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.

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.

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 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.

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.

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 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.

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.