Wear Conditions Research Articles

Mechanical and tribological performance of NaOH-Treated bamboo fiber-reinforced recycled HDPE composites under fretting wear conditions

ABSTRACT The study investigates the impact of bamboo fiber reinforcement on the mechanical and tribological properties of recycled high-density polyethylene (rHDPE) composites. Bamboo fibers extracted from locally sourced Bambusa Balcooa were subjected to a comprehensive alkali treatment to enhance their properties, including increased crystallinity, thermal stability, and altered morphology. The treated bamboo fibers were incorporated into rHDPE using a compression molding technique at varying weight percentages (0.5–2.0 wt-%). The results revealed that optimal mechanical performance, particularly tensile strength (155.7 MPa) and modulus (6.6 GPa), was achieved at 1.5 wt-% fiber content, attributed to effective load transfer between the matrix and fibers. Beyond this content, performance declined due to fiber agglomeration and poor dispersion. Flexural properties mirrored these findings, with the highest strength (207.5 MPa) and modulus (11.7 GPa) observed at 1.5 wt-%. Thermogravimetric analysis (TGA) and X-ray diffraction confirmed enhanced thermal stability and crystallinity in treated fibers. Fretting wear tests indicated a reduction in the coefficient of friction with increased fiber content, particularly at lower loads, while wear loss was minimized at 1.5 wt-% BF, suggesting optimal reinforcement. SEM analysis highlighted microstructural changes, including fiber pull-out, debonding, and void formation, correlating with the observed mechanical degradation at higher fiber contents. .

The Function and the Principle about How Self-healing Materials are Used in Metal Wear Condition

Due to the wide range of metals used in different industries, the increase in the service life of metals and metal materials has always been a more important research direction. Relevant research has proved that self-repairing materials in lubricants can help machines to repair themselves by utilizing self-modifying properties, thus restoring them to a better working condition. This paper mainly summarizes and analyzes the experimental materials by means of literature review, and discusses the working principle of such materials and the application of self-repairing materials in industrial production. The article also confirms through its analysis that during the operation of the machine, the composite lubricant reacts with the metal surface and forms a thin film, which results in a substitution reaction. In this way, the damaged metal is replenished by the composite material and the machine can return to normal operation. However, this method can only be applied to machines that work for a short period of time, and is not able to solve the problem of large areas of severely damaged machines, so the relevant materials to assist in the modification of the metal still need to be continuously developed and researched.

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies

Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies

Microstructure and tribological behavior of Al–TiC composite strips fabricated by a multi-step densification method

This study aims to enhance the tribological properties of automotive applications by examining the effects of TiC content on the microstructure, mechanical properties, and wear behavior. This study investigates the production of Al–TiC composite strips using a novel multi-step densification process combining mechanical alloying and hot rolling with TiC concentrations ranging from 0 to 12 vol%. The novelty of this work lies in its comprehensive approach to developing and analyzing Al–TiC composite strips using a multistep densification method. This study integrates microstructural analysis, mechanical property evaluation, and detailed tribological behavior assessment under different wear loads (5–25 N). A key innovation is the application of the Abbott Firestone method to analyze worn surfaces, providing insights into optimal wear conditions. The study reveals that increasing the TiC content to 12 vol% significantly improves densification, hardness (up to 268.8% increase), and wear resistance (up to 95% improvement at a 5N load). Dry ball-on-flat sliding wear tests at loads of 5–25N demonstrate that TiC particles hindered complete delamination wear in the composite strips. The Abbott Firestone method analysis of worn surfaces indicated an optimal exploitation zone in the Al-6 vol% TiC composite at both low and high wear loads. This comprehensive approach provides valuable insights into optimizing Al–TiC composites for enhanced performance in automotive components that require improved wear resistance.

Research on Tool Wear Monitoring Based on Enhanced Convolutional Neural Networks

Abstract Identifying the wear status of cutting tools during the machining process is essential because failure to promptly replace severely worn tools can significantly impact the quality of workpiece machining. Presently, machine learning methods are predominantly utilized for monitoring cutting tool wear status. However, these methods rely on manual feature extraction and exhibit low accuracy. This study introduces a novel RRP-Net model, built upon the RepVgg and ResNet frameworks, integrating a parameter-free self-attention mechanism called SimAM to expedite the model’s solving speed without increasing parameters. Within the foundational module of the model, a structural reparameterization approach is employed to transform the multi-branch structure during training into a single-branch structure during validation. This method not only enhances model accuracy but also accelerates the model validation process. The publicly available cutting data from PHM2010 is employed for model training and validation. The findings demonstrate that RRP-Net surpasses classical convolutional neural network models in identifying cutting tool wear status within the PHM2010 dataset, achieving an average accuracy of 98.65% and enhancing recognition accuracy on relevant datasets by 2.41%. To verify the model’s practical applicability, specificity and recall during the Break stage are computed at 99.73% and 98.18%, respectively, affirming the model’s exceptional robustness and stability. The heightened accuracy and efficiency of RRP-Net further broaden its applicability within the industrial domain.

Effect of Cr3C2 on wear behavior of Ni45 laser cladding coating

Abstract To improve the performance of engineering components subjected to severe wear, temperature fluctuations, and corrosive conditions in industries such as chemical, aerospace, and power plants, Ni45-based coatings with varying Cr3C2 contents (0%, 10%, 20%, and 30%) were applied to a 45-steel surface. The microstructure of these coatings was examined by scanning electron microscopy (SEM), and their wear resistance was compared. As the Cr3C2 content in the cladding layer increased, there was a notable improvement in its wear resistance. Specifically, when the Cr3C2 content reached 30% by weight, the wear resistance of the cladding layer exceeded that of the GCr15 steel. Furthermore, the adhesive wear between the cladding layer and the GCr15 steel diminished, while the oxidation wear of the GCr15 steel intensified. This study aims to elucidate the primary wear mechanisms of the Cr3C2/Ni45 cladding layer and offer valuable insight into improving the durability of engineering components in harsh environments.

Effects of carbon content and heat treatment on wear-resistance and impact properties of novel cast steels with bainitic and martensitic structure

Abstract The traditional material for caterpillar boards is mainly Hadfield steel. The wear resistance of the Hadfield steel cannot be fully exerted due to insufficient work hardening during the operation of the caterpillar board. The effects of carbon content and heat treatment on the wear-resistant and impact properties of a novel caterpillar board cast steels with bainitic and martensitic structures have been investigated. The microstructural observation of worn surface topography was performed by scanning an electronic microscope, and the impact toughness was carried out by an impact tester, in a bid to improve the wear-resistant properties and its impact toughness. The results demonstrate that the hardness and wear-resistant properties of cast steels increase with the increase of carbon content, whereas the impact properties reduce because the brittle phases, such as the carbide precipitation and the martensite, are more. The heat treatment temperature of steels can change the relative ratio of bainitic and martensitic structures, thus affecting the wear-resistant properties of the steels. By controlling the carbon content and heat treatment processing of the steels, the optimal combination of toughness and wear-resistant properties can be achieved. The wear mechanism of dual-phase cast steel was analyzed through wear resistance tests, and it also shows that dual-phase cast steel with bainitic and martensitic structures had better wear-resistance properties compared to the Hadfield steel at low-impact wear conditions.

Volumetric efficiency degradation prediction of axial piston pump based on friction and wear test

The axial piston pump (APP) is being developed towards higher pressure and higher rotational speeds to enhance operational power density. The piston-cylinder friction pair is a critical component of the APP. Due to its lack of self-compensation capability, the leakage of the piston-cylinder friction pair escalates rapidly under increasingly severe wear conditions. An innovative method for predicting the performance degradation and lifespan of APPs based on friction and wear tests has been proposed. This method can effectively predict the performance degradation trends of APPs under different operating conditions. The actual contact force on the piston pair (PP) during operation is determined through dynamic analysis. Friction and wear tests were conducted on 38CrMoAl piston and ZCuPb15Sn8 cylinder materials under various conditions using a testing apparatus. Utilizing friction and wear theory, the volumetric efficiency of APP under various operating conditions was derived as a function of operational time. The reliability of the theoretical analysis was validated through leakage tests on the APP. The results indicate that volumetric efficiency decreases exponentially with increasing working pressure at rated speed. This research provides theoretical guidance and an experimental foundation for the failure prediction of volumetric efficiency degradation in APP.

Electro-Mechanical Behavior of Copper-Based Electrical Motor Brushes

This study was mainly performed in order to examine the properties of electric motor brushes (EMB). EMBs are electrically conductive and work under wear conditions due to direct contact with the moving part (rotor) of electric motors. In accordance with the scope of the study, EMB characteristics were investigated in two different groups and reproduced with a new technique at the end of this phase. New EMBs were produced via varying proportions of copper matrix, graphene, and silicon carbide reinforcements. The composite-alloy powder elements were carefully squeezed by a cold and single-axis hydraulic press under a pressure of 500 MPa (±5 MPa) following 8 hours of mechanical alloying (MA). The molded composite product (sample) was sintered at 900° C for 1 hour in a reducing gas atmosphere. 100 kPa spring pressure was tested on each sample thrice with 8, 16, 24, and 32 m/s rotational speeds and densities of 4, 8, 12, and 16 A/cm2. Following this process, the electrical conductivity, porosity, hardness, wear loss, surface roughness, and temperature changes were investigated for each sample. It was determined that the electrical conductivity decreased with increasing reinforcement ratio and, in terms of electrical conductivity, affected the brushes’ properties negatively.

Multi-layer formation by oxidation and solid-state crystallization of cold sprayed amorphous coatings during dry sliding wear

Amorphous alloy coatings, known for the exceptional wear resistance, have emerged as a key solution for enhancing the wear performance of magnesium alloys under harsh environments. In this study, Fe-based amorphous alloy coatings were deposited on magnesium alloy by cold spraying technology, and the influence of microstructural evolution on the wear performance of coatings under dry sliding wear conditions was discussed. The results showed that a dense adherent oxide layer with a thickness of ∼700 nm comprising nanograins of less than 8 nm was formed at the outmost surface, which played a role of self-lubricating. Underneath, a 1 μm thick nanocrystalline-amorphous layer with nanograins of ∼20 nm dispersed in the amorphous alloy matrix was formed through in-situ crystallization induced by flash temperature. This composite structure prevented the formation of shear bands in amorphous alloys and enhanced the durability. Therefore, the transition from abrasive wear to adhesive wear was a consequence of the microstructural evolution from a dual-phase composite layer to a self-lubricating oxide layer.

Research on tool wear condition monitoring based on deep transfer learning and residual network

To address the issues of sample scarcity and insufficient recognition accuracy in existing deep learning models for tool wear monitoring, this study developed a milling tool wear monitoring model that combines transfer learning (TL) and deep residual networks (ResNet). The model uses continuous wavelet transform to convert vibration signals into time-frequency maps, which are then fed into the network model for analysis. ResNet50 was selected as the base feature extraction model, and transfer learning techniques were employed to update the classification layer’s weights, enabling tool wear detection. The model based on ResNet-TL achieved a detection accuracy of 94%, significantly exceeding the threshold for intelligent tool wear recognition. This accomplishment markedly improves the precision and stability of tool wear state monitoring, providing more reliable technical support for tool management in manufacturing processes. Additionally, the method demonstrated superiority in addressing the small sample problem, paving the way for future research in tool wear monitoring. By integrating advanced deep learning techniques with transfer learning, the model not only enhances detection capabilities but also improves adaptability and robustness in practical industrial applications.

RTCA-Net: A New Framework for Monitoring the Wear Condition of Aero Bearing with a Residual Temporal Network under Special Working Conditions and Its Interpretability

RTCA-Net: A New Framework for Monitoring the Wear Condition of Aero Bearing with a Residual Temporal Network under Special Working Conditions and Its Interpretability

Identification of cutter flat wear for rock TBMs using vibration signal of shield

Identification of cutter wear status is one of the critical issues for the successful operation of tunnel boring machines (TBM). This study proposed a novel model for predicting the locations of cutters experiencing flat wear, leveraging measured vibration signals from the TBM shield. Field tests were conducted to collect vibration acceleration data using accelerometers. This paper outlines the equipment and setup for monitoring vibration acceleration and develops procedures for signal processing. Analysis of vibration acceleration patterns reveals distinct characteristics corresponding to varying numbers and locations of cutters with flat wear. A linear relationship can be identified between the frequency of different vibration acceleration peak intervals and the extent of flat wear on the cutters (number and location of wear cutters). The proposed model is then applied to a TBM tunnelling section in Guangzhou, China, and validated using on-site wear data. This method offers real-time insight into the potential need for cutter replacement during tunnelling operations in challenging hard rock conditions.

Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings

Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a cloud-edge device. Based on the grinding data and grinding wheel wear mechanism, a monitoring model using multi-sensor information fusion is constructed to assess the grinding wheel wear state. In addition, edge data acquisition and online monitoring software have been developed to improve the speed of data transmission and processing. Finally, based on the proposed framework, a cloud-edge device collaborative intelligent monitoring system for assessing grinding wheel wear during high-speed cylindrical grinding of bearing rings is constructed. It improves the grinding quality and efficiency, reduces the grinding cost, and incorporates remote control functionality.

Analysis of abrasive impact wear of the Mn8/SS400 bimetal composite using a newly designed wear testing rig

In this study, the abrasive impact wear behaviour of a bimetal composite made of medium manganese steels (MMSs) and low carbon steels (LCSs), i.e., the Mn8/SS400 bimetal composite, was investigated using a newly designed wear-testing rig. The need for a new rig arose from the difficulty in replicating real-world wear conditions. Our rig allows for precise control and measurement of wear, simulating harsh environments more accurately than other wear-testing rigs. The bimetal composite Mn8/SS400 demonstrated superior wear resistance, showing an improvement of up to 2.8 times compared to benchmark steels, attributed to its enhanced work hardening sensitivity. Scanning electron microscopy (SEM), X-ray diffractometer (XRD), and electron backscatter diffraction (EBSD) analyses were employed to elucidate the wear mechanisms. After 300 h of abrasive impact wear, the subsurface microhardness of Mn8 reached 601.31 HV, significantly higher than that of the matrix hardness of 292.24 HV, indicating a substantial work hardening effect. The wear mechanism of the Mn8/SS400 bimetal composite was found to be a synergistic effect of grain refinement strengthening, dislocation strengthening, and twin strengthening. Initially, twin strengthening was the dominant mechanism up to 200 h of wear testing. However, after 300 h, contributions from all three mechanisms became increasingly significant, enhancing the overall wear resistance of the composite.

Electrochemical Behavior of Plasma-Nitrided Austenitic Stainless Steel in Chloride Solutions.

The application possibilities of austenitic stainless steels in high friction, abrasion, and sliding wear conditions are limited by their inadequate hardness and tribological characteristics. In order to improve these properties, the thermochemical treatment of their surface by plasma nitriding is suitable. This article is focused on the corrosion resistance of conventionally plasma-nitrided AISI 304 stainless steel (530 °C, 24 h) in 0.05 M and 0.5 M sodium chloride solutions at room temperature (20 ± 3 °C), tested by potentiodynamic polarization and electrochemical impedance spectroscopy. Optical microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy are used for nitrided layer characterization. The experiment results confirmed the plasma-nitrided layer formation of increased micro-hardness related to the presence of Cr2N chromium nitrides and higher surface roughness compared to the as-received state. Both of the performed independent electrochemical corrosion tests point to a significant reduction in corrosion resistance after the performed plasma nitriding, even in a solution with a very low chloride concentration (0.05 mol/L).

Examining the Flexural Behavior of Thermoformed 3D-Printed Wrist-Hand Orthoses: Role of Material, Infill Density, and Wear Conditions.

This paper examined the mechanical properties of wrist-hand orthoses made from polylactic acid (PLA) and polyethylene terephthalate glycol (PETG), produced through material extrusion with infill densities of 55% and 80%. These orthoses, commonly prescribed for wrist injuries, were 3D-printed flat and subsequently thermoformed to fit the user's hand. Experimental and numerical analyses assessed their mechanical resistance to flexion after typical wear conditions, including moisture and long-term aging, as well as their moldability. Digital Imaging Correlation investigations were performed on PLA and PETG specimens for determining the characteristics required for running numerical analysis of the mechanical behavior of the orthoses. The results indicated that even the orthoses with the lower infill density maintained suitable rigidity for wrist immobilization, despite a decrease in their mechanical properties after over one year of shelf life. PLA orthoses with 55% infill density failed at a mean load of 336 N (before aging) and 215 N (after aging), while PETG orthoses did not break during tests. Interestingly, PLA and PETG orthoses with 55% infill density were less influenced by aging compared to their 80% density counterparts. Additionally, moisture and aging affected the PLA orthoses more, with thermoforming, ongoing curing, and stress relaxation as possible explanations related to PETG behavior. Both materials proved viable for daily use, with PETG offering better flexural resistance but posing greater thermoforming challenges.

Assessment of the Uniform Wear Bending Strength of Large Modulus Rack and Pinion Pair: Theoretical vs. Experimental Results

Due to long-term operation under low-speed and heavy-load conditions, large module gears and racks are inevitably subject to tooth surface wear. To investigate the changes in gear tooth bending strength, the Three Gorges ship lift was taken as the research object and a simulation test bench was established. An analytical method, a finite element method, and an experimental method were utilized to analyze the bending stress of gears under normal and various uniform wear conditions. The obtained results revealed that with the increase in wear degree and load, the bending stress of single-tooth meshing was significantly higher than that of double-tooth meshing, and the single-tooth meshing time also increased, which indicates that gear wear accelerated the process of performance degradation. Furthermore, the relative errors obtained by the three calculation methods were all at a low level. This investigation aims to provide a solid theoretical and experimental basis for the dynamic analysis of large module gear and rack transmission.