The effectiveness of different wear indicators in quantifying wear on railway wheels of freight wagons

Railway infrastructure relies on the dynamic interaction between wheels and rails;thus,assessing wheel wear is a critical aspect of maintenance and safety.This paper focuses on the wheel-rail wear indicator T-gamma(Tγ).Amidst its use,it becomes apparent that Tγ,while valuable,fails to provide a comprehensive reflection of the actual material removal and actual contact format,which means that using only Tγas a target for optimization of profiles is not ideal.In this work,three different freight wagons are evaluated:a meter-gauge and a broad-gauge heavy haul vehicles from South American railways,and a standard-gauge freight vehicle operated in Europe,with different axle loads and dissimilar new wheel/rail profiles.These vehicles are subjected to comprehensive multibody simulations on various tracks.The simulations aimed to elucidate the intricate relationship between different wear indicators:Tγ,wear index,material removal,and maximum wear depth,under diverse curves,non-compensated lateral accelerations(A_(nc)),and speeds.Some findings showed a correlation of 0.96 between Tγand wear depth and 0.82 between wear index and material removed for the outer wheel.From the results,the Tγis better than the wear index to be used when analyzing wear depth while the wear index is more suited to foresee the material lost.The results also show the low influence of A_(nc)on wear index and Tγ.By considering these factors together,the study aims to improve the understanding of wheel-rail wear by selecting the best wear analysis approaches based on the effectiveness of each parameter.

Velocity-Incorporated Wear Model of Rolling Guide Shoe Material Selection

To ensure an accurate selection of rolling guide shoe materials,an analysis of the intricate relationship between linear speed and wear is imperative.Finite element simulations and experimental measurements are employed to evaluate four distinct types of materials:polyurethane,rubber,polytetrafluoroethylene(PTFE),and nylon.The speed-index of each material is measured,serving as a preparation for subsequent analysis.Furthermore,the velocity-wear factor is determined,providing insights into the resilience and durability of the material across varying speeds.Additionally,a wear model tailored specifically for viscoelastic bodies is explored,which is pivotal in understanding the wear mechanisms within the material.Leveraging this model,wear predictions are made under higher speed conditions,facilitating the choice of material for rolling guide shoes.To validate the accuracy of the model,the predicted degree of wear is compared with experimental data,ensuring its alignment with both theoretical principles and real-world performance.This comprehensive analysis has verified the effectiveness of the model in the selection of materials under high-speed conditions,thereby offering confidence in its reliability and ensuring optimal performance.

Calculation and Analysis of TVMS Considering Profile Shifts and Surface Wear Evolution Process of Spur Gear

Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation.Both profile shift and tooth surface wear(TSW)can impact the meshing characteristics by altering the involute tooth profile.In this study,a tooth stiffness model of spur gears that incorporates profile shift,TSW,tooth deformation,tooth contact deformation,fillet-foundation deformation,and gear body structure coupling is established.This model efficiently and accurately determines the time-varying mesh stiffness(TVMS).Additionally,an improved wear depth prediction method for spur gears is developed,which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions.Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process.Furthermore,the finite element method(FEM)is employed to accurately verify the values of TVMS and load sharing ratio(LSR)of profile-shifted gears and worn gears.This study quantitatively analyzes the effect of profile shift on the surface wear process,which suggests that gear profile shift can partially alleviate the negative effects of TSW.The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.

On the Polygonal Wear Evolution of Heavy-Haul Locomotive Wheels due to Wheel/Rail Flexibility and Its Mitigation Measures

Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track,and ultimately,lead to the shortening of service life of railway components.At present,wheel/rail medium-or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels,which are potentially resulted by the flexible deformations of the train/track system or other external excitations.In this work,the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations,in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented.Further,the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed.The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails.Execution of mixed-line operation of heavy-haul trains and application of multicut wheel re-profiling can effectively reduce the development of wheel polygonal wear.This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.

Research on the design method for uniform wear of shield cutters in sand-pebble strata

During shield tunneling in highly abrasive formations such as sand–pebble strata,nonuniform wear of shield cutters is inevitable due to the different cutting distances.Frequent downtimes and cutter replacements have become major obstacles to long-distance shield driving in sand–pebble strata.Based on the cutter wear characteristics in sand–pebble strata in Beijing,a design methodology for the cutterhead and cutters was established in this study to achieve uniform wear of all cutters by the principle of frictional wear.The applicability of the design method was verified through three-dimensional simulations using the engineering discrete element method.The results show that uniform wear of all cutters on the cutterhead could be achieved by installing different numbers of cutters on each trajectory radius and designing a curved spoke with a certain arch height according to the shield diameter.Under the uniform wear scheme,the cutter wear coefficient is greatly reduced,and the largest shield driving distance is increased by approximately 47%over the engineering scheme.The research results indicate that the problem of nonuniform cutter wear in shield excavation could be overcome,thereby providing guiding significance for theoretical innovation and construction of long-distance shield excavation in highly abrasive strata.

Prediction models of burst strength degradation for casing with considerations of both wear and corrosion

Casing wear and casing corrosion are serious problems affecting casing integrity failure in deep and ultra-deep wells.This paper aims to predict the casing burst strength with considerations of both wear and corrosion.Firstly,the crescent wear shape is simplified into three categories according to common mathematical models.Then,based on the mechano-electrochemical(M-E)interaction,the prediction model of corrosion depth is built with worn depth as the initial condition,and the prediction models of burst strength of the worn casing and corroded casing are obtained.Secondly,the accuracy of different prediction models is validated by numerical simulation,and the main influence factors on casing strength are obtained.At last,the theoretical models are applied to an ultra-deep well in Northwest China,and the dangerous well sections caused by wear and corrosion are predicted,and the corrosion rate threshold to ensure the safety of casing is obtained.The results show that the existence of wear defects results in a stress concentration and enhanced M-E interaction on corrosion depth growth.The accuracy of different mathematical models is different:the slot ring model is most accurate for predicting corrosion depth,and the eccentric model is most accurate for predicting the burst strength of corroded casing.The burst strength of the casing will be overestimated by more than one-third if the M-E interaction is neglected,so the coupling effect of wear and corrosion should be sufficiently considered in casing integrity evaluation.

Greatly enhanced corrosion/wear resistances of epoxy coating for Mg alloy through a synergistic effect between functionalized graphene and insulated blocking layer

The poor corrosion and wear resistances of Mg alloys seriously limit their potential applications in various industries.The conventional epoxy coating easily forms many intrinsic defects during the solidification process,which cannot provide sufficient protection.In the current study,we design a double-layer epoxy composite coating on Mg alloy with enhanced anti-corrosion/wear properties,via the spin-assisted assembly technique.The outer layer is functionalized graphene(FG)in waterborne epoxy resin(WEP)and the inner layer is Ce-based conversion(Ce)film.The FG sheets can be homogeneously dispersed within the epoxy matrix to fill the intrinsic defects and improve the barrier capability.The Ce film connects the outer layer with the substrate,showing the transition effect.The corrosion rate of Ce/WEP/FG composite coating is 2131 times lower than that of bare Mg alloy,and the wear rate is decreased by~90%.The improved corrosion resistance is attributed to the labyrinth effect(hindering the penetration of corrosive medium)and the obstruction of galvanic coupling behavior.The synergistic effect derived from the FG sheet and blocking layer exhibits great potential in realizing the improvement of multi-functional integration,which will open up a new avenue for the development of novel composite protection coatings of Mg alloys.

Novel Batch Polishing Method of Ceramic Cutting Inserts for Reducing Tool Wear

Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting applications.However,the wear of these inserts caused by friction between the workpiece and cutting inserts limits their overall effectiveness.In order to improve the tool life and reduce wear,this study introduces an emerging method called magnetic field-assisted batch polishing(MABP)for simultaneously polishing multiple ceramic cutting inserts.Several polishing experiments were conducted under different conditions,and the wear characteristics were clarified by cutting S136H steel.The results showed that after 15 min of polishing,the surface roughness at the flank face,edge,and nose of the inserts was reduced to below 2.5 nm,6.25 nm,and 45.8 nm,respectively.Furthermore,the nose radii of the inserts did not change significantly,and there were no significant changes in the weight percentage of elements before and after polishing.Additionally,the tool life of the batch polished inserts was found to be up to 1.75 times longer than that of unpolished inserts.These findings suggest that the MABP method is an effective way to mass polish ceramic cutting inserts,resulting in significantly reduced tool wear.Furthermore,this novel method offers new possibilities for polishing other tools.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

Advances in wearable and implantable bioelectronics for precision medicine

The continuous pursuit for a better quality of life promotes continuous advancements in intelligent technology.Flexible wearable and implantable bioelectronics have emerged as an innovative complement to rigid material-based electronic devices[1-3].Due to their distinct advantages in terms of ductile,ultrathin,and biocompatible features,these elastic and soft bioelectronic devices can be seamlessly mounted onto various real or artificial tissues and organs.

Microstructure transformations and improving wear resistance of austenitic stainless steel additively fabricated by arc-based DED process

In this study, austenitic stainless steel(ASS) was additively fabricated by an arc-based direct energy deposition(DED) technique. Macrostructure, microstructure, mechanical characteristics at different spatial orientations(0°, 90°, and 45°), and wear characteristics were evaluated at the deposited structure top, middle, and bottom regions. Results show that austenite(γ) and delta-ferrite(δ) phases make up most of the microstructure of additively fabricated SS316LSi steel. Within γ matrix, δ phase is dispersed both(within and along) grain boundaries, exhibiting a fine vermicular morphology. The bottom, middle,and top regions of WAAM deposited ASS exhibit similar values to those of wrought SS316L in the tensile and impact test findings. Notably, a drop in hardness values is observed as build height increases. During SEM examinations of fractured surfaces from tensile specimen, closed dimples were observed, indicating good ductility of as-built structure. Wear test findings show signs of mild oxidation and usual adhesive wear. By depositing a mechanically mixed composite layer, an increase in the oxidation percentage was discovered to facilitate healing of worn surfaces. The findings of this study will help in design, production and renovation of products/components that are prone to wear. WAAM-deposited ASS has remarkable strength and ability to withstand impacts;it can be used in the production of armour plates for defence applications, mainly military vehicles and aircraft.

Effect of Ni content on the wear behavior of Al-Si-Cu-Mg-Ni/SiC particles composites

In recent years,the addition of Ni has been widely acknowledged to be capable of enhancing the mechanical properties of Al-Si alloys.However,the effect of Ni on the wear behaviors of Al-Si alloys and Al matrix composites,particularly at elevated temperat-ures,remains an understudied area.In this study,Al-Si-Cu-Mg-Ni/20wt%SiC particles(SiCp)composites with varying Ni contents were prepared by using a semisolid stir casting method.The effect of Ni content on the dry sliding wear behavior of the prepared compos-ites was investigated through sliding tests at 25 and 350℃.Results indicated that theθ-Al_(2)Cu phase gradually diminished and eventually disappeared as the Ni content increased from 0wt%to 3wt%.This change was accompanied by the formation and increase inδ-Al_(3)CuNi andε-Al_(3)Ni phases in microstructures.The hardness and ultimate tensile strength of the as-cast composites improved,and the wear rates of the composites decreased from 5.29×10^(−4)to 1.94×10^(−4)mm^(3)/(N∙m)at 25℃and from 20.2×10^(−4)to 7×10^(−4)mm^(3)/(N∙m)at 350℃with the increase in Ni content from 0wt%to 2wt%.The enhancement in performance was due to the presence of strengthening network structures and additional Ni-containing phases in the composites.However,the wear rate of the 3Ni composite was approximately two times higher than that of the 2Ni composite due to the fracture and debonding of theε-Al_(3)Ni phase.Abrasive wear,delamination wear,and oxidation wear were the predominant wear mechanisms of the investigated composites at 25℃,whereas delamination wear and oxid-ation wear were dominant during sliding at 350℃.

Experimental study on the influences of cutter geometry and material on scraper wear during shield TBM tunnelling in abrasive sandy ground

When shield TBM tunnelling in abrasive sandy ground,the rational design of cutter parameters is critical to reduce tool wear and improve tunnelling efficiency.However,the influence mechanism of cutter parameters on scraper wear remains unclear due to the lack of a reliable test method.Geometry and material optimisation are often based on subjective experience,which is unfavourable for improving scraper geological adaptability.In the present study,the newly developed WHU-SAT soil abrasion test was used to evaluate the variation in scraper wear with cutter geometry,material and hardness.The influence mechanism of cutter parameters on scraper wear has been revealed according to the scratch characteristics of the scraper surface.Cutter geometry and material parameters have been optimised to reduce scraper wear.The results indicate that the variation in scraper wear with cutter geometry is related to the cutting resistance,frictional resistance and stress distribution.An appropriate increase in the front angle(or back angle)reduces the cutting resistance(or frictional resistance),while an excessive increase in the front angle(or back angle)reduces the edge angle and causes stress concentration.The optimal front angle,back angle and edge angle for quartz sand samples areα=25°,β=10°andγ=55°,respectively.The wear resistance of the modelled scrapers made of different metal materials is related to the chemical elements and microstructure.The wear resistances of the modelled scrapers made of 45#,06Cr19Ni10,42CrMo4 and 40CrNiMoA are 0.569,0.661,0.691 and 0.728 times those made of WC-Co,respectively.When the alloy hardness is less than 47 HRC(or greater than 58 HRC),scraper wear decreases slowly with increasing alloy hardness as the scratch depth of the particle asperity on the metal surface stabilizes at a high(or low)level.However,when the alloy hardness is between 47 HRC and 58 HRC,scraper wear decreases rapidly with increasing alloy hardness as the scratch depth transitions from high to low levels.The sensitive hardness interval and recommended hardness interval for quartz sand are[47,58]and[58,62],respectively.The present study provides a reference for optimising scraper parameters and improving cutterhead adaptability in abrasive sandy ground tunnelling.

Thermal fatigue and wear of compacted graphite iron brake discs with various thermomechanical properties

The increase in payload capacity of trucks has heightened the demand for cost-effective yet high performance brake discs.In this work,the thermal fatigue and wear of compacted graphite iron brake discs were investigated,aiming to provide an experimental foundation for achieving a balance between their thermal and mechanical properties.Compacted graphite iron brake discs with different tensile strengths,macrohardnesses,specific heat capacities and thermal diffusion coefficients were produced by changing the proportion and strength of ferrite.The peak temperature,pressure load and friction coefficient of compacted graphite iron brake discs were analyzed through inertia friction tests.The morphology of thermal cracks and 3D profiles of the worn surfaces were also discussed.It is found that the thermal fatigue of compacted graphite iron discs is determined by their thermal properties.A compacted graphite iron with the highest specific heat capacity and thermal diffusion coefficient exhibits optimal thermal fatigue resistance.Oxidization of the matrix at low temperatures significantly weakens the function of alloy strengthening in hindering the propagation of thermal cracks.Despite the reduced hardness,increasing the ferrite proportion can mitigate wear loss resulting from low disc temperatures and the absence of abrasive wear.

Health behavior outcomes in stroke survivors prescribed wearables for atrial fibrillation detection stratified by age

BACKGROUND Smartwatches have become readily accessible tools for detecting atrial fibrillation(AF).There remains limited data on how they affect psychosocial outcomes and engagement in older adults.We examine the health behavior outcomes of stroke survivors prescribed smartwatches for AF detection stratified by age.METHODS We analyzed data from the Pulsewatch study,a randomized controlled trial that enrolled patients(≥50 years)with a history of stroke or transient ischemic attack and CHA2DS2-VASc≥2.Intervention participants were equipped with a cardiac patch monitor and a smartwatch-app dyad,while control participants wore the cardiac patch monitor for up to 44 days.We evaluated health behavior parameters using standardized tools,including the Consumer Health Activation Index,the Generalized Anxiety Disorder questionnaire,the 12-Item Short Form Health Survey,and wear time of participants categorized into three age groups:Group 1(ages 50-60),Group 2(ages 61-69),and Group 3(ages 70-87).We performed statistical analysis using a mixedeffects repeated measures linear regression model to examine differences amongst age groups.RESULTS Comparative analysis between Groups 1,2 and 3 revealed no significant differences in anxiety,patient activation,perception of physical health and wear time.The use of smartwatch technology was associated with a decrease in perception of mental health for Group 2 compared to Group 1(β=-3.29,P=0.046).CONCLUSION Stroke survivors demonstrated a willingness to use smartwatches for AF monitoring.Importantly,among these study participants,the majority did not experience negative health behavior outcomes or decreased engagement as age increased.

Enhanced wear resistance,antibacterial performance,and biocompatibility using nanotubes containing nano-Ag and bioceramics in vitro

A good Ti-based joint implant should prevent stress shielding and achieve good bioactivity and anti-infection performance.To meet these requirements,the low-elastic-modulus alloy—Ti–35Nb–2Ta–3Zr—was used as the substrate,and functional coatings that contained bioceramics and Ag ions were prepared for coating on TiO_(2)nanotubes(diameter:(80±20)nm and(150±40)nm)using anodization,deposition,and spin-coating methods.The effects of the bioceramics(nano-β-tricalcium phosphate,microhydroxyapatite(micro-HA),and meso-CaSiO_(3))and Ag nanoparticles(size:(50±20)nm)on the antibacterial activity and the tribocorrosion,corrosion,and early in vitro osteogenic behaviors of the nanotubes were investigated.The tribocorrosion and corrosion results showed that the wear rate and corrosive rate were highly dependent on the features of the nanotube surface.Micro-HA showed great wear resistance with a wear rate of(1.26±0.06)×10^(−3)mm^(3)/(N·m)due to adhesive and abrasivewear.Meso-CaSiO_(3)showed enhanced cell adhesion,proliferation,and alkaline phosphatase activity.The coatings that contained nano-Ag exhibited good antibacterial activity with an antibacterial rate of≥89.5%against Escherichia coli.These findings indicate that hybrid coatings may have the potential to accelerate osteogenesis.

Influence of B4C and ZrB2 reinforcements on microstructural,mechanical and wear behaviour of AA 2014 aluminium matrix hybrid composites

Considering their affordability and high strength-to-weight ratio,lightweight aluminium alloys are the subject of intensive research aimed at improving their properties for use in the aerospace industry.This research effort aims to develop novel hybrid composites based on AA 2014 alloy through the use of liquid metallurgy stir casting to reinforce dual ceramic particles of Zirconium Diboride(ZrB_(2))and Boron Carbide(B4C).The weight percentage(wt%)of ZrB_(2) was varied(0,5,10,and 15),while a constant 5 wt%of B4C was maintained during this fabrication.The as-cast samples have been assessed using an Optical Microscope(OM)and a Scanning Electron Microscope(SEM)with Energy Dispersive Spectroscopy(EDS).The properties such as hardness,tensile strength,and wear characteristics of stir cast specimens were assessed to examine the impact of varying weight percentages of reinforcements in AA 2014 alloy.In particular,dry sliding wear behaviour was evaluated considering varied loads using a pin-on-disc tribotester.As the weight%of ZrB_(2) grew and B4C was incorporated,hybrid composites showed higher hardness,tensile strength,and wear resistance.Notably,the incorporation of a cumulative reinforcement consisting of 15 wt%ZrB_(2) and 5 wt%B4C resulted in a significant 31.86%increase in hardness and a 44.1%increase in tensile strength compared to AA 2014 alloy.In addition,it has been detected that wear resistance of hybrid composite pin(containing 20 wt%cumulative reinforcement)is higher than that of other stir cast wear test pins during the whole range of applied loads.Fractured surfaces of tensile specimens showed ductile fracture in the AA 2014 matrix and mixed mode for hybrid composites.Worn surfaces obtained employing higher applied load indicated abrasive wear with little plastic deformation for hybrid composites and dominant adhesive wear for matrix alloy.Hence,the superior mechanical and tribological performance of hybrid composites can be attributed to dual reinforcement particles being dispersed well and the effective transmission of load at this specific composition.

HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine tunnels.Although existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective distortion.So,a new safety helmet-wearing detection method based on deep learning is proposed.Firstly,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network.Secondly,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part.This block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small objects.Moreover,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance further.Extensive experiments on open dataset validate the proposed method.It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method.To be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version 7.The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.

LPCS Ni-Zn-Al_(2)O_(3) Intermediate Layer Enhanced SPS NiCr-Cr_(3)C_(2) Coating with Higher Corrosion and Wear Resistances

The double-layer NiCr-Cr_(3)C_(2)/Ni-Zn-Al_(2)O_(3) coatings with sufficient corrosion and wear resistance were prepared on low carbon steel substrates.The intermediate layers Ni-Zn-Al_(2)O_(3) were fabricated by using low-pressure cold spray (LPCS) method to improve the salt fog corrosion resistance properties of the supersonic plasma spray (SPS) NiCr-Cr_(3)C_(2) coatings.The friction and wear performance for the double-layer and single-layer NiCr-Cr_(3)C_(2) coatings were carried out by line-contact reciprocating sliding,respectively.Combined with the coating surface analysis techniques,the effect of the salt fog corrosion on the tribological properties of the double-layer coatings was studied.The results showed that the double-layer coatings exhibited better wear resistance than that of the single-layer coatings,due to the better corrosion resistance of the intermediate layer;the wear mass losses of the double-layer coatings was reduced by 70%than that of the single layer coatings and the wear mechanism of coatings after salt fog corrosion conditions is mainly corrosion wear.