Wear Degradation Research Articles

Wear mechanisms and failure analysis of a tool used in refill friction stir spot welding of AA6061-T6

Investigating tool wear in refill friction stir spot welding (RFSSW) is essential for understanding its limitations and improving its efficiency. Increasing the tool's service life is important to push the technology's readiness level and transfer the technology from the laboratory to industrial applications. In this study, the quasi-static lap shear performance of AA6061-T6 similar welded spots was investigated and tool wear was continuously monitored until tool failure at 3450 welding cycles. Furthermore, the fundamentals of the wear mechanisms in RFSSW were further elucidated. The investigation shows that it is possible to achieve steady quasi-static lap shear performance of the spot welds over advancing tool wear by adjusting the heat input to compensate for losses in frictional heat generation efficiency (related to tool profile changes by abrasion). A subsequent tool failure case analysis showed the main causes for the continuous wear degradation of the shoulder. Tool wear was driven by plastic deformation of the hot-work tool steel and subsequent break-out of tool steel ridges, introducing big hard particles into the contact region between the moving and rotating tools. In addition, the formation and detachment of Fe-Al intermetallic compounds counteract with the rotating tools and increase tool wear.

Effect of macro- and micro-morphology on fluid film properties based on plasto-elastohydrodynamic lubrication

Purpose The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities in contact, which may further affect the fluid-film characteristics. Design/methodology/approach Geometric morphology is integrated into the PEHL model to elucidate the fluid-film properties governed by both macro- and micromorphologies. Findings Results show the model, accounting for combination of elastic and plastic deformations, realistically reveals fluid film distribution affected by the significant pressure highly concentrated within surface micro roughness interaction. The designed macroscopic textured surface mitigates the fluid film rupture phenomenon and prevents accumulated wear degradation from plastic deformation. Originality/value The PEHL model takes into account both elastic and plastic deformations and realistically reveals the fluid film distribution affected by large pressures that are highly concentrated in surface micro-roughness interactions. The macro-textured surfaces are designed to mitigate fluid film rupture phenomena and prevent cumulative wear caused by plastic deformation. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0170/

Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process

In the cutting process, reasonable evaluation of tool remaining useful life (RUL) is essential to ensure machining stability and safety. However, traditional data-driven methods rely on datasets with complete degradation data and prior knowledge, which are usually difficult to obtain in real scenarios. For these problems, this paper proposed a model-data hybrid driven approach for RUL prediction of cutting tool based on improved inverse Gaussian process. The tool wear degradation is considered as a random process, the cutting physical model and the data-driven stochastic process model are fused, so that the prior knowledge from the physical model and the statistical law of measured data are fully utilized to improve the accuracy of tool RUL prediction. The cutting physical model is established, and the prior knowledge of tool wear degradation is obtained based on physical simulation method. A data-driven random effects inverse Gaussian process model is constructed for tool RUL modeling and prediction. According to Bayesian method, the unknown parameters are estimated and updated integrating physical simulated and measured wear data, so as to obtain the probability density function for specific tool RUL, and quantify the prediction uncertainty. Finally, comparative experiments are conducted to validate the performance of the proposed method. The results suggest that the hybrid approach has the best performance in different tool RUL prediction.

Investigating the Roles of Protein on the Cobalt Alloy Surface Degradation for Biomedical Implant Through Tribocorrosion Mechanisms

Previous investigation has established the formation of tribofilm is influenced by tribochemical reactions between the electrolyte and the articulating surface of cobalt alloy through sliding tests in various simulated fluids. Although it has successfully characterized the film composition via spectroscopy analysis and indicated to have impact on material loss, a comprehensive understanding of the material degradation mechanism in tribocorrosion condition was still lacking. Therefore, this study aims to investigate the role of protein in the tribocorrosive degradation of cobalt-chromium-molybdenum (CoCrMo) alloy in different simulated physiological electrolytes. Using a similar testing protocol, tribocorrosion tests were conducted with reciprocating ceramic ball against CoCrMo samples immersed in saline and culture medium, compared to both electrolytes diluted with 25% fetal bovine serum (FBS). Synergistic and mechanistic approaches were employed to model the tribocorrosive degradation. Results reveal that protein plays a beneficial role in reducing corrosive (electrochemical) surface degradation under tribocorrosion condition, whilst increasing mechanical wear degradation in the process. Despite studies have shown that tribocorrosion behavior in metal alloys is highly influenced by the presence of organic matter, this study provides a more clarity of the roles played by protein in tribocorrosive degradation on CoCrMo surface as its novel finding.

Wear evaluation of hard disk drive head based on a converter-like neural network

Hard disk drives (HDDs) are indispensable industrial products in modern information society, with their heads playing a critical role in read/write performance, which is directly affected by head wear. Head wear at nanoscale challenges the applicability of traditional macroscopic physical wear models, necessitating innovative solutions for accurate wear estimation. Data-driven methodologies have emerged as an effective paradigm, yet facing limitations when addressing the multiple phases of head wear degradation. Herein, building on insights into nanoscale wear and interface dynamics, we devised a "converter" module. Simultaneously, a physical constraint is incorporated into the network to guarantee the physical compliance consistency of wear outcomes. The effectiveness of this method is validated on real worn heads. An interpretive analysis is performed, involving the visualization of GRU module's hidden units and the quantification of feature’s activity contribution. Our investigations revealed that the converter results in a higher proportion of neurons becoming actively engaged and discriminative in wear evaluation, thereby enhancing the network's sensitivity to wear-related variations. Meanwhile, the trace ratios of scatter indicate that the converter improves the discriminability of the input data, thereby bolstering the network's capacity to differentiate between wear states.

Digital Twin for wear degradation of sliding bearing based on PFENN

The emergence of “Digital Twin” has raised higher demands for the health management of industrial equipment. Sliding bearings, as crucial supporting components in rotating machinery, may wear under radial loads, posing potential risks to the safety and stability of the system. However, The wear profile is usually challenging to measure online, making it difficult to present the real-time wear status of the bearing. To address this issue, this paper proposes a novel framework of digital twin by combining physics-driven with data-driven model in parallel. Within this framework, we establish a parallel hybrid model of finite element and deep neural network (PFENN), used for monitoring and visualizing the real-time wear profile of sliding bearing. In the offline phase, PFENN gets the wear profile through numerical calculations of finite element model and constructs the space of bearing wear states; during the online phase, PFENN captures the bearing’s vibration signal and obtains the maximum wear depth through deep neural network. Subsequently, mapping the wear profile in the wear data space based on the maximum wear depth and visually presenting. Additionally, we employ relevance vector regression with a sliding window to fit the bearing degradation curve for predicting the remaining useful life. Experimental validation confirmed PFENN’s 93% profile accuracy and the capability for real-time monitoring and prediction, meeting the demands for high-fidelity digital twinning and real-time performance. This research also introduces new methodologies and perspectives for industrial equipment health management.

Comprehensive behavior of reduced graphene oxide (rGO) reinforced plasma sprayed nanostructured WC–Co coatings

The effects of reduced graphene oxide (rGO) as an additive on the microstructure, mechanical properties, and tribological behavior of air plasma sprayed (APS) nanostructured tungsten carbide–cobalt (WC-25 wt.%Co) coatings were studied. The rGO was added in three distinct weight percentage proportions of 0.5, 1, and 1.5 wt.%. The microhardness and surface roughness of the APS sprayed nanostructured WC-25 wt.%Co coatings reinforced with rGO were changed significantly. Microstructural alterations in the coatings were also noticed with the addition of additives. Gluing of rGO amid coating splats was also seen in the microstructure of the coatings. The coatings’ microstructure revealed the presence of tungsten (W), tungsten carbide (WC), and semi-tungsten carbide (W2C) as the primary phases. The existence of the W2C phase in the microstructure of the coatings was mostly due to the decarbonization of WC at higher temperatures during plasma spraying. It was also found that the amount of wear of the coatings was exceptionally low because the rGO could not be extracted from the 1.5 wt.% rGO-reinforced coatings particularly. The wear degradation in the 0.5 and 1.0 wt.% rGO added coatings was mostly due to delimitation and the subsequent pulverization, notably at loads and sliding speeds of 1.5 kgf and 0.17 m/s.

Accurate tool wear and breakage monitoring method for milling process based on vision and laser sensor fusion

Accurate online acquisition of tool wear degradation indicators is an important prerequisite for tool wear monitoring and tool remaining life prediction. The tool degradation process is usually accompanied by the flank face of cutting tool wear and blade breakage, however, the existing degradation indicators only consider the flank face wear value, but not the tool breakage value, resulting in the lack of accuracy of degradation indicators. To this end, an online identification method of tool degradation indicators based on the fusion of image sensors and laser displacement sensors is proposed, which adopts the VGG16-UNet network to identify the wear value in the image and obtains the tool breakage value based on the time series data of the laser displacement sensor. Finally, the tool wear and breakage degradation label containing wear and breakage values is established. Compared to manual measurements, the absolute average error is within 15 µms for cutter face damage values and within 3 µms for cutter face wear values.

A life-cycle dynamic wear degradation model of planetary gear systems

An accurate prediction model for surface wear in planetary gear systems is essential to decipher wear fault mechanisms and facilitate model-driven failure prognosis. A high-fidelity dynamic wear model relies on the appropriate characterization and updating strategy of the tooth macro-geometry, contact parameters and dynamic load. A geometrically adaptive-loaded tooth contact analysis (GA-LTCA) method is proposed to “bridge” wear morphology and dynamic behaviours. The finite element method and run-to-failure test are adopted to verify the GA-LTCA method and wear prediction model, respectively. Based on the proposed wear model, the life-cycle wear characteristics and the updating strategy of wear geometry are investigated. Owing to the consideration of the micro-cycle of the contact stress and geometrical updating, the predicted wear profile exhibits a desirable agreement with experimental results. The tooth profile deviation vector should be updated more frequently than the finite element model of worn gears. Only the finite element grid of the sun-planet gear pair should be updated because the wear of the sun-planet is much more serious than that of the ring-planet. Numerical results indicate the potential of the proposed high-fidelity model for digital twin-based wear prediction and life cycle failure prognosis.

The influence of competition between thermal expansion and braking parameter on the wear degradation of friction block

A simulation method is adopted to study the influence of the competition between thermal expansion and braking parameters on friction block wear. The reliability of the simulation is verified through the braking tests. The results indicate that the dynamic wear distribution of friction blocks is jointly affected by thermal expansion and relative motion of the friction pair. The thermal expansion causes concave wear and the relative movement causes eccentric wear of the friction block. The extreme numbers of the wear morphology can be used as an indicator for the wear evolution process. The changes in braking parameters affect the wear distribution of the friction block, the increase in braking pressure and speed exacerbates the eccentric wear and the concaved wear of the friction block, respectively. According to the results, a good match between thermal expansion behavior and braking parameters can optimize the wear behavior of the friction block.

Tribological Behaviour of Low Temperature Plasma Assisted Carburized 316 L Steel Produced by L-PBF Technique

Austenitic stainless steels produced by Laser Powder Bed Fusion (L-PBF) are interesting materials because of their excellent corrosion resistance. Due to their relatively low hardness, the tribological response of these materials is poor, which limits their use in applications where control of wear degradation is important. Nevertheless, low-temperature plasma-assisted carburisation is an interesting process for improving the wear resistance of austenitic stainless steels, as has been observed for wrought materials. In fact, the increase in hardness is guaranteed by a surface layer of expanded austenite (S-phase) with a thin top layer of amorphous carbon. In this work, AISI 316 L, produced by the L-PBF technique, was carburised using 5 different plasma gas mixtures (by varying the CH4/H2 ratio) at 475°C for 7 hours. The samples obtained were then subjected to a detailed microstructural characterisation in order to obtain information on surface modification. The morphological features of the surface were examined by SEM observations in top view and in cross-section. The tribological performance was evaluated by pin-on-flat tests (alumina sphere as counter-material) with 2 different applied loads and a stroke length of 5 mm. Friction coefficient, wear rate (stylus profilometer) and wear mechanisms (SEM) were also evaluated. Preliminary results show an increase in wear resistance of all plasma treated materials compared to the untreated material. The improved tribological performance was discussed in relation to the abrupt increase in surface hardness.

Remaining useful life prediction of ball screw based on integrating preload and precision

Ball screw remaining useful life (RUL) prediction is of great interest to industry and academia. However, the lack of a reliable prediction model limits accuracy. To address this, a hybrid method that combines physical-based and data-driven methods is proposed. A novel integrated index is developed to capture wear degradation by integrating the preload and precision parameters, and the optimum partitioning method is used for wear stage categorization. A physical-based method of a two-stage empirical model is constructed to characterize the randomness and nonlinearity of the degradation process. Model parameters are initialized and updated using particle filtering (PF) through a data-driven method for RUL prediction. To address discontinuous predictions in the empirical model, the random forest with PF (RF-PF) method is employed. The effectiveness of this approach is evaluated through experiments and comparisons with other methods.

Assessing the wear degradation of tungsten in acidic solutions using a lubricated tribocorrosion model

The tribocorrosion property of tungsten plays a great role in the chemical mechanical polishing of tungsten plugs used in integrated circuits. This study applied a lubricated tribocorrosion model to tungsten, verified and calibrated the model using results from laboratory tribocorrosion experiments, and predicted the effect of the key parameters on the mechanical and chemical wear behavior of tungsten. The results showed that the model could be successfully applied to the tribocorrosion of tungsten considering that the prevailing wear mechanism and the electrochemical corrosion property of tungsten fulfills the model's basic concepts. The model was able to predict the influence of the material, mechanical, electrochemical and rheological parameters on the mechanical wear and chemical wear behavior of tungsten. The model could clearly distinguish the contribution of the mechanical wear and chemical wear to the total wear degradation of tungsten, which provides useful instructions on how to control the related parameters in order to obtain tailored removal rate and surface quality in tungsten's chemical mechanical polishing process.

Selective oxidation induced wear degradation in cemented carbides

Understanding the wear micro-mechanisms of metallic binders is a prerequisite to designing durable cemented carbides for drilling applications. Here we studied the oxidational wear behaviors of WC-based composites encompassing particularly the CoAl binder (α phase, comprised of 5.7 wt% Al and bal. Co) and the CoFeAl binder (B2 phase) with same Al content. Unlike the oxidation of Co in the CoAl, selective oxidation of Al occurred unexpectedly in the CoFeAl, which weakened the binder integrity and aggravated the wear losses. Such unique behavior was rationalized computationally, where the Al2O3 formation was attributed to the low energy barrier to Al diffusion in the B2 structure. These findings could lay a useful basis for future development of robust cemented carbides.

Reliability Analysis of Mining Machinery Pick Subject to Competing Failure Processes With Continuous Shock and Changing Rate Degradation

Efficient mining machinery operation is essential for coal mining enterprises to reduce production costs, improve production efficiency, and maximize profits. As a key consumable part of mining machinery, the reliability of the pick directly determines the operational performance and service life of the machinery. During the mining process, the pick can be affected by natural wear and tear caused by the coal and rock and load shocks caused by the gangue and faults—that is, the result of the competing influence of soft failure caused by natural wear and tear and hard failure caused by random load shocks. At the same time, because the gangue and faults have a certain volume and hardness, a random load shock may last for a certain period of time, producing different acceleration effects on the pick wear under different hardness conditions. In this article, the reliability modeling of the pick competing failure process under random load shocks was studied by considering the influence of continuous shocks and the accelerated degradation at changing rates for different shock durations on the pick wear. First, the pick degradation model under random load shocks was established by considering the natural wear degradation, instantaneous shock degradation, and accelerated degradation at changing rates for different shock durations. Second, the pick reliability model under the competing failure mode was established on this basis. Finally, a numerical experiment and an effectiveness analysis of the pick reliability model were conducted based on the engineering data. The results showed that the competing failure reliability model of the pick considering continuous shocks and a changing degradation rate was more in line with engineering practice, which is of great significance for pick design optimization and improved reliability.

Comprehensive modeling strategy for thermomechanical tribological behavior analysis of railway vehicle disc brake system

A comprehensive modeling strategy for studying the thermomechanical tribological behaviors is proposed in this work. The wear degradation considering the influence of temperature (T) is predicted by Archard wear model with the help of the UMESHMOTION subroutine and arbitrary Lagrangian–Eulerian (ALE) remeshing technique. Adopting the proposed method, the thermomechanical tribological behaviors of railway vehicle disc brake system composed of forged steel brake disc and Cu-based powder metallurgy (PM) friction block are studied systematically. The effectiveness of the proposed methodology is validated by experimental test on a self-designed scaled brake test bench from the perspectives of interface temperature, wear degradation, friction noise and vibration, and contact status evolution. This work can provide an effective way for the investigation of thermomechanical tribological behaviors in the engineering field.

A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data

Based on the multistage and nonlinear characteristics of cutting tool wear, a hybrid method for cutting tool remaining useful life (RUL) prediction based on convolutional neural network (CNN) and multistage Wiener process using small sample data is proposed in this paper. First, wavelet transform is used to analyse the vibration data collected for cutting tools in the time–frequency domain to obtain an initial image data set. A CCVAE (conditional variational autoencoder with CNN) network is built to expand the initial data set and solve the problem of unbalanced data in different stages of cutting tool wear. The expanded data is used as the input of the CNN to monitor the tool wear state and amount of wear. Then, a nonlinear multistage Wiener process is established to describe the cutting tool wear degradation process and achieve accurate RUL prediction for the cutting tool. Specifically, a nonlinear Wiener process model corresponding to different degradation stages based on the change points between the cutting tool wear states output by the CNN is established. Maximum likelihood estimation and Bayesian methods are used to estimate and update the parameters, respectively, and the RUL value and corresponding probability density function (PDF) are obtained under different wear conditions. Finally, through experimental research and comparative analysis, it is found that the multistage nonlinear Wiener model accurately simulates cutting tool wear degradation, which verifies the feasibility and performance of the method proposed in this paper.

Corrosion and wear of coatings fabricated by HVOF-spraying of nanostructured and conventional WC–10Co-4Cr powders on Al7075-T6

The present work compares WC-10Co-4Cr coatings fabricated by HVOF spraying of a conventional powder (1.33 ± 0.67 μm) and a nanostructured powder (0.17 ± 0.06 μm) on Al7075-T6, in terms of microstructure and phase composition, microhardness and fracture toughness, corrosion performance in 3.5 wt% NaCl (potentiodynamic polarization, chronoamperometry, galvanic effect measurements and salt spraying) and sliding wear performance under lubrication. Τhe nanocoating, despite its greater microporosity and decarburization, displayed higher microhardness, fracture toughness and surface roughness compared to its conventional counterpart. A superiority of the corrosion behaviour and sliding wear behaviour of the nanocoating as compared to its conventional analogue was observed in terms of a higher resistance to general/localized corrosion, lower passive film conductivity, lower wear rate, subtler wear scars and greater occurrence of islands of tribofilms. The reasons for such superiority are being clarified. A good galvanic compatibility between the nanocoating and its substrate was manifested. Both coatings exhibited high corrosion resistance during salt spraying for 45 days. The mechanisms of corrosion and wear degradation are being proposed.

A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold

• A dependent competing failure model is established for drill lifetime estimation. • The effect of wear degradation on the sudden failure threshold is considered. • Reliability functions and useful life expression of the drill are derived. • A drilling experiment is provided for validating the proposed method. Accurate remaining useful life (RUL) prediction of the drill bit is central to ensuring the processing quality of products and enhancing processing safety and reliability. However, the majority of current methods are confined to considering the wear degradation during the drilling and ignore the combined effect of wear degradation and random shock on RUL. Therefore, it is difficult to the sudden failure prediction of the drill bit. To address this limitation, a degradation-shock dependent competing failure processes (DCFPs) based method considering time-shifting sudden failure threshold is proposed in this paper for RUL prediction. First, a degradation-shock DCFPs model is established based on the processing characteristics of the drill bit. Further, the expressions of reliability function and useful life taking into account the time-shifting sudden failure threshold are derived. Then, based on the Bayesian method, a two-step maximum likelihood estimation (MLE) is designed for parameter estimation, and the crucial parameters are updated utilizing a local gaussian importance resampling particle filter. Finally, a carbon composite laminate drilling experiment is provided for demonstration. Experimental results demonstrate the superiority of the proposed method for drill bit RUL prediction, especially when the drill bit is chipped.

Improving high-temperature wear resistance of NiCr matrix self-lubricating composites by controlling oxidation and surface texturing

• NiCr matrix composites with WS 2 as lubricant suffer severer wear degradation at high temperatures • NiCr-WS 2 -Ti composite with heterostructure is prepared by spark plasma sintering. • The composite NiCr-WS 2 -Ti exhibits low friction coefficient and high wear resistance at elevated temperatures • Ti is preferentially oxidized, forming abundant TiO 2 protrusions and constructing in situ a special surface texture. • By controlling oxidation and surface texturing, a protective glaze layer formed at surface, leading to excellent tribological properties Self-lubricating composites (SLCs) are widely used in the fields of aerospace and marine, but the conventional NiCr matrix SLCs with sulfide as solid lubricant often suffer from low wear resistance at high temperatures. In view of its high affinity with oxygen and also the high oxidation rate, appropriate amount of nano Ti was added to NiCr-WS 2 composites prepared by spark plasma sintering (SPS) to adjust the oxidation behavior and surface texture. When exposed to high temperature, Ti was preferentially oxidized in comparison to Ni and Cr, resulting in abundant TiO 2 protrusions and microdimples on the surface, i.e. in situ surface texturing. Besides, TiO 2 was of high chemical activity and readily to react with other oxide debris during high temperature sliding process to form compounds of NiTiO 3 and CrTi 2 O 5 . The high chemical activity of oxide debris that was conducive to sintering, combining with the special surface texture that stores as many wear debris as possible, promoted the rapid formation of a protective glaze layer on the sliding surface. The NiCr-WS 2 -Ti composite exhibited low friction coefficient but high wear resistance at elevated temperatures. Especially at 800 °C, it presented a wear rate of as low as (2.1±0.3) × 10 −5 mm 3 N −1 m −1 , accounting for only 2.7% of that of NiCr-WS 2 composite.