Wear Stage Research Articles

Research on tool wear and breakage state recognition of heavy milling 508III steel based on ResNet-CBAM

Milling water chamber head material 508 III steel belongs to extreme manufacturing, and the tool failure is very serious in the process of machining. How to monitor and identify the tool wear damage state in time and effectively is a key problem to be solved urgently. Identifying chip morphology changes under machining conditions plays a very important role in characterizing tool wear and breakage. Tool wear has an important influence on the chip morphology during the process of milling 508III steel, and changes in chip morphology can also reflect the tool wear and breakage state. Therefore, this paper takes the chip morphology image as an important feature for tool wear and breakage recognition, and uses Gaussian fuzzy estimation morphology method to preprocess the chip morphology image. Build a ResNet network combined by convolutional block attention module (ResNet-CBAM) tool wear and breakage recognition model, and explore the selection of the model backbone network, determination of the ResNet backbone network depth, and fusion methods of different attention mechanisms. Through the ablation experiment and visual analysis of the attention mechanism module, it is verified that the ResNet-CBAM model proposed in this paper has an accuracy rate of 96.67% in tool wear and breakage state recognition, especially in the stage of severe tool wear and breakage. This study realizes the prediction and early warning of tool life, and provides effective guarantee for the efficient cutting of large parts of high-end equipment and the stable operation of manufacturing system.

Tooth eruption status and bite force determine dental microwear texture gradients in albino rats (Rattus norvegicus forma domestica).

Dental microwear texture analysis (DMTA) is widely applied for inferring diet in vertebrates. Besides diet and ingesta properties, factors like wear stage and bite force may affect microwear formation, potentially leading to tooth position-specific microwear patterns. We investigated DMTA consistency along the upper cheek tooth row in young adult female rats at different growth stages, but with erupted adult dentitions. Bite forces for each molar (M) position were determined using muscle cross-sectional areas and lever arm mechanics. Rats were categorized into three size classes based on increasing skull length. Maximum bite force increased with size, while across all size classes, M3 bite force was almost 1.4 times higher than M1 bite force. In size class 1, M1 and M2 showed higher values than M3 for DMTA complexity, height, and volume parameters, while in size class 3, M1 had the lowest values. Comparing the same tooth position between size classes revealed opposing trends: M1 and M2 showed, for most parameters, decreasing roughness and complexity from size class 1-3, while M3 displayed the opposite trend, with size class 1 showing lowest, and either size class 2 or 3 the highest roughness and complexity values. This suggests that as rats age and M3 fully occludes, it becomes more utilized during mastication. DMTA, being a short-term diet proxy, is influenced by eruption and occlusion status changes. Our findings emphasize the importance of bite force and ontogenetic stage when interpreting microwear patterns and advise to select teeth in full occlusion for diet reconstruction.

Abrasive Wear Characteristics of 30MnB5 Steel for High-Speed Plough Tip of Agricultural Machinery in Southern Xinjiang Region

The high-speed plough tip is the core soil-touching component in southern Xinjiang field cultivation, but the interaction of the plough tip with the soil results in severe wear of the tip. The friction behaviour of sand and soil on plough tips was investigated with a homemade rotary abrasive wear tester in a one-factor multilevel test with three parameters: moisture content, velocity/rotational speed and friction distance. The objective was to study the friction behaviour of the sand soil and plough tip and analyse and characterise the wear amount, wear thickness and compressive stress distribution, three-dimensional wear morphology and microscopic wear morphology of the plough tips. The results show that with increasing speed, the wear amount changes more gently; with increasing soil water content, the soil adhesion force and lubricating water film increase so that the wear amount follows a second-order parabolic law; and with increasing friction distance, the wear amount gradually increases, and the wear rate also shows an upward trend when the plough tip is in the abrasive wear stage. The tip makes contact with the firmer soil with higher surface compressive stresses, causing the most wear. As the friction distance increases, sand particles become embedded in the contact surfaces, creating a groove effect along with spalling pits caused by fatigue wear. During the whole wear period, the groove effect is always accompanied by spalling pits appearing repeatedly. The analysis of the wear micromorphology of the plough tip shows that the number of flaking pits gradually decreases in the direction of soil movement, and the form of damage changes from impact wear to plough groove scratches. Abrasive wear interacts with corrosive wear to exacerbate plough tip wear.

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 2.8 % in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

A Study of Tribological Performance Prediction Based on Surface Texture Parameters

Surface texture parameters are a quantitative way of characterising surface topographical features and are closely related to tribological properties. In this paper, the correlation between surface topographic features and friction coefficient is investigated on the basis of the proposed improved correlation analysis model for high-speed milling surface topography of hardened steel. It was found that the friction coefficient could not be accurately reflected by a single parameter, so a prediction model for the friction coefficient based on Sxp, Sq, Sp, Sz, Sku and Sdq was developed. In this paper, the parameter screening was completed based on the changing characteristics of the data, and a multi-parameter prediction model of the friction coefficient in the stable wear stage was established, which provides a new idea to investigate the influence of the characteristics of surface topography on tribological performance.

Evolution of Lubrication Characteristics of Double-Nut Ball Screws Based on an Efficient Surface Roughness Modeling Method

Abstract Accurate prediction of lubrication characteristics, specifically film thickness and pressure distribution, is pivotal for ensuring optimal performance in ball screws. Despite its significance, there is a notable dearth of studies investigating the evolution of lubrication characteristics resulting from the changing surface roughness over prolonged operation of ball screws. Furthermore, obtaining the surface roughness of ball screws poses a challenge due to the limited loading capacity of measurement instruments. Traditional methods involving cutting the screw for surface roughness measurement are impractical for continuous monitoring during extended operation. To address this issue, the present study introduces an efficient approach to model the surface roughness of the raceway in double-nut ball screws. A profilometer is employed to measure profile roughness along two directions (parallel and perpendicular to the rolling direction) without the need to cut the screw raceway. The 2D power spectral densities and height probability densities of profile roughness are calculated to model the surface roughness, and the synthesized data are utilized to solve the Reynolds equation. The simulation method is validated through friction torque tests, demonstrating a calculation accuracy exceeding 92%. The study further explores the evolution of film thickness and pressure distribution in double-nut ball screws during running-in and steady wear stages, revealing severe asperity contact in the two nuts. Additionally, variations in load ratio, friction coefficient, and film thickness ratio (λ) are investigated. Considering the load ratio and λ of the slave nut, it can be inferred that boundary lubrication persists in the two nuts throughout the operation.

A visual guide for the Brabant index to score dental macrowear quantity and direction

AbstractThis study aimed to reduce subjectivity bias in scoring dental macrowear quantity and direction using the Brabant index, which previously relied solely on written descriptions. To achieve this, we present a new, optimized visual guide incorporating buccal and lingual scores. The optimization process involved conceptualizing and illustrating a visual guide using Holocene southern African hunter‐gatherer and herder teeth, featuring both buccal and lingual scores for multicuspid teeth. The guide was hand‐drawn using a stippling technique and digitized to depict surface details for each wear stage and tooth type. We conducted intra‐ and inter‐observer assessments to evaluate the optimized method using both the original and optimized Brabant indices. Statistical analysis was performed in R using Cohen's kappa for direction and Cohen's weighted kappa for quantity. Intra‐observer results for the original method yielded kappa values of 0.84 for direction and 0.94 for quantity, while the optimized version both resulted in improved values of 0.99. Inter‐observer results revealed some differences between an inexperienced and an experienced observer. The inexperienced observer achieved kappa scores of 0.20 for direction and 0.86 for quantity with the original method, and 0.17 and 0.80, respectively, with the optimized version. The experienced observer's results using the original index were 0.66 for direction and 0.89 for quantity, and 0.75 and 0.96, respectively, with the optimized version. These findings demonstrate that the optimized method enhances data reliability for experienced observers, highlighting the value of a published visual guide and multicuspid scoring adjustments. However, reduced or unappreciable changes in accuracies for the inexperienced observer illustrate the need for dental expertise when scoring for dental wear, even with a modified method.

Chaotic dynamic analysis of electrical contact resistance measured in sliding current-carrying friction

This work explores the dynamic laws of electrical contact resistance (ECR) signals measured in the sliding current-carrying friction tests based on chaos theory. It is found that ECR time series have a chaotic nature, strongly verified by the positive largest Lyapunov exponent λ1. The chaos may originate from the combined friction effects on short and long timescales. Phase space analysis indicates that the ECR phase trajectory adheres to the “convergence–stability” evolution law over time, which pertains closely to energy dissipation. Hence, the chaotic attractor exists during current-carrying friction. Strikingly, wear surfaces confirm that the ECR phase trajectory evolution process corresponds to the stages of “formation–keeping” of the chaotic attractor, and also agrees with the electrical wear stages of “running-in–steady-state”. Further, the chaos quantifier variation is consistent with the phase space analysis results to identify wear transition quantitatively.

Research on the influence of time-varying wear of wheel-rail profile on vehicle dynamic response

Employing a combination of field measurements and numerical simulations, this study initially evaluates the impact of simplifying wheel-rail profile wear on the calculation results of vehicle performance indicators. Subsequently, it investigates the influence of time-varying wear of wheel-rail profiles on the vehicle’s dynamic response, along with an analysis of change law in wheel wear depth and range under different wheel-rail profiles. The finding indicates that simplifying wheel-rail profile wear notably affect the accuracy of predicting wheel wear, with potential maximum prediction errors of up to 84 % compared to actual conditions. Furthermore, changes in the wheel-rail profile resulting from wear significantly impact the vehicle’s straight-line running performance and curve negotiation performance, particularly in the early and later stages of wheel-rail wear. Moreover, profiles in the mid-term wear stage significantly exacerbates wheel wear depth, demonstrating a maximum increase 4.33 times higher than that of the standard profile.

Investigation of tribological behavior of polytetrafluoroethylene/graphene composite

AbstractIn this study, we constructed molecular friction models for polytetrafluoroethylene (PTFE)/graphene composites with various defect ratios to represent their different wear stages and simulated their friction processes under different load conditions. The friction and wear mechanisms were revealed by analyzing the variations in their molecular architectures and energies during friction. The results indicate that the friction coefficient decreased with increasing load. When the load was constant, the friction coefficients of the composites containing defections were resemble, indicating that they remained unchanged in the different wear stages. The composite wear continued to increase during friction. However, the wear increment decreased gradually. In addition, by analyzing the changes in MSD, wear can be divided into initial, stable, and accelerated wear stages.Highlights Construct PTFE/Gr molecular models to represent their different wear stages. Simulate the dynamic changes of PTFE/Gr molecular structure during friction. The influence of PTFE/Gr dynamic structural changes was revealed. Reveal the normal force variation from an energy perspective.

A dual-stage wear rate model based on wear mechanisms analysis during cutting Inconel 718 with TiAlN coated tools

Inconel 718 is recognized as the difficult-to-cut material. TiAlN coated tool is frequently applied in machining of such material, due to its excellent hot hardness and oxidation resistance. However, research on the wear rate model during the cutting of Inconel 718 with TiAlN coated tools is limited. Therefore, this paper proposes a novel dual-stage wear rate model to predict wear of TiAlN coated tools when cutting Inconel 718 using finite element (FE) simulation. The dual-stage model integrates impacts from adhesive wear, diffusion wear and abrasive wear, and includes two wear rate models for the initial wear stage and steady wear stage, respectively. Subsequently, an in-depth analysis of tool wear mechanisms is conducted through Energy Dispersive Spectrometer (EDS) and Scanning Electron Microscope (SEM) techniques. The analysis results reveal that the adhesive wear dominates in the initial wear stage, while the steady wear stage witnesses the concurrent involvement of abrasive wear, diffusion wear and adhesive wear. Moreover, extensive cutting experiments validate that the model can predict wear of the TiAlN coated tools with an error margin of less than 8.5 %.

Influence of Coating Deformation Law on Characteristics of Axisymmetric Contact Problem at Different Stages of Wear

Influence of Coating Deformation Law on Characteristics of Axisymmetric Contact Problem at Different Stages of Wear

Wear mechanisms of Ni–W and Ni–W/Al2O3 composite coatings sliding against Si3N4 and AISI 52100 steel counterbodies

In this work, wear mechanisms of electrodeposited Ni–W and Ni–W/Al2O3 coatings sliding against Si3N4 and AISI 52100 steel have been comparatively studied. For both Ni–W and its composite coatings sliding against Si3N4, worn morphology shows the tribochemical reaction (tribooxidation) followed by abrasive wear is the dominated mechanism. Comparing with Ni–W coatings, the protective oxide layer is slower peeled off at tribooxidation stage and wear debris are easier squeezed on the Ni–W/Al2O3 during abrasive wear stage. The incorporated Al2O3 provide load bearing effect during microploughing and improve the wear resistance. As sliding against AISI 52100 steel, wear rate of Ni–W/Al2O3 is several times higher than that of Ni–W coatings. Conformity between surfaces of the Ni–W coating and counterbody leads to mild smoothening wear, while the plastic deformation dominated delamination makes severe wear of Ni–W/Al2O3. Deformation resistance of the coatings and transition of counterbodies materials from ceramic to hard metal are responsible for the different wear mechanisms. For Si3N4 ceramic, low thermal conductivity leads to tribooxidation followed by abrasive wear of both Ni–W and Ni–W/Al2O3 coatings. Relatively high deformation resistance of Ni–W coatings is responsible for the conformity as the premise for mild wear sliding against AISI 52100. Low deformation resistance of composite coatings induced by weak interfaces between Al2O3 and Ni–W matrix makes delamination sustained.

An international, open-access dataset of dental wear patterns and associated broad age classes in archaeological cattle mandibles

Zooarchaeologists investigate past interactions between animals, humans, and their environments by analyzing the remains of archaeological fauna. Age-at-death distributions are fundamental to faunal analysis and are often estimated by comparing exposed dentine patterns to standardized tooth wear stages that have been associated with relative age classes. We present Bubona, an international dataset of dental wear patterns and associated broad age classes in archaeological cattle mandibles. Our open-access dataset of 1460 data entries from nine counties is being used to create tooth-type specific reference tables of probable age class attribution for cattle mandibles lacking complete dentition. Bubona is a valuable resource for the innovation of new systems of age estimation for cattle and it is the creators hope that researchers will continue to both help expand the dataset by contributing their own data, as well as utilize the data to refine and innovate age-at-death estimation methods.

Wear behavior of white alumina and seeded gel abrasive wheels in ultrasonic vibration-assisted grinding of nickel-based single-crystal alloy

This study investigated the tool wear behavior of white alumina (WA) and seeded gel (SG) abrasive wheels in the ultrasonic vibration-assisted creep feed grinding (UVACFG) of nickel-based single-crystal alloy. The abrasive wheel wear characteristics were examined, and their effects on the grinding force, temperature, and surface quality were evaluated. Finally, the wheel wear mechanism in the UVACFG was discussed by analyzing the intermittent cutting behavior and wear patterns of a single grain. Experimental results indicated that the workpiece material adhesion and pore-clogging were the main wear patterns of abrasive wheels without ultrasonic vibration. In contrast, these were adequately mitigated after introducing ultrasonic vibration, and the main wear pattern for both wheels became the grain fracture. The WA wheel in the UVACFG experienced reduced radial wear by around 60.7 % in the initial wear state, 40.2 % in the stable wear state, and 25.3 % in the rapid wear stage, respectively, compared to the conventional grinding (CG). The intermittent cutting behavior of SG grains caused by high-frequency vibration promoted the micro-fracture capacity and ensured the coolant entered the grinding zone sufficiently, which reduced the grinding force and temperature by up to 63.2 % and 46.7 %, respectively, and meanwhile introduced the defects of high bulges and slight plastic deformation on the workpiece surface.

Study on the Cutting Performance and Remaining Life Prediction of Micro-Texture Ball End Milling Cutters for Titanium Alloys

As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. This heat leads to chip adhesion and exacerbates the wear of the ball end milling cutter, ultimately affecting its service life. Therefore, studying the residual life of the tool during the cutting process is essential to prevent significant impacts on the product’s surface quality due to tool damage and passivation. Most research on micro-texture cutters is based on experiments that analyze the wear patterns of cutters under various lubrication conditions and their influence on the cutting process. Different neural network prediction models are employed to enhance the accuracy and stability of tool life prediction models. However, the exploration of other superior models for predicting the life of micro-texture cutters remains ongoing. This paper is based on an experiment involving the milling of titanium alloy using a micro-pit-structured ball end milling cutter. It was found that the cutting force of the tool is higher during the initial and later wear stages. During the stable wear stage, the unevenness of the defective layer on the tool surface is reduced, increasing the contact area and reducing the surface pressure, thereby decreasing the cutting force. This study analyzes the influence of micro-pit structural parameters on the wear and milling force of the ball end milling cutter. By evaluating the wear value of the ball end milling cutter after each cut, the wear mechanism of the micro-texture cutter is identified. A deep-learning-based bidirectional long short-term memory (BiLSTM) neural network model for tool life prediction is developed. Through training and validation, the model’s accuracy and stability are continuously improved. A comparative analysis with different predictive models is conducted to determine whether the proposed model offers advantages over existing models, which is crucial for maximizing tool utilization and reducing manufacturing costs.

Tool wear induced multimode vibration and multiscale patterns in precision turning NAK80

Cubic boron nitride (CBN), with a hardness second only to diamond and an excellent chemical stability, is used to replace the role of diamond and machine precision/ultra-precision components in ferrous materials. However, the wear mechanisms of CBN tools and their wear-induced effects on machining system vibration seriously affect the machined surface quality. Therefore, this research focus on the wear characteristics of CBN tools in machining of NAK80. Based on the wear characteristics, the change in surface morphology and finish of the machined surface with tool wear was investigated. Meanwhile, the transformation of chip morphology at different wear stages was observed. Also, the effect of tool wear on the machining system vibration is analyzed by Butterworth filtering and Fourier transforming the cutting force signal. Research results show that the wear mechanisms of CBN cutting NAK80 are abrasive wear, oxidative wear and diffusive wear. Further, when the width of the flank wear of the CBN tool reaches 53 μm it causes multimodal vibration of the machining system, resulting in chattering patterns on the machined surface. As the width of the flank wear increased from 56.64 μm to 70.80 μm, the vibration frequency increased by 13.3 %. However, by reducing the feed rate from 5 μm/rev to 2 μm/rev, the vibration frequency can be reduced by 66.8 %. This study provides theoretical help in the research of wear of CBN cutting NAK80.

A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools.

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages.

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.

Experimental and Numerical Investigation of Bogie Hunting Instability for Railway Vehicles Based on Multiple Sensors.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel-rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel-rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.