Wear Propagation Research Articles

Experimental investigations to assess surface fatigue failure in rolling contact bearing

Rolling element bearings are the most important components in almost all rotating machines. These bearings are often subjected to repetitive load cycles at different operating conditions. Excessive loads, speeds, and improper operating conditions lead to the propagation of defects on their load-bearing surfaces, thereby causing a negative impact on the performance of rotating machines. This paper presents the results of experimental investigations to assess wear propagation in roller bearings using lubricant degradation, vibration, and statistical parameter analysis methods. A roller bearing setup was developed in the laboratory, and the test bearing (NJ 307E) was subjected to fatigue tests over a period of 900 h. The bearing was operated at a speed and radial load of 800 rpm and 1 kN, respectively. The film thickness analysis revealed a transition in lubrication regimes during 600–900 h of operation. Grease structure degradation and oxidation analyses were carried out using scanning electron microscope images and the Fourier transform infrared radiation technique. Further, the vibration signals are extracted from the bearing housing at regular intervals. Using the fast Fourier transform technique, these vibration signals were used to analyze bearing fault frequencies to highlight the faults developed on bearing contact surfaces. The statistical features of vibration signals such as root mean square, kurtosis, and crest factor were used to assess the severity of wear propagated on the bearing contact surfaces. Integrating tribological and vibration parameter analysis techniques provided a reliable assessment of surface fatigue wear propagated on the roller-bearing contact surfaces.

Effect of Carbide Orientation on Wear Characteristics of High-Alloy Wear-Resistant Cast Irons

Both erosive and abrasive wear are common in mechanical systems and moving components in industrial production. Once they occur in machine parts, the task of breakdown maintenance should be carried out, leading to high production costs. Previous research has shown that high-chromium cast iron (HCCI), a commonly used wear-resistant material, experiences repeated particle impacts that break up the chromium carbides (M7C3) on its surface, resulting in the formation of fine fracture carbides. It has been reported that erosion wear occurs progressively due to the shedding of protrusions caused by the plastic deformation of the material’s matrix. Thus, the erosion wear characteristics of a material are strongly affected by carbides, which come in various shapes and can affect resistance. This research examined the orientation of carbides on the microstructure and their effects on erosion and abrasion properties. The wear tests showed that 27 wt.% chromium content high-alloy cast irons showed the best wear resistance properties due to the coarse strips of M7C3 carbides that effectively suppressed wear propagation. Additionally, the M2C carbides crystallized around the M7C3 carbides which support the M7C3 carbide to reduce plastic deformation and carbide peel-out. Consequently, the wear resistance properties of this material are significantly improved.

A fundamental approach to determine the impact of aramid and carbon fibers on durability and tribological performance of different polymer composites demonstrated in gear transmission process

Abstract When selecting a polymer or a reinforced polymer material for a specific drive technology, it is crucial to have a comprehensive understanding of the component’s operating characteristics, including the mechanics of tribological contact and the fatigue life of the materials used in gears. The choice of material plays a pivotal role in the gear running process, as it induces heat generation in the meshing zone and triggers flank wear progression along the high points of the teeth, which directly impacts the gear’s lifetime. This paper presents the results of durability testing conducted on steel/polymer gear pairs. The tested polymer materials were based on polyacetal and polyamide 66 chemical bases, reinforced with aramid, carbon fibers, and internally lubricated with polytetrafluoroethylene. The aim of the study was to evaluate the durability and thermal response of the materials and determine their correlation with flank wear rate progression. The results indicate that gears produced from polyacetal, and carbon-reinforced polyamide 66 materials exhibit the longest durability. Notably, the generated meshing temperatures are closely connected with flank wear propagation. The lowest wear coefficients were observed in gears produced from polyacetal and polyamide 66 materials reinforced with carbon fibers.

A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system

Gearbox has a compact structure, a stable transmission capability, and high transmission efficiency. Thus, it is widely applied and used as a critical transmission system in intelligent manufacturing systems, such as machine tools and robotics. The gearbox usually operates in harsh and non-stationary working environments, making the gear surface prone to wear. The progression of gear surface wear may lead to severe gear failures, such as gear tooth breakage and root crack, potentially damaging the whole gear transmission system. Therefore, it is essential to assess the gear surface wear progression and predict its remaining useful life (RUL) in order to ensure the reliable operation of the gear transmission system. To this end, this paper developed a novel gear wear prognostic scheme based on vibration analysis for gear health management. More specifically, a novel health indicator (HI) is first developed for gear wear monitoring in the proposed prognostic scheme. The novel HI, inferred from the cyclic correntropy and Wasserstein distance (WD), can accurately reflect the wear-induced cyclic correntropy spectra distribution change over time. Therefore, the novel HI can robustly evaluate the gear wear severity with high accuracy. With the developed HI, a network, namely the optimized gated recurrent unit (GRU), is applied for predicting the gear transmission system RUL during surface wear progression. As for the optimized GRU network, the genetic algorithm (GA) is applied to find the optimal hyperparameters adaptively, which can significantly improve the practicality of the developed prognostic scheme. To conclude, the developed prognostic scheme can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. A series of endurance tests are conducted to verify the effectiveness of the developed prognostic scheme for gear health management in surface wear progression.

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.

Digital twin-driven intelligent assessment of gear surface degradation

Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.

Towards modeling of Grid to Rod and the effect of its nonlinear dynamic behaviors on grid-to-rod fretting wear

The grid-to-rod fretting (GTRF) wear is always a significant cause of fuel failure. The dynamic behavior of fuel rods during actual operation is complex, so understanding the dynamic mechanism of the grid-to-rod system is essential for wear analysis. This paper developed a mathematical model of a fuel rod subjected to sinusoidal excitation loads with gap constraint. The partial differential equation was discretized by the Galerkin method and solved by Runge–Kutta scheme. The vibration response and the wear rate of cladding in the possible gap range under numerous hypothetical cases were studied. The result shows that the nonlinear vibration motion significantly impacts GTRF wear. The wear rate changes are linear under harmonic and low-period vibration, and most high-period movement significantly affects the wear rate. Some exciting and fresh results allow us to understand better GTRF wear propagation and optimize fuel assembly design.

A novel similarity-based status characterization methodology for gear surface wear propagation monitoring

The gearbox is a vital component for rotating machinery and has been used in many critical engineering applications. Surface wear is a common but inevitable phenomenon during the lifespan of the gearbox. Its propagation can result in some catastrophic failures and cause unexpected economic loss. Therefore, it is vital to evaluate the degradation process of the gear system caused by surface wear propagation in order to make reliable predictive maintenance-based decisions to ensure the safe operation of the gearbox transmission system. The vibration analysis technique is a prevailing tool for rotating machine condition monitoring. However, research on vibration-based gear wear monitoring is relatively rare as the dynamic interactions between gear surface wear and gear system dynamic characteristics would produce complex gear dynamic responses and vibration features. Therefore, this paper presents a novel similarity-based status characterization methodology for gear wear monitoring. In this proposed methodology, a novel gear wear monitoring indicator is developed to evaluate gear tooth contact characteristics at different wear severities, which could significantly benefit gear systems' health management. The effectiveness of the proposed method for gear wear propagation process monitoring is presented and proven through a series of run-to-failure tests with different lubrications and operational conditions.

Optimization of Cutting Parameter for Machining Ti-6Al-4V Titanium Alloy

Titanium alloy Ti-6Al-4V, has been broadly used in industries, primarily medical manufacturing, and aerospace, due to their considerable mechanical properties. Aerospace structural components made of titanium alloy (Ti-6Al-4V) material, a difficult-to-machine material which results in massive cutting force, steep cutting temperature, and significant tool wear. In this research, carbide insert tool is used to cut a Ti-6Al-4V titanium block at constant depth of cut of 0.50 mm using the method of dry machining is investigated with the aim of estimating the effects of two manipulated cutting parameters, which are spindle speed (140 and 150 m/min), and feed rate (0.1 and 0.2). This was done to observe their effects on the tool wear of the insert. The objective of this study is to study the machinability performance of coated carbide insert tool under dry machining condition, and to optimize the cutting parameter to machine Ti-6Al-4V using dry machining method. It was found in this research that lowest valued parameter, Parameter 1 (140 m/min, 0.1 mm/rev) caused the most flank wear, and the roughest surface on the Ti-6Al-4V block. Lower speeds can possibly inflict higher shocking force, which leads to higher wear propagation.

Non-Linear Wear Propagation Property and Prediction Method Having Influencing Pitting Failure of Helical Gears

Non-Linear Wear Propagation Property and Prediction Method Having Influencing Pitting Failure of Helical Gears

A novel cyclic-correntropy based indicator for gear wear monitoring

Gearbox is a vulnerable component of a turbine’s drivetrain and plays a vital role in the power transmission in wind turbines. Wind turbines usually operate under harsh working environments, such as in deserts, oceans, and on hills. The adverse operating conditions (such as inevitable fluctuating wind loads and speeds) make the gearbox transmission prone to reliability degradation and premature failure. Gear wear is a common and unavoidable surface degradation phenomenon during the lifespan of the gear transmission system. The gear wear propagation can result in severe failures, such as gear surface spalling, gear root crack, and gear tooth breakage, all of which could lead to the failure of the drivetrain system of wind turbines and bring unexpected economic loss, even serious accidents. Thus, it is crucial to monitor the gear wear propagation progression in order to enable reliable and safe operation. To this end, this paper develops a novel vibration-based health indicator to monitor the gear surface degradation induced by gear wear progression. With the help of the novel indicator developed, the health status of the gearbox can be well evaluated and thus predictive maintenance-based decisions can be made to reduce maintenance costs and minimize gearbox failures in wind turbines. A series of endurance tests under different lubrication conditions and operational conditions are carried out to verify the effectiveness of the gear wear monitoring indicator.

Pantograph Sliding Strips Failure-Reliability Assessment and Damage Reduction Method Based on Decision Tree Model.

Damage to the pantograph or sliding strip may cause the blocking of the railway line. This is the main reason for which the prediction of pantographs’ failure is important for railway carriers and researchers. This article presents a sliding strips failure prediction method as a main means of preventing disruptions to the transport chain. To develop the best predictive model based on the decision tree, the complex tree, medium tree and simple tree machine learning methods were tested. Using a decision tree, the categorization of the given technical conditions can be properly realized. The obtained results showed that the presented model can reduce sliding strip failure by up to 50%. Special attention was paid to the current collector (AKP-4E, 5ZL type), measured during periodic reviews of locomotives EU07 and EU09. To assess the reliability of the selected pantograph strips, a non-destructive degradation analysis was carried out. On the basis of the wear measurements of the strips and the critical value of wear, a failure distribution model was developed. Operational data, collected during periodic technical reviews, were provided by one of the biggest railway carriers in Poland. The results of the performed analyses may be used to build a preventive maintenance strategy to protect pantographs. The applied reliability models of wear propagation can be extended by the parameters of the cost and repair time becoming the basis for estimating the costs of operation and maintenance.

Numerical Wear Analysis of a PLA-Made Spur Gear Pair as a Function of Friction Coefficient and Temperature

Polylactic acid (PLA)-made machine elements exhibit easy machining, biodegradability, and excellent mechanical properties. However, enhancing their wear resistance is still a crucial engineering point, which may be achieved by altering (lowering) their coefficient of friction (CoF). Therefore, the first aim of this paper is to analyze how wear is affected by the alteration of CoF. The second aim is connected to the fact that PLA is sensitive to heat, which also limits its applicability. Accordingly, the next goal is to explore the effect of temperature on wear propagation. This study answers these questions by means of multibody dynamics simulations of a PLA-made spur gear pair. Simulations were carried out under constant torque, while the CoF and the temperature were varied in a normal operation domain (CoF: 0.1–0.05, T = 20–30 °C). The results showed that the wear volume gradually began to decline at approximately 0.085 CoF, whilst convergence to steady-state wear could be observed at 0.05 CoF. In conclusion, alteration of the CoF can lower wear by 35%, in this specific domain, while even a 5 °C rise in temperature causes 40% wear progression. The feasibility of the numerical procedure was validated by comparing numerically and experimentally obtained wear–torque results.

Establishment of a Rolling-Sliding Test Bench to Analyze Abrasive Wear Propagation of Different Bearing Materials for Knee Implants

Currently, new materials for knee implants need to be extensively tested but such tests are expensive in a knee wear simulator in a realized design. However, using a rolling-sliding test bench, these materials can be examined under the same test conditions, but with simplified geometries. In the present study, the test bench was optimized, and forces were adapted to the physiological contact pressure in the knee joint using the available geometric parameters. Various polymers made of polyethylene and polyurethane, articulating against test wheels made of cobalt-chromium and aluminum titanate, were tested in the test bench using adapted forces based on ISO 14243–1. Polyurethane materials showed distinctly higher wear rates than polyethylene materials and showed inadequate wear resistance for use as knee implant material. Thus, the rolling-sliding test bench is an adaptable test setup to evaluate newly developed bearing materials for knee implants. It combines the advantages of screening and simulator tests and allows for the testing of various bearing materials under physiological load and tribological conditions of the human knee joint. The wear behavior of different material compositions and the influence of surface geometry and quality can be initially investigated without the need to produce complex implant prototypes of total knee endoprosthesis or interpositional spacers.

Knife-edge interferogram analysis for corrosive wear propagation at sharp edges.

This paper presents a novel noncontact measurement and inspection method based on knife-edge diffraction theory for corrosive wear propagation monitoring at a sharp edge. The degree of corrosion on the sharp edge was quantitatively traced in process by knife-edge interferometry (KEI). The measurement system consists of a laser diode, an avalanche photodiode, and a linear stage for scanning. KEI utilizes the interferometric fringes projected on the measurement plane when the light is incident on a sharp edge. The corrosion propagation on sharp edges was characterized by analyzing the difference in the two interferometric fringes obtained from the control and measurement groups. By using the cross-correlation algorithm, the corrosion conditions on sharp edges were quantitatively quantified into two factors: lag and similarity for edge loss and edge roughness, respectively. The KEI sensor noise level was estimated at 0.03% in full scale. The computational approach to knife-edge diffraction was validated by experimental validation, and the computational error was evaluated at less than 1%. Two sets of razor blades for measurement and control groups were used. As a result, the lag will be increased at an edge loss ratio of 1.007/µm due to the corrosive wear, while the similarity will be decreased at a ratio of 5.4×10-4/µm with respect to edge roughness change. Experimental results showed a good agreement with computational results.

An experimental approach to study the effect of wear on traction coefficient and dynamic response of roller bearing

Cylindrical roller bearings are generally used to support rotating shafts mounted in a rotating machinery. These machine elements are prone to premature failure due to increase in friction which causes the heat generation and gradual wear on rolling contact surfaces of the bearing. Bearing failure leads to unexpected shutdown of machinery, therefore several condition monitoring methods have been developed to detect and diagnose the bearing faults. The purpose of this paper is to establish a relationship between surface roughness and wear on the traction coefficient parameter of roller bearing and corresponding dynamic response. Results obtained from the experimental investigations highlight dependency of traction coefficient and dynamic response of the bearing on wear propagation on the rolling contact surfaces.

Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction

Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.

Tool Wear Characterization and Monitoring with Hierarchical Spatio-Temporal Models for Micro-Friction Stir Welding

Abstract Tool condition in manufacturing plays a significant role on process dynamics and part quality. Effective modeling and monitoring of tool condition deterioration can provide the technical basis for maintaining production efficiency and quality. Inspired by the need of tool condition monitoring in joining dissimilar materials, especially the micro friction stir welding (μFSW) process, this paper aims to model and monitor the spatial and temporal patterns in the dynamic tool wear propagation in μFSW. A hybrid hierarchical spatio-temporal model is developed for the time-ordered, high-dimensional tool surface measurement images to characterize the dynamic tool wear propagation in μFSW. The model is developed in a hierarchical Bayesian structure with the first level being a data-driven regression model for the high-resolution tool pin profile images and the second level being a physics-based advection-diffusion model for the welding temperature distribution. Kalman filter is adopted to estimate the posterior distributions of the state variable (temperature distribution) and the error between the measured tool surface image and the predicted images. Regularized Mahalanobis distance is proposed to monitor tool wear progression. Numerical studies on three abnormal tool wear progression patterns demonstrate the effectiveness of the proposed spatio-temporal modeling method, as well as the timeliness, confidence, and power of detection. Results show that abnormal tool wear progression can be detected within 2 time-steps, the detecting accuracy of abnormal tool wear progressions can reach 99%, and the Type II error rates remain at 0. The method developed in this paper is expected to facilitate early detection of abnormal tool wear progressions, reduce the efforts in manual inspection, and support smart manufacturing. The proposed methods can be easily extended to other manufacturing processes with online sensing and tool measurement capabilities.

Musculoskeletal Multibody Simulation Analysis on the Impact of Patellar Component Design and Positioning on Joint Dynamics after Unconstrained Total Knee Arthroplasty.

Patellofemoral (PF) disorders are considered a major clinical complication after total knee replacement (TKR). Malpositioning and design of the patellar component impacts knee joint dynamics, implant fixation and wear propagation. However, only a limited number of studies have addressed the biomechanical impact of the patellar component on PF dynamics and their results have been discussed controversially. To address these issues, we implemented a musculoskeletal multibody simulation (MMBS) study for the systematical analysis of the patellar component’s thickness and positioning on PF contact forces and kinematics during dynamic squat motion with virtually implanted unconstrained cruciate-retaining (CR)-TKR. The patellar button thickness clearly increased the contact forces in the PF joint (up to 27%). Similarly, the PF contact forces were affected by superior–inferior positioning (up to 16%) and mediolateral positioning (up to 8%) of the patellar button. PF kinematics was mostly affected by the mediolateral positioning and the thickness of the patellar component. A medialization of 3 mm caused a lateral patellar shift by up to 2.7 mm and lateral patellar tilt by up to 1.6°. However, deviations in the rotational positioning of the patellar button had minor effects on PF dynamics. Aiming at an optimal intraoperative patellar component alignment, the orthopedic surgeon should pay close attention to the patellar component thickness in combination with its mediolateral and superior–inferior positioning on the retropatellar surface. Our generated MMBS model provides systematic and reproducible insight into the effects of patellar component positioning and design on PF dynamics and has the potential to serve as a preoperative analysis tool.

Corrosion Performance and Wear Behaviour of AA6061 Reinforced Hybrid: Nano-Rice Husk Ash/Clay Particulate for Cooling Tower Fan Blade in 0.75 M H2SO4

The failures of material performance in industry are due to structural, corrosion and wear dilapidation in service. This study attempts to develop AA6061 with rice husk ash and clay hybrid to address this phenomenon for application in cooling tower fan blade. This study also targets at developing sustainable and long-lasting hybrid of aluminium biodegradable composites through metallurgical stir casting techniques. Corrosion properties were examined using Potentiostat Galvanostat (PGStat 101) with Nova software acquisition while pin-on-disc wear machine was used to analyse the wear propagation of the composites. The microstructure, chemical composition and quantification were examined with SEM/EDS. It was established that the reinforcement improved corrosion susceptibility of the composites in 0.75 M H2SO4 solution. The coefficient of friction and the wear resistance of the hybrid composites were better than the as-received sample. Furthermore, AA6061/8% rice husk ash + clay at 75 µm possesses rod-like pattern and unique structure grain crystal.