Wear Debris Analysis Research Articles

Microstructure and high temperature tribological behavior of nickel-based superalloy with addition of revert

The recycling of revert plays a crucial role in cost-efficiency and green manufacturing of superalloy. In this study, optical microscopy, scanning electron microscopy coupled with energy dispersive spectroscopy, tensile test, and tribological analysis were employed to investigate the microstructure, mechanical properties, and tribological behaviors of vacuum induction melted superalloys of 100% original material (Sample 1) and 40% original material + 60% revert (Sample 2). Dry sliding tests were conducted at 300 and 730 °C. The addition of revert promotes microstructure refinement, hardness increase, and carbide formation, at the cost of 18.5% ductility loss. During the high-temperature dry sliding process, abrasive, adhesive, and oxidative wear mechanisms were observed on the contact surfaces. Sample 2 exhibited a lower wear rate and coefficient of friction at both 300 and 730 °C. Additionally, a more continuous and wear-resistant tribolayer was formed on the wear track of Sample 2, effectively mitigating matrix oxidation compared to Sample 1. At 730 °C, a thick tribolayer developed on the wear surface of Sample 2, characterized by a hard and wear-resistant glaze layer on the outermost surface. The continuous sliding induced matrix heating, which facilitates recrystallization in the subsurface of Sample 2, induced by increased inclusions and carbides from the revert. The analysis of wear debris further illustrated the variations of wear mechanism at different temperatures. These findings are meaningful for understanding the high-temperature behavior of superalloys and optimizing alloy recycling process.

A comprehensive study on dry sliding wear and in-vitro degradation of AZ31 composite with 6 wt% Sn and 1.5 wt% TiO2 fabricated by stir casting

Abstract This experimental study investigates the dry sliding wear behaviour of novel AZ31 composite with 6 wt% Sn and 1.5 wt% TiO2 samples, focusing on the influence of sliding distance and normal load. Additionally, the research examines degradation rates during a 120-hour immersion period in simulated body fluid (SBF). The wear volume loss increased significantly with higher loads and longer sliding distances, showing a 142% rise from 1000 m to 2000 m and 76% from 2000 m to 3000 m under a 5 N load, peaking at a 25 N load over 5000 m. The specific wear rate dropped by 39% from 5 N to 15 N but increased with distance due to thermal softening, while the coefficient of friction decreased from 0.284 at 1000 m to 0.213 at 5000 m under 5 N. FESEM analysis revealed a transition from abrasive to delamination wear, with a protective oxide layer forming during sliding. Sn in the composite formed hard Mg2Sn intermetallic compounds, affecting crack formation and propagation under higher loads. Wear debris analysis showed both abrasive and delamination wear mechanisms, highlighting the protective role of the oxide layer. The degradation rate increased rapidly during the initial immersion stage in simulated body fluid (SBF), reaching 15.9 mm year−1 after 24 h, and then stabilizing after 48 h. Hydrogen evolution rose sharply from 0.28 ml cm−12 at 2 h to 7.6 ml cm−12 within 24 h, with a 92% increase by 48 h and an additional 38% increase from 48 to 72 h. Post-immersion FESEM analysis showed corroded surfaces with protective layers, cracks, and pitting corrosion. Magnesium hydroxide (Mg(OH)2) and hydroxyapatite played crucial roles in inhibiting corrosion. This analysis provides insights into the wear, and degradation dynamics of the AZ31-6Sn/1.5TiO2 composite, with implications for engineering applications and the development of biodegradable implants in biomedical fields.

Machining performance and material removal mechanism of sapphire with novel polishing slurry

Enhancing the interfacial reactivity of polishing slurry is crucial for improving the polishing efficiency and surface quality of sapphire wafers. In this study, a novel green polishing slurry was developed, comprising aminomethyl propanol, xylitol, highly active silica, and deionized water. The processing quality and removal mechanism of this novel green polishing slurry on semi-solid flexible polishing were investigated to achieve ultra-precision, high-efficiency, and environmentally friendly polishing of sapphire wafers. Experimental results demonstrated that the novel green polishing slurry reduced surface roughness by 12.5% and increased the material removal rate by 71.3% compared with traditional polishing slurry. Various analytical techniques, including wear debris analysis, molecular simulation, infrared detection, electrochemical testing, and physical phase detection, were utilized to explore the material removal mechanism. The findings indicated that aminomethyl propanol promotes chemical reactions between the green polishing slurry and sapphire wafers, leading to the conversion of the intermediate product Al(OH)3 to Al(OH)4-. In addition, the complexation reaction between xylitol and Al(OH)4- ions during polishing was found to accelerate the removal of surface materials from sapphire wafers, resulting in improved polishing efficiency and surface quality.

Exploring Tribological Behaviors of Profile-Shifted Spur Gears: An Experimental Investigation

The tribological behavior of an addendum modified altered tooth sum optimized spur gear set were experimentally compared with that of the standard spur gear under several loading conditions including the wear morphology study and wear debris analysis. The temperature, viscosity, elastohydrodynamic oil film thickness, specific film thickness, and combined surface roughness variation under increasing operating load were analyzed experimentally. Finally, the performance impact of addendum shifted altered tooth sum optimized gear was examined. Results show that the tribological improvement strongly depends on the addendum alteration and modified tooth sum condition. The modification of the addendum in gears with a negatively altered tooth sum facilitates the transition from single-tooth contact to two-tooth contact, broadening the lubrication range while preserving optimal film thickness and improving wear resistance. However, it is reverse to decrease the film thickness and increase wear for standard and profile-modified positively altered tooth sum gear. The gear tribological performance can be significantly improved by profile modification and negatively altered tooth sum approach with a lower rate of degradation of oil and damage on the tooth contacting surface.

Wear debris analysis of Al-Si/MWCNT nanocomposite during dry sliding wear tests

This study demonstrates the enhancement of wear resistance achieved by incorporating multi-walled carbon nanotubes (MWCNTs) in LM6 aluminum alloy to form nanocomposites. Experiments on wear resistance study were performed at different test parameters for various compositions of MWCNT in LM6 alloy. The size and nature of debris obtained post experiments were significantly dependent on the MWCNT contents. Wear resistance was found to increase with increase in the MWCNT fraction in the nanocomposite. The worn surface and the shape as well as the size of the debris were studied under scanning electron microscopy (SEM). Energy-dispersive spectroscopy (EDS) analysis of the worn surfaces was used to detect and measure the constituents present in the debris. SEM micrographs of the nanocomposites show that the features of the wear debris are completely altered when MWCNT was added. Further the wear mechanism underwent a change from oxidative in LM6 to that of ploughing in LM6 nanocomposite. The recorded surface roughness values also confirm the above findings and show significantly reduced surface roughness (∼82%, 0.75 wt% MWCNT). These results clearly demonstrate the advantage of addition of MWCNT for enhancing resistance to wear in LM6 alloys.

Sustainable assessment of low viscous biofuel for compression ignition engine using wear debris analysis – Tribological investigation

Lemon Peel oil is one of the ecologically friendly biofuels has many significant benefits for power generation applications. Lemon peel oil is extracted from left over rinds of lemon peel by applying steam distillation process. In the present work, long term durability study of compression ignition engines has been conducted using 10 % and 20 % low viscous biofuels with diesel. For 256 h long term durability investigation, wear debris, ferrography evaluation of lubricating oil samples and cylinder liner surface roughness analysis are conducted for these fuels operations. The results have revealed that higher metal wear concentrations are observed in the samples of lubricant oil with LPO20 compared with LPO10 and diesel. It is due to more oxidation of fuel and lubricant oil dilution while running the engine. Similarly, wear of the engine cylinder liner surface is also more when the engine is fuelled with LPO20 as compared to LPO10 and diesel. In addition, Ferrography results have shown a higher wear concentration of lubricant samples collected from the LPO20-operated engine compared with other used fuels. Finally, it is concluded that has poor engine life due to its low viscous nature and it could be improved by suitable reformulation for long term application.

Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

AbstractFerrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

Motion simulation analysis of wear debris in an integrated detection unit for lubricating oil

Accurate wear debris detection is vital for diagnosing engine wear. However, due to uncertainties in debris behavior, size, and operating conditions, identifying different materials with a single sensor is challenging. To improve sensitivity and accuracy, we propose an integrated detection unit for wear debris in lubricating oil, emphasizing multifunction integration and active intervention. This unit includes a static mixer, a flared transition zone, and a sensor-equipped detection zone. Parametric analysis reveals that debris mixed in the static mixer tends to aggregate toward the center, forming three distinct distribution rings at the detection unit's outlet. A 90° blade angle and a 1:1 aspect ratio enhance the difference in debris quantity between inner and outer rings. To enhance detection reliability, installing capacitive sensors at the front and electromagnetic sensors at the rear of the detection zone can improve the accuracy of detecting different materials debris. This concept offers a multifunctional solution for precise wear debris detection, addressing complex wear scenarios in engines.

Experimental investigation on rolling contact wear in grease lubricated spherical roller bearings using microcomputed tomography (μCT)

This study presents a novel and non-destructive method for quantitatively analysing grease lubricated bearing wear by using X-ray microcomputed tomography (μCT). The efficacy of μCT to measure rolling contact wear via wear debris analysis was demonstrated, as was quantifying grease contamination and wear debris identification. Test rig experiments of grease lubricated spherical roller bearings, were performed to simulate rolling contact wear. Six grease samples were collected from two bearings at three different intervals to investigate wear progression. Results showed μCT measured wear volume increased exponentially with increasing bearing operation times. Grease cleanliness (particle counts per ml) worsened nonlinearly due to the exponential growth of wear debris over longer bearing operational times. Comparing the conventional ISO281 bearing assembly and operating condition assessment, a particle-count based method via μCT analysis provided a less subjective and dynamic assessment for quantifying grease contamination. Grease contamination was found to increase with longer operational timeframes due to the combination of wear debris growth and decreased lubrication film thickness. Finally, laminar, chunk and spherical wear particles, associated with rolling contact wear, were identified by the morphometric parameters measured by μCT. This study highlights the potential of μCT as a wear monitoring and grease quality assessment technique for railway bearing applications.

202. In vivo analysis of wear debris produced from titanium plasma-sprayed implants: A microcomputed tomographic investigation

202. In vivo analysis of wear debris produced from titanium plasma-sprayed implants: A microcomputed tomographic investigation

The Effect of Spherical Hybrid Silica–Molybdenum Disulfide on the Lubricating Characteristics of Castor Oil

Abstract This investigation demonstrates the effect of a structural hybrid of spherical silica and lamellar molybdenum disulfide (MoS2) combined to form a sphere used as an antifriction and antiwear additive in vegetable oil in steel-on-steel tribopair. Hybrids demonstrated improved dispersion stability due to the deposition of lightweight silica on the surface of hydrothermally prepared 2D sheets of MoS2. The concentration of nanohybrid was optimized for optimal lubricant performance, and the best region of test space is presented in this work. At the optimum concentration, the coefficient of friction (COF) was 0.03236, with an average wear volume of 2.16 × 10−12 m3. The synergism of the particles significantly reduces friction and wear. The collision of the hybrid spheres with the surface has an immediate effect on it. The broken sphere of wear debris was observed under scanning electron microscopy. The wear debris analysis indicates that the lubrication mechanism begins with the rolling of hybrid spheres and ends with the rolling and sliding of silica and MoS2.

High-temperature dry sliding wear behavior of hybrid aluminum composite reinforced with ceria and graphene nanoparticles

Wear and friction occur naturally in the mating surfaces of moving machine parts. Overcoming friction can waste as much as 30 percent of the energy used. The last ten years have seen research on optimizing the amount of a particular nanoparticle additive for a certain tribological feature. The primary goal of this study is to improve tribological materials by combining two types of nanoparticles as reinforced to an aluminum base alloy. Hybrid Aluminum Nanocomposites (HANCs) made of cerium oxide or ceria nanoparticles (CeO2) and graphene nanoplatelets (GNPs) reinforced in Al-6061 alloy were subjected to a wear test at temperatures ranging from 250 °C to 1000 °C at different loads ranging from 15 N to 60 N, and the sliding velocity and distance held constant at 3 m/s and 2000 m, respectively. The nanocomposites were characterized using Scanning Electron Microscopy (SEM) and Energy-Dispersive Spectroscopy (EDS). The wear rate of the Al-6061/3CeO2/3GNPs was found to be superior and the same is confirmed by the worn-out surface and wear debris analysis. At different high temperatures and at various loads; abrasion, adhesion oxidative, plastic deformation, and delamination wear were observed as wear mechanisms during the dry sliding wear test. However, there were challenges encountered during the wear test, such as extreme operating conditions, realistic modeling, test standardization, and multiscale wear analysis.

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network.

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.

Probability-weighted ensemble support vector machine for intelligent recognition of moving wear debris from joint implant

Friction-induced wear debris from joint implants are effective resources in investigating artificial joint wear and cellular immune response mechanisms. To improve the accuracy of wear debris analysis, an intelligent recognition method is developed for wear debris measurement under motion conditions. In this method, the multi-view image sequence of moving wear debris is captured to acquire the variations of aspect ratio, area, and roundness features. Then multiple SVM models are integrated to identify wear debris types based on weighted probability to improve the accuracy. The proposed method can achieve a classification accuracy of 90.51%, which is better than HIVE-COTE2.0, MultiRocket, and other time series classification algorithms. This method can be applied to monitor wear status of artificial joint articulating surfaces.

Tunable magnetophoretic method for distinguishing and separating wear debris particles in an Fe-PDMS-based microfluidic chip.

Wear debris analysis provides an early warning of mechanical transmission system aging and wear fault diagnosis, which has been widely used in machine health monitoring. The ability to detect and distinguish the ferromagnetic and nonmagnetic debris in oil is becoming an effective way to assess the health status of machinery. In this work, an Fe-poly(dimethylsiloxane) (PDMS)-based magnetophoretic method for the continuous separation of ferromagnetic iron particles by diameter and the isolation of ferromagnetic particles and nonmagnetic particles with similar diameter by type is developed. The particles experience magnetophoretic effects when passing through the vicinity of the Fe-PDMS where the strongest gradient of the magnetic fields exists. By choosing a relatively short distance between the magnet and the sidewall of the horizontal main channel and the length of Fe-PDMS with controlled particles flow rate, the diameter-dependent separation of ferromagnetic iron particles, that is, smaller than 7µm, in the range of 8-12µm, and larger than 14µm, and the isolation of ferromagnetic iron particles and nonmagnetic aluminum particles based on opposite magnetophoretic behaviors by types are demonstrated, providing a potential method for the detection of wear debris particles with a high sensitivity and resolution and the diagnostic of mechanical system.

Experimental and statistical analysis of wear on gear material

AbstractThe objective of this paper is to study the parameters that affect the remaining useful life of lubricant (RULL). Fourier‐transform infrared spectroscopy (FTIR) of gear oil is used to observe the contamination in the oil. Coefficient of friction (COF) has been found out using pin on disc tribometer using gear oil, and simultaneously, wear debris has been collected. Particles distribution and elemental analysis of wear debris were observed by emission scanning electron microscopy (FESEM) Field with energy dispersive x‐ray (EDX) respectively. Oxidation, sulfation, soot and water content in lubricant increase with time and decrease the RULL. ANOVA is applied to find out the most influential parameter for wear debris and found a load on gears is the dominating factor for wear. This method is the potential to monitor the root causes of contamination and change in lubricant properties and may help to detect the other early signs of failure.

An Investigation on the Enhanced Wear Behavior of Ultrasonically Stirred Cast A356/SiO2np Nano-composites

Metal matrix nanocomposites (MMNCs) are becoming the materials of choice in a variety of engineering and medical applications owing to their exhibiting a superior combination of targeted properties. Amongst different MMNCs, aluminum-based composites are of special importance. In many applications, a relatively inferior wear property limits the use of this valued metal in practice. However, reinforcing aluminum and its alloys by ceramics, carbon allotropes, etc., may circumvent these limitations to a great extent. In the present study, aluminum alloy A356/SiO2 nanocomposite is fabricated by a vibration-assisted casting process, wherein varied amount of nanosilica, namely, 0.125, 0.25, and 0.375 wt.%, have been added to the melt. The use of power ultrasonic treatment had a great influence on the microstructure, hardness, and wear properties. Microstructural and XRD analyses were performed on the fabricated monolithic and composite samples. To evaluate wear behavior, a hardness test and pin-on-disk experiment were conducted on the samples under 60, 80, and 100 N forces at a constant speed of 1 m/s and the sliding distance was varied from 1000 to 2000 m. The abraded surfaces, wear debris, and EDS analysis were used to identify wear mechanisms. The samples having 0.125 wt.% exhibited the highest increase in hardness and the highest reduction in both friction coefficient and wear rate by 52%, 50%, and 68%, respectively. The main governing wear mechanism was abrasion, with limited evidence of delamination.

Numerical Simulation and Experimental Investigation on Tribological Properties of Gear Alloy Surface Biomimetic Texture

A biomimetic microtexture was constructed on the surface of gear alloys, and the stress distribution and oil film pressure of the textured tooth surface were evaluated by numerical simulation. The tribological test of the textured gear alloy was carried out using a "reciprocating ball-on-flat" tribotester, and the friction mechanism was analyzed. The influence of microtexture on the tribological properties of gear alloys was analyzed by cross-checking between the simulation and experiment. The simulation results show that all three biomimetic textures can change the stress contact state of the tooth surface. The oil film pressure of the textured tooth surface is higher than that of the untextured tooth surface. The experimental results show that the textured alloy has excellent tribological properties. Among these, the crescent texture has the best comprehensive performance, and the friction coefficient and wear rate can be reduced by more than 30% and 95%, which is the result of the dynamic pressure effect of the texture and debris storage. In the analysis of wear marks and debris, it is found that the small size of debris wrapped by thick carbon material is helpful to improve the tribological properties of gear alloy. Large-size debris is the main cause of plowing and wear.