Wear Prediction Model Research Articles

Prediction of TBM cutter wear in heterogeneous ground under high ambient pressure

To prevent tunnel face collapse in heterogeneous ground, the applied face pressure in the excavation chamber can be increased to stabilise the tunnel face, which may hinder cutter rotation during tunnel boring machine (TBM) tunnelling. This study proposes a TBM cutter wear prediction model that considers the impact of the variation in the cutter normal force on the cutterhead owing to the high applied face pressure and low penetration rate. First, the cutter motion mode in heterogeneous ground is investigated. The cutter is found to slide when cutting soft rock based on an analysis of the field data. The evolution process of the cutter wear morphology under heterogeneous ground conditions is summarised. A method for calculating the cutter normal force when the cutter slides on the tunnel face is proposed by referring to the cutting mechanism of the drag bits. The cutter hardness is measured on-site using a portable hardness tester. A semi-empirical model considering the variation in the cutter normal force on the cutterhead is proposed to predict the cutter wear in heterogeneous ground. Wear prediction is compared with other predictive models and field data for validation, and the discrepancies between the different models are discussed. The results show that the proposed model is effective for predicting TBM cutter wear under complex geological conditions.

Tool wear prediction based on K-means and Adaboost auto-encoder

Abstract A new tool wear prediction model is proposed to address the tool wear issue, aimed at monitoring tool wear based on specific task requirements and guiding tool replacement during actual cutting operations. In the data preprocessing phase, tool wear states are classified using unsupervised K-means clustering. The time, frequency, and time-frequency domain features are then labeled and fused using an autoencoder neural network applied to the original set of signal features from the tool. For tool wear prediction, an enhanced autoencoder neural network leveraging AdaBoost is employed to establish the prediction model. The reconstruction error serves as the chosen loss function to assess the autoencoder's performance, taking into account data correlation and the inherent lossy nature of the autoencoder. Experimental results from real machining data obtained from a CNC milling machine demonstrate that the proposed model achieves higher prediction accuracy while reducing data dimensions.

Numerical simulation of multiphase flow and prediction of sediment wear in a large Pelton turbine

AbstractThe problem of sediment wear presents a significant challenge for hydraulic turbines operating in sediment‐rich rivers, particularly for high‐head Pelton turbines. In this study, the VOF model, SST k–ω model, and DPM model were employed to simulate the gas–liquid–solid three‐phase flow within a large Pelton turbine, which operates under a rated water head of 671 m and has a single capacity of 500 MW, at a hydropower station situated on a sediment‐laden river. The sediment wear prediction model, derived from the sediment wear test of the model turbine, was utilized to forecast the sediment wear on the flow components of the Pelton turbine at the hydropower station. The results show that there are obvious pressure and velocity gradients near the nozzle outlet of the Pelton turbine in the power station, and the wear of the nozzle surface is gradually increasing, and the wear in the downstream area of the nozzle is more serious. The wear rate at the needle tip surface reached 1.372 μm/h, while the socket ring surface exhibited a wear rate of 3.175 μm/h. he highest wear rate recorded for the water bucket is 0.940 μm/h. After a year of continuous operation, the maximum erosion observed was 5.62 mm on the runner bucket made of stainless steel and wear‐resistant metal, 8.23 mm on the spray needle, and 19.05 mm on the nozzle mouth ring, highlighting the severity of sediment wear on the Pelton turbine. It is recommended that surface treatment technology be applied to the flow‐through components of the Pelton turbine at this hydropower station to enhance the wear resistance of the turbine and extend the operational life of the unit.

A Contact Mechanics Model for Surface Wear Prediction of Parallel-Axis Polymer Gears.

As surface wear is one of the major failure mechanisms in many applications that include polymer gears, lifetime prediction of polymer gears often requires time-consuming and expensive experimental testing. This study introduces a contact mechanics model for the surface wear prediction of polymer gears. The developed model, which is based on an iterative numerical procedure, employs a boundary element method (BEM) in conjunction with Archard's wear equation to predict wear depth on contacting tooth surfaces. The wear coefficients, necessary for the model development, have been determined experimentally for Polyoxymethylene (POM) and Polyvinylidene fluoride (PVDF) polymer gear samples by employing an abrasive wear model by the VDI 2736 guidelines for polymer gear design. To fully describe the complex changes in contact topography as the gears wear, the prediction model employs Winkler's surface formulation used for the computation of the contact pressure distribution and Weber's model for the computation of wear-induced changes in stiffness components as well as the alterations in the load-sharing factors with corresponding effects on the normal load distribution. The developed contact mechanics model has been validated through experimental testing of steel/polymer engagements after an arbitrary number of load cycles. Based on the comparison of the simulated and experimental results, it can be concluded that the developed model can be used to predict the surface wear of polymer gears, therefore reducing the need to perform experimental testing. One of the major benefits of the developed model is the possibility of assessing and visualizing the numerous contact parameters that simultaneously affect the wear behavior, which can be used to determine the wear patterns of contacting tooth surfaces after a certain number of load cycles, i.e., different lifetime stages of polymer gears.

Mechanism analysis and prediction of bull-nose cutter wear in multi-axis milling of Ti6Al4V with TiAlN coated inserts

Cutter wear is an unavoidable issue during the milling of difficult-to-machine materials such as Ti6Al4V, which severely affects cutting performance and surface quality. Especially in multi-axis milling, the complex and irregular contact area between cutter and workpiece increases the difficulty of wear prediction. This paper proposes a flank wear prediction model of bull-nose cutter in the multi-axis milling process of Ti6Al4V with TiAlN coated inserts. The cutter workpiece engagement (CWE) zone is analyzed and the wear contact length of cutting edge is obtained, which reveals the uneven distribution characteristics of flank wear. After that, by analyzing the geometric profile properties of cutter wear in multi-axis milling, abrasive and adhesive wear, a flank wear prediction model that takes coating hardness and cutting temperature model into account is established. The proposed model is calibrated and validated by the multi-axis milling experiment of Ti6Al4V with TiAlN coated inserts. The results show that the novel wear model has high accuracy with an average percentage error of 12.76 % and can accurately and quickly predict flank wear in multi-axis milling of Ti6Al4V. Finally, the cutter wear mechanism and chip formation are analyzed, which show that the main wear mechanism is abrasive wear and adhesive wear, and there was no obvious oxidation wear.

Prediction and analysis of wheel flange wear on small curved track considering wheel-rail conformal and lubricated contact

Wheel flange wear on curved tracks is a significant issue that poses a threat to the safety of rail vehicles and leads to increased maintenance costs for railway operators. This study aims to develop a numerical model to predict wheel flange wear on small radius curves. The model integrates an efficient wheel-rail conformal contact model with laboratory-tested wear coefficients. By using virtual penetration and influence coefficients for conformal geometries, the model accurately simulates the conformal contact between the wheel flange and the rail gauge corner. The validity of the wheel flange wear prediction model is confirmed through field tracking testing. Using the proposed model, the effects of track layout parameters and solid lubricants on wheel flange wear are investigated. This study provides theoretical guidance for the operation and maintenance of the wheel-rail system to reduce wear.

Prediction of rail wear band evolution under excitation by periodic track irregularities and its influence on vehicle dynamic performance

Periodic track irregularities (PTIs) are an important and sensitive source of vehicle vibration and have attracted considerable attention from railway researchers. However, previous studies have mainly focused on the irregularities themselves, with limited investigation of how the rail wear band (RWB) generated by PTIs excitation affects vehicle responses. To assess this, a three-dimensional rail wear prediction model is developed in this study to simulate the evolution of the RWB under PTIs conditions and then the effect of RWB excitation on the vehicle dynamic performance is investigated. The results show that the spatial trace of the rail contact points generated by the PTIs also exhibits clear periodic characteristics. Accordingly, the distribution pattern of the RWB also reflects these periodic features. The excitation of the periodic rail wear band (PRWB) has a negative effect on the vehicle running stability, especially when the vehicle is traveling at speeds that induce coupling resonance, which exacerbates the consequences. As the wear level of the PRWB increases, there is also an escalation in the vibration of the carbody. Furthermore, the amplitude of the change in the wheelset balance position resulting from variations in rail profile is found to serve as an effective descriptor for characterizing the wear intensity of PRWB, and the vibration amplitude of the carbody exhibits a clear linear correlation with PRWB excitation. The research results are helpful for understanding the relationship between PRWB excitation and vehicle vibration behavior and serve as a theoretical basis for track maintenance and profile optimization.

The mechanism of polygonal wheel wear considering a three-dimensional profile

Wheel polygonal wear is one of the most serious problems in the railway industry nowadays. To study the mechanism of wheel polygonal wear, the wear prediction model was established. The three-dimensional wheel profile model and the improved USFD wear prediction algorithm were used to research the wear state of the wheel. The vehicle system dynamic characteristics and wheel wear state under different running speeds are studied. The results show that the vibration state of the wheel will be affected by the vertical and lateral coupling vibration of the wheel when considering the three-dimensional wheel polygonal wear. Due to the influence of coupled vibration, the wheel will appear larger wheel-rail force fluctuation points at different phase angles. The large wheel-rail force fluctuation at these different phase angles is one of the reasons for the formation of wheel polygonal wear. At the same time, the natural mode of the vehicle system will also affect the order of the wheel polygon and the amplitude of the wheel polygon. This study provides a new idea for the research of the wheel polygon mechanism.

Predicting the wear on the non-working side of the rail profile registration method and its validation

Abstract Rail cross-section profile detection can assess the wear and tear of measured rails, providing crucial references for railway maintenance and upkeep. It is challenging to use the conventional method to register only the profile of rail head detection accurately. After the rail edge adjustment in some conventional railways, it becomes difficult to determine the base point of rail profile registration. Due to the wear of non-working edge of rail, a rail profile registration method is proposed. Firstly, a wear prediction model based on the generalized regression neural network is constructed by optimizing smoothing parameters through ten-fold cross-validation to achieve the optimal values. This model predicts the wear values on the non-working rail edge, providing reliable coordinates for two wear points as alignment reference points. Secondly, an initial alignment between the measured profile and the target profile is achieved using the nearest point iterative algorithm, which ensures that both profiles are in the same region and oriented similarly. Thirdly, the weight is assigned to the wear measurement points based on their respective wear values. The predicted positions of wear points are applied for calculating the translation and rotation parameters. These parameters could align the measured profile and facilitate the final profile alignment. Lastly, the experimental profiles under rail adjustment conditions were registered, verifying the accuracy of the proposed method. The research results indicate that under rail adjustment conditions, the mean squared error (MSE) between the calculated and actual values of lateral wear is 0.094 mm2, which is lower than the MSE from manual measurements. The calculated lateral wear value for experimental profiles achieved high alignment and calculation accuracy. This method can be applied in practical projects, providing an effective solution for rail head profile alignment and serving as a reference for profile alignment on the non-working rail edge with incomplete measurement data.

A predictive wear model in grinding using single-layer electroplated cBN tools

Wheel wear is a complex phenomenon significantly affecting grinding performance. While microscopic grit observation can detect various wear mechanisms, a predictive model is required to anticipate the total wear behavior under diverse grinding conditions. This study incorporates all wear mechanisms, including pullout, attrition, and fracture, into an analytical model aimed at determining improved grinding conditions. The grinding particles are approximated as standard constitutive geometries to model the wheel surface topography and the grit height reduction obtained from the wheel surface roughness is correlated with wear. The present model introduces an innovative approach to tracking and predicting wheel wear performance, eliminating the need for time-consuming grit micro-observation. Numerous grinding tests employing different tools were conducted, revealing strong agreement between the experimental results and the model predictions. The Application of this approach for predicting prolonged tool life is also demonstrated. Finally, the paper discusses how these findings contribute to a deeper understanding of the fundamental wear mechanisms in the grinding process.

Research on dynamic and wear prediction models of compound planetary transmission systems

Under high speeds and heavy loads, gear tooth surfaces will be worn, which will lead to accidents. The current wear prediction model is not comprehensive, but the wear prediction model that considers the temperature field and dynamic response can provide more accurate gear wear fault early warning than previous methods. Therefore, according to the Archard model, the discrete wear prediction model of the tooth surface is deduced in this paper, and the wear evolution trend of the tooth surface is predicted and analyzed in each operation stage. The tooth surface temperature field was simulated by the finite-element method, and the tooth surface wear prediction model was improved considering its influence on materials. In addition, the dynamic model of the compound planetary transmission system was established, and the wear prediction model was improved again by using its nonlinear meshing force. Finally, the tooth wear prediction model under the interaction of the dynamic response and temperature field was obtained. A wear measurement method based on duplicate adhesive film was proposed. The wear evolution trend along the tooth length direction and the tooth width direction were measured, and the accuracy of the wear prediction model was verified. The results show that the wear depth along the tooth width direction is larger in the middle part and smaller on both sides under the influence of temperature. After the interference of the dynamic response, the wear depth along the tooth rectangle fluctuates, which is very similar to the results of the theoretical prediction model. It provides a theoretical basis for the early warning of gear health status in practical engineering.

Prediction of micro wear depth between engineering polymers

In the polymer industry, changes in the wear area that affect surface aesthetics have emerged as a significant issue in applications where surface appearance is crucial. Even minute changes in surface height can be visible to the naked eye, necessitating the need for micro-scale wear prediction of polymers. While the Archard model has been verified for decades for metal-metal or polymer-metal contact, predicting wear between polymer-polymer contacts remains challenging due to limitations in wear measurement and wear coefficient determination. In this study, we propose a method for determining the wear coefficient for polymer-polymer contact and validate the prediction model with a component wear test on polymer parts. Wear depth, an essential indicator of wear is measured after the sliding wear test of the polycarbonate (PC) pin and Acrylonitrile butadiene styrene (ABS) copolymer plate. The wear prediction model with the obtained wear coefficient exhibits 99.7 % accuracy in comparison to the test wear depth. A component wear test is performed on engineering polymer parts, and wear prediction under test conditions is made using the proposed model. The measured and predicted wear depth of the component demonstrates a difference smaller than 5 μm, which is within the range of no change in scratch visibility. This study ensures the accurate wear prediction of engineering polymer components with affordable computational efficiency. The ability to predict polymer-polymer wear, previously unattainable, will enable several applications in the polymer industry, such as component design, scratch visibility prediction, and quality assurance.

A hybrid tool wear prediction model based on JDA

PurposeAiming at solving the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed.Design/methodology/approachFirstly, JDA is exploited to adapt the data features with different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task.FindingsThe results of milling experiments show that the maximum prediction accuracy of this method is 95.13%, and it has good recognition accuracy and generalization performance. Through the application of the tool wear hybrid prediction modeling method, the prediction accuracy and generalization performance of the model are improved and the tool monitoring is realized.Originality/valueThe research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical industrial applications.

Milling wear prediction using an artificial neural network model

The minimization of milling wear from prolonged contact between a tool and a workpiece is crucial in modern manufacturing, where high precision, efficiency, and productivity are required. Therefore, many researchers have developed systems for monitoring milling wear in real-time. However, while the adoption of multiple sensor signals can enhance the training effectiveness of the model, the complex setup process, data extraction, and feature analysis make it challenging to practically apply it for wear monitoring on production lines. Therefore, this study integrates various machining parameters of machine tools and a single type of sensor signal to extract features related to tool wear. These features are utilized as training data for an artificial neural network (ANN) model. The paper proposes a tool wear prediction ANN model based on automatic hyperparameter tuning and transfer learning, incorporating the architecture of Inception, DenseNet, and Layer Normalization. The network structure enhances the training efficiency for target features by expanding and reusing features from a single sensor's data. The combination of automatic hyperparameter tuning with a general optimizer optimizes the ANN architecture to achieve the best tool wear prediction model. Lastly, the proposed model is compared with other models using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) to evaluate the model performance. In the results, the proposed model shows the best performance among all the comparing models in all three metrics. The MAE is 0.03527, RMSE is 0.05135, and the R2 is 0.9611. Compared to the common models such as Random Forest, the MAE and RMSE can be reduced by 43.2%, and 43.5% respectively, and a 9.5% R2 performance increase.

Investigation on the causes of rail corrugation in high-speed railways based on the wheel–rail transient rolling contact model and wear model

This work investigated the causes of rail corrugation in a high-speed railway. A three-dimensional (3D) finite element (FE) model of the vehicle-track system (VTS) and a rail wear prediction model were developed. The 3D FE model was employed to study the dynamic interactions of the VTS excited by rail welds with varying wavelengths. The wear model calculates the wear depth on rail surface based on the Archard’s theory. The frequency response analysis and modal analysis were implemented to identify the vibration modes relevant to the corrugation. The phase relationship between the initial corrugation and the resulting dynamic wear was discussed. The vertical vibration at 543 Hz of the track was found to be responsible for the initiation of the studied corrugation. The excitation from the rail welds is one of the necessary conditions for the formation of this type of corrugation. The evolution of the phase shift of the dynamic wear with respect to the existing corrugation indicates that the corrugation caused by the rail welds is self-limited and the corrugation will stabilize at a certain value. The corrugation will translate on the rail surface due to a leading phase angle of the dynamic wear relative to the corrugation geometry.

Experimental Analysis and Wear Prediction Model Based on Friction Heat for Dry Sliding Contact

Experimental Analysis and Wear Prediction Model Based on Friction Heat for Dry Sliding Contact

Development of a wheel wear prediction model considering the interaction of abrasive block–wheel and wheel–rail

A reliable model for predicting wheel wear that considers the interaction between an abrasive block and a wheel as well as the contact between the wheel and rail is developed. First, a wheel wear prediction model that integrates vehicle system dynamics and the Archard wear model for wheel–rail interactions is developed. The wheel wear prediction procedure is validated using measured wheel profiles without the influence of abrasive blocks. Second, a new wheel wear prediction model that integrates the previously validated wheel wear prediction model, a finite element method, and a modified Archard wear model for abrasive block–wheel interactions is developed. The new method for predicting wheel wear is validated using the measured data with acting abrasive blocks. The results show that the tread wear rate due to wheel–rail contact is approximately 0.057 mm/104 km and that the tread wear rate due to abrasive block–wheel and wheel–rail contact is approximately 0.070 mm/104 km at a continuous air pressure of 3.0 bar.

A multi-stand work roll bending and shifting approach for profile contour and flatness control of electrical steel in multi-width schedule-free rolling using NSGA-II algorithm

To overcome the severe uneven work roll wear and uneven deformation of the wide-width electrical steel in the SFR (Schedule-Free Rolling) is an effective way to organize production flexibly and maximize production efficiency in hot rolling. In order to meet the increasingly stringent requirements of PCFC (Profile Contour and Flatness Control) of multi-width electrical steel in hot rolling, the collaborative control strategy of multi-stand work roll bending and shifting for profile and flatness of electrical steel in multi-width SFR was developed using NSGA-II approach. The work roll wear prediction model, thermal crown model, and strip crown model were established based on on-site measured data. Combining the work roll wear characteristics with principle, the mathematical model for work roll shifting considering multi-width was established. The main parameters of the multi-width work roll shifting strategy are obtained using the multi-objective NSGA-II algorithm. On this basis, a multi-stand collaborative control strategy, including rolling contour, short stroke roll shifting, and work roll bending, in the downstream stands during multi-width SFR was proposed. The industrial application of the developed control strategy shows that can prominently enhance the PCFC capability of electrical steel, which the uneven wear contour can be reduced.

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

Dynamic modeling and analysis considering friction-wear coupling of gear system

Gear wear becomes one of the important reasons for the fault of gear systems by causing tooth surface geometric deviations, changing time-varying mesh stiffness (TVMS), and increasing unloaded static transmission error (USTE). However, existing wear prediction models ignore the effects of friction and tooth profile parameter updates, which inevitably leads to unclear research on dynamic analysis. Based on the Archard equation, this work proposes a real-time iterative update model to calculate the wear evolution process with equivalent curvature radius updates. Subsequently, an improved friction-wear coupling model is developed to calculate the wear depth, TVMS, and USTE, with an iterative solution process based on the updating of friction and wear coefficients, and the load sharing factor (LSF). The effectiveness of the improved model is verified by wear experiments and finite element analysis. Based on the tribo-dynamic model of the gear-rotor-bearing systems, the dynamic characteristics considering friction-wear coupling are studied. The results indicate that the effect of friction intensifies as the speed decreases, while increased wear amplifies vibrations at most speeds, exacerbates meshing shocks, and initially expands then contracts the chaotic speed range. This work provides strong theoretical support for predicting wear depth and understanding gear wear behavior.