Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings

TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

Wear characteristics and prediction of wheel profiles in high-speed trains

Wheel/rail relationship is a fundamental problem of railway system. Wear of wheel profiles has great effect on vehicle performance. Thus, it is important not just for the analysis of wear characteristics but for its prediction. Actual wheel profiles of the high-speed trains on service were measured in the high-speed line and the wear characteristics were analyzed which came to the following results. The wear location was centralized from-15 mm to 25 mm. The maximum wear value appeared at the area of 5 mm from tread center far from wheel flange and it was less than 1.5 mm. Then, wheel wear was fitted to get the polynomial functions on different locations and operation mileages. A binary numerical prediction model was raised to predict wheel wear. The prediction model was proved by vehicle system dynamics and wheel/rail contact geometry. The results show that the prediction model can reflect wear characteristics of measured profiles and vehicle performances.

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

The real-time prediction of bearing wear for roller cone bits using the Intelligent Drilling Advisory system （IDAs） may result in better performance in oil and gas drilling operations and reduce total drilling cost. IDAs is a real time engineering software and being developed for the oil and gas industry to enhance the performance of complex drilling processes providing meaningful analysis of drilling operational data. The prediction of bearing wear for roller cone bits is one of the most important engineering modules included into IDAs to analyze the drilling data in real time environment. The Bearing Wear Prediction module in IDAs uses a newly developed wear model considering drilling parameters such as weight on bit （WOB）, revolution per minute （RPM）, diameter of bit and hours drilled as a function of International Association of Drilling Contractors （IADC） bit bearing wear. The drilling engineers can evaluate bearing wear status including cumulative wear of roller cone bit in real time while drilling, using this intelligent system and make a decision on when to pull out the bit in time to avoid bearing failure. The wear prediction module as well as the intelligent system has been successfully tested and verified with field data from different wells drilled in Western Canada. The estimated cumulative wears from the analysis match close with the corresponding field values.

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.

Wear mechanism and life prediction of the ripper in a 9-m-diameter shield machine tunneling project of the Beijing new airport line in a sand-pebble stratum

Tool wear is a noteworthy problem in the process of shield tunneling,and the degree of wear varies with stratum.The sand-pebble strata in Beijing are typically mechanically unstable.However,many subways are buried wholly or partially in sand-pebble strata.Taking the Beijing New Airport line tunneling project as research background,this study evaluated the wear characteristics of the multiconfiguration rippers of a 9-m-diameter spoke-type shield tunneling machine in a sand-pebble stratum.The wear values of five ripper teeth and ripper flanks were analyzed based on field-measured data from the Beijing New Airport line project.As the analytical results show,the wear value generally increases as the installation radius enlarges with the rise of cutting trace length.The wear of the 190-rippers was divided into five categories:pedestal wear,ripper teeth collapse,uniform wear,ripper teeth falling off and ripper flank wear.Uniform wear of the ripper teeth and ripper flank wear were the two abrasion types of the 190-rippers.The teeth of the 155-rippers mostly maintained their cutting capacity under the protection of the 190-rippers.A wear prediction model of linear fitting field data was developed for a 190-ripper face to obtain the optimum shield driving distance in the sand-pebble stratum.The average wear coefficients of the 190-ripper before and after replacement matched well,being 0.045 and 0.066 mm/km,respectively.The results of this study provide a theoretical reference for tool wear prediction in shield construction under similar geological conditions.

Roll wear on finishing trains of ASP1700 hot strip mills

A great deal of research and practical production indicated that a perfectshape control system needs a precise prediction model of roll wear. According to the practical wearcurve of work roll in Angang ASP1700 hot strip mill, which was measured by a roll-profilemeter, themodel of wear curve caused by one single strip was established. The prediction of work-roll wear wasachieved by combining Fortran language and practical technology parameters. The calculated resultsagreed well with the measured.

Dynamic Investigations on the Wear Behavior of a 3D Revolute Joint Considering Time-Varying Contact Stiffness: Simulation and Experiment

The existence of the relative radial and axial movements of a revolute joint’s journal and bearing is widely known.The three-dimensional(3D)revolute joint model considers relative radial and axial clearances;therefore,the freedoms of motion and contact scenarios are more realistic than those of the two-dimensional model.This paper proposes a wear model that integrates the modeling of a 3D revolute clearance joint and the contact force and wear depth calculations.Time-varying contact stiffness is first considered in the contact force model.Also,a cycle-update wear depth calculation strategy is presented.A digital image correlation(DIC)non-contact measurement and a cylindricity test are conducted.The measurement results are compared with the numerical simulation,and the proposed model’s correctness and the wear depth calculation strategy are verified.The results show that the wear amount distribution on the bearing’s inner surface is uneven in the axial and radial directions due to the journal’s stochastic oscillations.The maximum wear depth locates where at the bearing’s edges the motion direction of the follower shifts.These find-ings help to seek the revolute joints’wear-prone parts and enhance their durability and reliability through improved design.

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybrid model

In tunnel construction with tunnel boring machines(TBMs),accurate prediction of the remaining useful life(RUL)of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns.This paper introduces a novel hybrid model,integrating fundamental and data-driven approaches,to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation.The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques,showcasing superior prediction accuracy compared to single-mechanism models.Additionally,the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model,leading to a high-performance wear prediction model.Using actual field data from a highway tunnel project in Shenzhen,the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models.The hybrid model outperforms the other models,achieving the highest accuracy in predicting TBM disc cutter wear(mean absolute error(MAE)=0.53,root mean square error(RMSE)=0.64).Furthermore,this study thoroughly analyzes the hybrid model’s generalization capability,revealing significant impacts of geological conditions on prediction accuracy.The model’s generalization capability is also improved by expanding and updating the data sets.The RUL estimation results provided by the hybrid model are straightforward and effective,making it a valuable tool by which construction staff can monitor TBM disc cutters.

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.

Electrode Wear Prediction in Milling Electrical Discharge Machining Based on Radial Basis Function Neural Network

Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%.

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.

Evolution and formation mechanism of rail corrugation in high-speed railways involving the longitudinal wheel-track coupling relationship

Rail corrugation poses a significant threat to train running safety in the field of railway engineering.Therefore,this study employs numerical analysis to investigate the evolution and formation mechanism of rail corrugation in high-speed railways(HSR).Firstly,a three-dimensional(3D)vehicle-track coupled dynamics(VTCD)model is established,which considers the longitudinal wheel-rail(WR)coupling relationship more adequately.Then,by integrating the USFD wear model into this 3D VTCD model,a long-term iterative wear model is developed to reproduce the corrugation evolution process.The predicted corrugation exhibits two distinct wavelength components and closely matches the sample obtained from China's HSR,validating the established model in terms of reliability.Furthermore,the formation mechanism of these two wavelength components is investigated by analyzing the harmonic behavior of vehicle-track coupled systems(VTCS)and the evolution law of rail corrugation under different calculation conditions.The findings reveal that the 3rd-order vertical rail local bending mode(RLBM)between two wheelsets of a bogie(TW-B)is the primary factor contributing to the formation of the long-wavelength component of rail corrugation.The discrete supports of the fasteners do not affect the 3rd-order vertical RLBM,which can be stably excited.Moreover,the vertical rail vibration has a substantial coupled effect on the longitudinal WR creep.When the 3rd-order vertical RLBM is excited,the coupled effect and the negative longitudinal WR creepage together evidently promote the formation of the short-wavelength component of rail corrugation.

Development and validation of a model for predicting wheel wear in high-speed trains

In this paper, we present a comprehensive model for the prediction of the evolution of high-speed train wheel profiles due to wear. The model consists of four modules： a multi-body model implemented with the commercial multi-body software SIMPACK to evaluate the dynamic response of the vehicle and track; a local contact model based on Hertzian theory and a novel method, named FaStrip （Sichani et al., 2016）, to calculate the normal and tangential forces, respectively; a wear model proposed by the University of Sheffield （known as the USFD wear function） to estimate the amount of material removed and its distribution along the wheel profile; and a smoothing and updating strategy. A simulation of the wheel wear of the high-speed train CRH3 in service on the Wuhan-Guangzhou railway line was performed. A virtual railway line based on the statistics of the line was used to represent the entire real track. The model was validated using the wheel wear data of the CRH3 operating on the Wuhan- Guangzhou line, monitored by the authors＇ research group. The results of the predictions and measurements were in good agreement.

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/machines.Wear occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between contacts.However,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment.Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time.A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon,followed by in situ classification using a state-of-the-art machine learning(ML)algorithm.Though this technique is better than offline inspection,it possesses inherent disadvantages for training the ML models.Ideally,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing.The collection of such a dataset is very cumbersome and expensive in practice,as in real industrial applications,the malfunction period is minimal compared to normal operation.Furthermore,classification models would not classify new wear phenomena from the normal regime if they are unfamiliar.As a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal regime.This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission(AE)signals captured using a microphone related to the normal regime.As a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal arrives.To achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and trained.During the validation procedure of the proposed CNN architectures,we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97%and 80%.Hence,our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.