Wear Prediction Research Articles

Prediction of Tool Wear Rate and Tool Wear during Dry Orthogonal Cutting of Inconel 718

A new prediction method was proposed based on the positive feedback relationship between tool geometry and tool wear rate. Dry orthogonal cutting of Inconel 718 was used as a case study. Firstly, tool wear rate models and a tool wear prediction flowchart were proposed. Secondly, the evolution of the tool geometry during tool wear was analyzed considering the combined effect of tool crater wear and tool flank wear. Thirdly, the evolution of the cutting temperature, normal stress and tool–chip relative sliding velocity on the tool wear surface was studied, the evolution of the tool wear rate during tool wear was revealed. Finally, the evolution of the tool geometry and tool wear rate during tool wear were applied to the tool wear prediction method to accurately predict the tool wear. The prediction error of KT is less than 15% in comparison with the experimental results. The tool wear prediction method in this paper is helpful to improve the prediction accuracy of tool crater wear.

Dynamic computational wear model of PEEK-on-XLPE bearing couple in total hip replacements.

Understanding wear mechanisms is a key factor to prevent primary failures causing revision surgery in total hip replacement (THR) applications. This study introduces a wear prediction model of (Polyetheretherketone) PEEK-on-XLPE (cross-linked polyethylene) bearing couple utilized to investigate the wear mechanism under 3D-gait cycle loading over 5 million cycles (Mc). A 32-mm PEEK femoral head and 4-mm thick XLPE bearing liner with a 3-mm PEEK shell are modeled in a 3D explicit finite element modeling (FEM) program. The volumetric and linear wear rates of XLPE liner per every million cycles were predicted as 1.965 mm3/Mc, and 0.0032 mm/Mc respectively. These results are consistent with the literature. PEEK-on-XLPE bearing couple exhibits a promising wear performance used in THR application. The wear pattern evolution of the model is similar to that of conventional polyethylene liners. Therefore, PEEK could be proposed as an alternative material to the CoCr head, especially used in XLPE-bearing couples. The wear prediction model could be utilized to improve the design parameters with the aim of prolonging the life span of hip implants.

Development of research and prediction of wear of plasma coatings from nickel-clad graphite powder

Development of research and prediction of wear of plasma coatings from nickel-clad graphite powder

The multi-sensor-based measurement of machining signals and data fusion to develop predictive tool wear models for TiAlN-PVD coated carbide inserts during end milling of Inconel 617

Machining signals from the Force dynamometer, Acoustic Emission (AE), and Accelerometer are acquired and fused to develop Machine Learning (ML) models for tool wear monitoring of TiAlN-PVD coated carbide inserts. Milling experiments were performed on Inconel 617 with varied process parameter combinations until the tool flank wear met the failure criterion. Support Vector Regression, Random Forest Regression, and Long Short-Term Memory models are developed and compared based on a combination of sensor data fusion for tool wear predictions. It is observed that the Random Forest Regression approach can predict the tool wear with 94% accuracy compared to Support Vector Regression (85%) and Long Short-Term Memory (84%) models while using three-sensor data fusion. Further, the two-sensor data combination was used to test the relative efficacy of all the three developed machine learning tool wear models and found that the force dynamometer and the AE sensor fared better for Random Forest Regression and Long Short-Term Memory models in comparison to Support Vector Regression. For Support Vector Regression-based tool wear predictive models, force dynamometer and accelerometer data fusion performed better.

A steps-ahead tool wear prediction method based on support vector regression and particle filtering

This paper develops a steps-ahead tool wear prediction method based on particle filtering and support vector regression. A degradation phase classification method is presented based on clustering algorithm and support vector machine. The support vector regression models are established to achieve the mapping between degradation features and the flank tool wear values. In online prediction, the measured signals are input into the SVM model to judge the current phase of the tool, and the particle filtering algorithm is used for online steps-ahead prediction of the features. The prediction of tool wear can be obtained by inputting the feature prediction results into the SVR models. The experimental tool wear dataset is introduced as an application example to test the proposed method. The results demonstrate the effectiveness of the proposed method, and the comparison with other tool wear prediction shows the advantage of the proposed method in prediction accuracy.

Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM

Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM

Predicting railway wheel wear by calibrating existing wear models: Principle and application

The accuracy of physics-based wheel wear prediction approaches depends not only on the vehicle-track dynamics model but more importantly on the wear model. The currently available wear models, however, were developed for specific wheel/rail materials and under fixed operational conditions, and they need to be calibrated when used in new operational conditions. In this paper we introduce a data-driven approach into the physics-based approach to automatically calibrate the existing wear model so that it can be extended to a new operational scenario. Specifically, based on a one-year follow-up measurement data on the wheel profile evolution of a metro train commuting on a Guangdong metro line, we introduce radial basis function (RBF) and particle swarm optimization (PSO) to determine the boundary of each wear regime and the wear coefficient for each wear regime in the Archard-local model. Finally, we present a calibrated Archard-local model and then apply it to two cases. One application case shows that the calibrated model can be used for long-term wear prediction and another case shows that the calibrated model can be used to roughly estimate the wear evolution over the tread region of vehicles operating on other China metro lines.

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.

Wear prediction of BTA drill based on finite element method for drilling laminated Inconel 625 deposited metal and FeCr alloy material

This study investigated the wear prediction of BTA drills for laminated Inconel 625 deposited metal and FeCr alloy materials using 3D finite element modelling. High interface temperature owing to increased cutting speed enhances the abrasive wear mechanism of Inconel 625, while FeCr alloy exhibits a predominantly adhesive wear mechanism. The interface temperature and normal pressure required as inputs for tool-wear prediction were obtained using response surface methodology (RSM), which was developed by combining the central composite design (CCD) with finite element method simulation. This approach significantly improved the computational efficiency and provides accurate predictions with the calibrated friction model. A modified Usui wear model was established considering the sensitivity of temperature to wear rate for Inconel 625. A significant improvement in prediction accuracy was observed compared with the original Usui equation. The proposed tool-wear prediction model was validated through experimental tests. The maximum predicted flank wear width deviations for the outer and intermediate edges of BTA drill were 2% and 7%, respectively. The results demonstrate the effectiveness of the proposed BTA drill wear-prediction approach for laminated materials with different wear characteristics, providing theoretical guidance for cutting-parameter optimisation and tool-replacement strategies.

Effect of tool flank wear on formation and prediction of tool flank rolling angle using finite element method

The formation and determination of the tool flank rolling angle are discussed in this study. First, the relationship between the tool flank rolling angle and the distribution of the tool wear rate on the tool-flank worn zone is developed by combining the finite element (FE) method and a turning experiment, and the determination method of the tool flank rolling angle using the FE simulation is revealed. Then, the evolution of the tool flank rolling angle with the tool flank wear progress is analyzed, and the evolution of the tool flank rolling angle is also considered in the determination of constants in the Usui model for tool flank wear. Finally, the empirical model of the tool flank rolling angle based on the tool flank wear width VB is proposed based on the FE method. The empirical model results are found to be in good agreement with the experimental results. The evolution of the tool flank rolling angle is considered in tool wear prediction. Compared with the method with a constant tool flank rolling angle, the tool wear prediction accuracy of our model is improved.

Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning

Effective tool wear monitoring (TWM) is essential for maintaining high quality and efficiency of cutting operations, preventing defective parts, and minimising economic losses. However, current research on TWM is mostly concentrated on specific working conditions, which limits its application. This study proposes a variable-condition TWM method based on multi-channel hybrid information and deep transfer learning. Multi-channel hybrid information is formed by combining multidimensional cutting force data and multidimensional process information. Based on this, a residual network (ResNet) is trained to obtain a TWM model that could predict the tool wear under multiple working conditions. Once variable working conditions arise (e.g., a change of tool or workpiece), the established ResNet model can be fine-tuned using a small amount of multi-channel hybrid information. These models were validated using datasets from a local experiment and NASA. The prediction results of the local dataset show that the model using hybrid information has a maximum tool wear prediction error of 9 µm and 2.7 µm under multiple and variable working conditions, respectively, which is much smaller than the prediction error of the model using only cutting force data. Moreover, fine-tuning the multi-channel hybrid information yields better prediction performance than using only sensor data for the TWM model. These results were demonstrated using the NASA dataset.

Prediction of Disc Cutter Wear Considering the Argillization Effect

When a tunnel boring machine (TBM) excavates in the mudstone layer, the slacking mudstone will be formed under the action of the disc cutter due to the argillization effect. Therefore, the cutter wear mechanism differs significantly from that in the hard rock layer, and the hazard of flat wear increases rapidly. To predict the cutter wear in the mudstone, a novel prediction model based on the “mixture lubrication-cavity expansion” theory is established in the present study, which takes the argillization effect into consideration. Initially, the cutter wear mechanism in the mudstone was revealed by the mixture lubrication theory. Then a modified cavity expansion model was proposed and the thickness of the slacking mudstone in the rock was determined. Subsequently, according to the force acting on the disc cutter, the motion of disc cutter during the rock breakage process, and the critical condition for flat wear, formulas for the normal and flat wear of a disc cutter considering the argillization effect were derived. Finally, the proposed model was employed to predict the wear loss of a disc cutter in a tunnel project in Chongqing. The effectiveness of the proposed model was verified. It was found that the force acting on the disc cutter is borne by the slacking mudstone and the cutter simultaneously. In the modified cavity expansion model, the zone of the rock beneath the cutter is divided into the core zone (0 ≤ r ≤ T/2), argillization zone (T/2 ≤ r ≤ ra), plastic zone (ra ≤ r ≤ rp), and elastic zone (r ≥ rp). The stress caused by the cutter is equal to the stress at the argillization zone. The argillization effect can reduce the wear loss to some extent due to the mixture lubrication, however, it aggravates the risk of flat wear. This study will enrich the understanding of the cutter wear mechanism during the tunneling. Furthermore, it will provide some references for the design of the TBM and the scheme of the tunneling project.

A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear

Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models.

Application of the extended FASTSIM for non-Hertzian contacts towards the prediction of wear and rolling contact fatigue of wheel/rail systems

In this paper, the extension of the FASTSIM algorithm to a SDEC region is implemented and a strategy is proposed to generalise this algorithm to any general non-elliptic contact patch. The extended FASTSIM is applied to the prediction of wheel wear and rolling contact fatigue under typical non-Hertzian wheel/rail contact conditions, using CONTACT as a reference and showing that the extended version of FASTSIM proposed in this work provides more accurate solutions for non-Hertzian contact conditions, compared to other widely used formulations of FASTISM.

Wear prediction of YN/Si3N4 tribopair lubricated with seawater based on time-series prediction Group Method of Data Handling (GMDH) method

The time-series prediction Group Method of Data Handling (GMDH) method is proposed to predict the friction coefficient of YN9X/Si3N4 tribopair of seawater hydraulic axial piston pump based on wear experiment. The effects of surface roughness, YN nickel content, load, sliding speed and temperature on the friction and wear interaction and sensitivity of YN/Si3N4 tribopair in seawater were studied by orthogonal test. The influence of corrosion wear on the tribological properties of YN material was quantitatively analyzed. The experimental results show that YN9X has better tribological properties than other materials in artificial seawater. Raman spectrum analysis proves that there is tribological reaction between the friction pairs, which can effectively improve the tribological properties of the materials.

From atomic attrition to mild wear at multi-asperity interfaces: The wear of hard Si3N4 repeatedly contacted against soft Si

Wear causes surfaces to be irreversibly damaged, thereby incurring significant economic cost, for instance in the semiconductor industry. Much progress has been made in describing wear at single asperity interfaces between silicon based materials (Si, SiOx, Si3N4), translating the fundamental understanding of wear into wear predictions and control over wear. Yet, predicting and controlling wear at industrially relevant multi-asperity interfaces remains a challenge, especially when considering the wear of the harder material subjected to repeated, nanometric scale displacement. We studied pre-sliding Si3N4-on-Si wear using the atomic force microscopy topography difference method and showed that the harder Si3N4 wears through either atomic attrition or ductile removal enhanced by subsurface damage, depending on the magnitude of the local Si3N4-on-Si contact pressure. Our methods and results bridge fundamental insight into wear based on nanoscale studies to industrial applications.

Study on the sliding wear map of cylinder liner − piston ring based on various operating parameters

The wear map can directly reflect the wear state and mechanism of the friction pair under different conditions and is widely used in wear prediction and fault diagnosis of steel materials. However, there are few reports on the wear map of the engine cylinder liner and its materials. In this study, the operating parameters were obtained through an engine bench test. The parameters near the cylinder liner's top dead center （TDC） were selected as wear test parameters. Thousands of hours of reciprocating lubrication wear tests were carried out on a multi-function wear tester. By analyzing the wear rate variation under different test parameters, the cylinder liner's wear state was divided into mild, medium, and severe wear. The morphology of the wear scar surface and cross-section of the cylinder liner was analyzed by scanning electron microscope (SEM) and then drawing the wear mechanism map. The results show that the primary wear mechanism under slight wear is spalling wear, accompanied by the plastic deformation phenomenon. Delamination wear is the primary mechanism in the medium wear state, accompanied by a small amount of massive spalling wear. The delamination wear is mainly caused by the crack generated by graphite in or below the deformation layer. The primary wear mechanism of the severe wear state is massive spalling. At this time, the graphite at the bottom of the deformation layer plays the role of primary cracks, which causes the plastic deformation layer to fall off.

Study on Internal Flow Characteristics and Abrasive Wear of Pelton Turbine in Sand Laden Water

When a Pelton turbine operates in sand laden water, the abrasive wear of its overflow components by high-speed jets is serious. Based on the VOF (volume of fluid) multiphase flow model, the SST (shear stress transfer) k-ω turbulence model, the particle motion Lagrangian model, the generic wear model, and the SIMPLEC (Semi-Implicit Method for Pressure Linked Equations Consistent) algorithm, the liquid–air–solid three-phase flow in the key overflow components of a Pelton turbine were simulated, the abrasive wear was predicted, and the internal sand-water flow characteristics and the abrasive wear of the overflow components were analyzed. The results show that the trailing edge at the root of the runner bucket, the leading face of the bucket near the root, the notch, and the splitter are severely worn. The abrasive wear of the splitter and the notch is more severe than that of the leading face of the bucket. The wear rate from the splitter to the trailing edge increases first and then decreases. The wear pattern of the needle tip is mainly “dotted”, while that of the nozzle opening is “flaky”, and the abrasive wear of the nozzle opening is more severe than that of the needle. The predicted results are consistent with the actual conditions at the site of the power station. This study provides a technical method for the prediction of abrasive wear of the Pelton turbine and a technical basis for the operation and maintenance of the power station.

Effect of Structural Flexibility of Wheelset/Track on Rail Wear

To investigate the influence of the structural deformation of the wheelset and track on rail wear in the longitudinal and lateral directions, a rail wear prediction model is established that can calculate the three-dimensional distribution of rail wear. The difference between the multi-rigid-body dynamic model and the rigid-flexible coupled dynamic model, which considers the structural flexibility of the wheelset and track, is compared in terms of the three-dimensional distribution of rail wear. The results show that the three-dimensional distributions of rail wear predicted by the two models are relatively similar. There is no obvious difference in the wear band, and the rail wear in the longitudinal direction is almost identical. The cross sections of the worn rail shapes determined by the two models are essentially the same, with a maximum difference of 3.6% in the average value of the wear areas of all cross sections. The track irregularity is the main reason for the uneven distribution of rail wear in the longitudinal direction. The position where the rail wear is more pronounced hardly varies with the evolution of the rail wear. It is recommended to use a multi-rigid-body dynamic model for the prediction of rail wear, which allows both calculation accuracy and efficiency.

Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning

The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.