accuracy. The contact wear is focused on the ball and the rough raceway surface of the ball screw. Therefore, a novel method is proposed in this paper to characterize the rough spherical morphology of raceways for ball screws based on fractal theory, and the fractal parameters of the screw and nut raceways are identified. A fractal contact model of the asperity on the raceway surface is established. By introducing contact parameters to modify the contact area, an area distribution function suitable for the coordinated contact is obtained. A mathematical model for calculating the normal contact load of the ball screw in three states is established by considering the friction factor. A precision loss model of the ball screw is established based on the full ball load distribution model and the multiscale contact load model, and the proposed model is verified by experiments. The surface contact coefficient of the inner and outer raceways is discussed. The influence of raceway fractal parameters on the wear rate is analyzed, and coupling research on precision loss and preload degradation for the ball screw is carried out. The initial wear rate under different axial load is discussed, and the change trend of wear rate with time is analyzed. The precision loss model of the preload double-nut ball screw based on fractal theory provides a new theoretical basis for accurately predicting the precision degradation and provides method support for establishing the wear life prediction of a ball screw.

Wear life prediction method of crowned double helical gear drive in point contact mixed elastohydrodynamic lubrication

Double helical gears are widely applied in the aviation, marine and energy equipment, and generally required to be modified as the crowned since the machining error and assembling error of the gear drive are inevitable. The crowned gears usually operate in point contact mixed elastohydrodynamic lubrication (EHL), and adhesive wear has been one of the most prominent problems, but few previous researches were focused on this problem. In this work, a wear life prediction model of crowned double helical gears in point contact mixed EHL is proposed. The tooth profiles of the crowned gears are obtained according to the generating principle, and the load is attained in consideration of multi-tooth pairs contact. The contact pressure is derived from Hertzian elastic contact theory and load sharing concept, and the sliding distance is determined by a generalized model. Then, the wear rate in point contact mixed EHL is evaluated by extending the Archard theory to lubricated case, and the extended Archard's wear model is used to formulate the gear tooth wear. Moreover, the wear life prediction model which takes account of rotation speed and input torque is established, and the influences of surface roughness and crowned amount on the wear life of the crowned gears are further studied. Results show that surface roughness has a great influence on the wear life at different rotational speeds, while input torque has a great influence on the wear life at different crowned amount. It indicates that the wear life of the crowned double helical gears can be improved by reasonable matching lubrication, modification and working parameters.

Wear life prediction of cemented carbide microtextured surface

In the study of metal surface friction, microtextured surfaces have been shown to reduce the surface friction coefficient, However, the influence of microtexture preparation parameters on its surface wear rate is rarely considered. In this paper, the effects of microtexture preparation parameters were studied by detecting elements in the area around the microtexture. Through friction experiments, the relationship between different microtexture preparation parameters and the surface wear rate of cemented carbide was studied. After three stages of experiments, it is concluded that the laser power has an important effect on the surface wear rate of microtextures. In addition, the interaction between laser power and microtexture diameter has a great effect, but gradually decreases with increasing friction distance. Finally, a wear prediction model for carbide microtextured surfaces with different preparation parameters was established, the results can provide a reference for the application of microtextured surfaces in cutting tools.

Experimental study on wear life of journal bearings in the rotor system subjected to torque

In the rotor system subjected to torque, the lubrication state of the journal bearings will change, which can lead to wear life change of such bearings. Therefore, the experimental study on the wear life prediction of the journal bearings in the rotor system subjected to torque was carried out. An improved Archard model for wear rate prediction was proposed, which can be applied to determine the relationship among speed, torque, and wear of different bearings. The 20 h wear of a single-span rotor system was tested at three speeds and torque. Test results show that the impact of torque on wear is greater than that of speed. The wear life of a bearing can be predicted with determined wear threshold. The 20 h wear was calculated by using the wear data of the rotor test bench in this model and compared with the actual wear. The comparison results indicate that the accuracy is higher than 92%. The torque – wear life curve shows that wear life is logarithmic to torque at a constant speed and significantly affected by changes in the low torque. Given a constant torque, the wear life will decrease logarithmically with the speed increasing.

Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels

This study presents an early stage data-based maintenance strategy of driving wheels that have different life distributions depending upon their location. Wear was predicted under the condition that the shape of the contact surface changes over time by an original method of back calculating degradation over time through the establishment of a basic wear model and a recursive function for wear progression. An accurate wear model was established and verified by an experiment. The variation in the profile of a wear-induced wheel was applied to the wear model. Furthermore, the model was combined with a recursive function and used to obtain the time-series degradation data. Subsequently, the factors which have a major influence on wheel production were analyzed, and a meta-model was configured using the response surface method. The degradation function and parameter distribution were estimated using uncertainty propagation, and the wear life distribution was derived using Bayesian inference and Markov chain Monte Carlo method. The reliability of driving wheels was obtained, and the maintenance interval was optimized under each maintenance conditions. Based on this novel method, the early stage data-based maintenance strategy was achieved, and the result of the wear life prediction was validated using the probability distribution analysis.

Numerical Simulation and Life Prediction of Steel Ball Unfolding Wheel Wear

Wear is the main failure form of unfolding wheel for bearing steel ball. When the wear increase to a certain extent, the function of the wheel is ineffective. According to the unfolding wheel structure and unfolding principle, the critical failure condition of the unfolding wheel is established. Based on the classical Archard wear model and combined with the finite element method, a discrete Archard wear model is proposed, and a prediction model of the wear life of the unfolding wheel is established. Using Ansys software to simulate the wear of the unfolding wheel, the relationship equation between the wear depth and the wear times is obtained, and the wear life prediction of the unfolding wheel is carried out.

A combined CFD-experimental study of erosion wear life prediction for non-Newtonian viscous slurries

Solid particle erosion is one of the key issues affecting operational reliability and cost of tools and equipment in the oil and gas industry. Solid particles are typically either entrained in the fluid stream as a part of the unfiltered solids during drilling and production operations or are intentionally added to the fluid and transported into the formation during hydraulic fracturing.Erosion rates of optimized and suboptimal equipment designs can differ by several orders of magnitude. Recent developments in computational fluid dynamics (CFD) have led to the development of methodologies enabling accurate erosion wear life prediction. The study includes an overview of best practices in erosion prediction and shows practical examples of their application based on experimental studies of viscous slurry erosion in a direct impingement jet. Additionally, a Cross rheological model is used to model the non-Newtonian behavior of the fluid from the experimental program. The results show that the Cross model predicts a shear-thinning behavior; thus, erosion prediction with this rheological model agreed better with data than a Newtonian model.

Wear reliability of spur gear based on the cross-analysis method of a nonstationary random process

Tooth wear is one of the main reasons that lead to gear failure. The amount of wear is nonlinearly related to temperature, lubrication, load, and various random factors of materials, with obvious randomness and slow time-varying characteristics. Wear is a nonstationary random process, which has no accurate mathematical model or accurate reliability estimation method. This article proposes a reliability model of spur gears which works under a nonstationary random process that exceeds the limit, and the time-varying wear reliability is studied based on the level crossing analysis method. The wear at tooth root is revised in the calculation under the nonstationary random process, and the reliability curves are obtained afterwards. An experiment is carried out on the spur gear meshing test rig, and the reliability model and wear performance are verified and analyzed. Results obtained with the proposed tooth surface wear reliability model match well with the experimental results. Therefore, this model is applicable for situations under a nonstationary random process. The new method makes contribution to the assessment of gear running status and is of great significance in the prediction of wear life under a nonstationary random process.