Gradual Wear Research Articles

Закономерности износа плазмотронов при плазменной резке толстолистового проката на токе обратной полярности

The introduction describes the features of the process of plasma cutting of various metals and alloys using reverse-polarity plasma torches with and the features of cutting thick sheets. The purpose of the work is to study the wear process of plasma torches operating on reverse polarity current when cutting thick rolled sheets of aluminum and titanium alloys. Research methods include optical and scanning electron microscopy, filming of the cutting process and visual inspection of plasma torch elements after receiving specimens. Results and discussion. The section shows the appearance of the main working elements of the plasma torch after cutting in various modes, which led to both stable and gradual wear and to catastrophic failure of the plasma torch. The results of structural studies of the main characteristic zones of nozzles and electrodes after cutting are presented. The studies carried out made it possible to establish the main reasons for the failure of the working elements reverse-polarity plasma torches. The causes of catastrophic failure of plasma torches include failure to maintain the gap between the nozzle and the electrode and melting of the channel of gas supply into the discharge chamber. The wear of nozzles and electrodes in a stable mode can be intensified due to abnormal operation of the starting arc, the presence of manufacturing inaccuracies and excess gas pressure. In conclusion, the main conclusions based on the results of the research are formulated. The process of wear of electrodes, nozzles and body elements of plasma torches during operation at high electric arc power values is described.

Cryogenic Machining Performance of M303 at High Cutting Speeds

The material M303 is commonly used in the fabrication of machinery, automotive components, locomotive axle housings, and injection moulds. It is a stainless martensitic chromium steel known for its high strength, wear resistance, and corrosion resistance. The primary purpose of this study is to investigate the machinability of M303 under cryogenic conditions, specifically focusing on high cutting speeds. By exploring the effects of cryogenic machining on M303, the study aims to provide insights into the performance and characteristics of this material under extreme cutting conditions. This study investigates the influence of cutting parameters on the machinability of M303 in a cryogenic environment using liquid nitrogen (LN2) and coated carbide cutting tools in a high-speed turning process. The study focuses on high cutting speeds and examines essential machinability factors, including cutting forces, surface finish, and tool life. The experimental design utilises a Taguchi L4 orthogonal array to systematically study feed rates (0.1-0.2 mm/rev), depth of cut (0.2-0.6 mm), and high cutting speeds (260-340 m/min). Notably, at a low cutting speed of 260 m/min, coupled with low feed rates and depth of cut, the study reveals the longest tool life of 48.57 mintues was achieved. This condition is characterized by a good surface finish and low cutting forces with Ra of 0.9 µm and cutting force of 100 N respectively. The predominant wear occurs on the flank face, primarily due to fracturing and chipping, especially under high combinations of cutting parameters. Conversely, gradual wear is observed under low combinations of cutting parameters, resulting in an extended tool life. In conclusion, the application of LN2 proves effective under conditions of low cutting parameters. The study suggests that the risk of fracturing the cutting tool increases at higher feed rates and depths of cut, especially when combined with elevated cutting speeds. This research provides valuable insights into optimizing the machining of M303 for enhanced efficiency and tool longevity.

Study on wear of diamond during scratching SiC using molecular dynamics simulations

In this work, molecular dynamics (MD) simulations have been performed to investigate the wear mechanisms of diamond probe scratching 3C-SiC substrate. The number of worn atoms, the number of atoms in contact with the substrate, the friction force and normal force all increased significantly with the increase of the scratching depth. It is also found that as the scratching depth increased, the high stress region gradually shifted from the bottom to the rear end of the probe, indicating the risk of probe failure changing from gradual wear to fracture. Moreover, the SiC substrate undergone high temperature, high compressive stress and structural transformation in the wear process. We also found that only single atoms or atomic clusters are detached from the tip under the attractive interaction, indicating the occurrence of atomic attrition. The insights gained can shed light on the wear mechanism of diamond tool in machining SiC and provide a theoretical reference for controlling diamond tool wear in ultra-precision machining.

Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic Emission and Machine Learning

Human joint prostheses experience wear failure due to the complex interactions between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo). This study uses the wear classification to investigate the gradual and progressive abrasive wear mechanisms in UHMWPE. Pin-on-disc tests were conducted under simulated in vivo conditions, monitoring wear using Acoustic Emission (AE). Two Machine Learning (ML) frameworks were employed for wear classification: manual feature extraction with ML classifiers and a contrastive learning-based Convolutional Neural Network (CNN) with ML classifiers. The CNN-based feature extraction approach achieved superior classification performance (94% to 96%) compared to manual feature extraction (81% to 89%). The ML techniques enable accurate wear classification, aiding in understanding surface states and early failure detection. Real-time monitoring using AE sensors shows promise for interventions and improving prosthetic joint design.

Nonlinear dynamics of full-ceramic bearing-rotor system considering structural defects in cage pockets

Full ceramic bearings provide exceptional characteristics, including elevated accuracy and substantial load-bearing capability, rendering them indispensable in high-end rotating machinery. Still, the long-term use of the bearing will lead to the gradual wear and defects of the cage pocket, which may affect the unstable operation of the rotor system. This study examines the vibration characteristics of cage failure through simulation and experiments. To study the dynamic characteristics of the bearing-rotor system, a dimensionless model is established by using the lumped mass method, and a cage failure model is added to the model, which can study the influence of cage failure on the system stability. Phase trajectories, Poincaré plots, and bifurcation diagrams are employed to analyze the impact of individual parameters on the nonlinear vibration of the system. Ultimately, to gather cage vibration data from two different types of faulty, the experimental approaches are used. The findings indicate that in the event of cage failure, the system’s amplitude amplifies and the time-domain vibration signal exhibits distinct periodicity. Then, the vibration energy gradually focuses on low-frequency vibrations. Additionally, the spectrograms allow for easier identification of frequency components associated with the cage frequency, such as nfc and mfs ± nfc, the largest error is 3.32% when comparing the two models’ simulated results with the experimental data. The model accurately depicts the frequency characteristics of failures resulting from wear and defects in full ceramic bearing cages. It offers crucial information for detecting failures and monitoring the health of full ceramic bearings.

Nonlinear Dynamic Analysis of a Spur Gear Pair System with Wear Considering the Meshing Position

In this paper, a nonlinear dynamic model of a parallel shaft gear system consisting of two involute spur gears is developed to investigate the coupling effect between the gradual surface wear of gear teeth over time and nonlinear dynamic characteristics. A uniform wear model that accounts for how the volumetric wear of the gear teeth affect their meshing position, backlash, and stiffness is proposed. Additionally, a nonlinear dynamic model with six degrees of freedom is described that considers friction, time-varying gear backlash, and time-varying meshing stiffness. The proposed model significantly changes the mesh stiffness, not only in terms of value but also in terms of contact ratio. Furthermore, the nonlinear dynamic characteristics of the gear system vary significantly. It is found that the gradual wear of gear teeth affects the meshing position and further has a significant impact on the nonlinear dynamic characteristics of the spur gear system. This paper provides a basis for studying the nonlinear dynamic characteristics of the spur gear system as it experiences the gradual wear of teeth over time.

Analysis of lateral forces at the wheel-rail contact in cases of derailment Incidents

This article is dedicated to a thorough exploration of dynamic interactions within the intricate “wheel-rail” system during the occurrence of rolling stock derailments – a prominent and challenging aspect in the realm of railway transportation. Through meticulous analysis, the multifaceted factors influencing rolling stock derailments are meticulously dissected, embracing an in-depth scrutiny of dynamic interaction calculations. The investigation encompasses a comprehensive examination of various methodologies for comprehending the lateral forces that travel from the wheels of rolling stock to the rail track. By applying these methodologies, the study extrapolates insights from a real-world railway accident that transpired on Ukrainian territory. The research findings shine a light on a pivotal factor amplifying lateral forces: the gradual wear and tear of wheel flanges, coupled with the broadened gaps between axle boxes and the side frames of bogies. This intricate interplay ultimately leads to an augmentation in the angles at which wheel sets approach the rail track. Consequently, the research underscores the pressing need for an ongoing commitment to further exploration and the evolution of methodologies – a necessary endeavor to bolster rail safety comprehensively.

CONCEPTS ON THE RESISTANCE OF ACRYLIC TEETH, PMMA

Artificial acrylic teeth, also known as dentures, are a common solution for replacing missing or damaged teeth. Wear of artificial acrylic teeth can be influenced by several factors, such as: if dentures are ulilized and worn every day, they can suffer from gradual wear over time. Mastication and friction against other hard surfaces can lead to a gradual deterioration of artificial teeth. It is very important how artificial acrylic dental compositions are made, but more importantly it seems that it is the stratification that is of particular importance when we talk about wear resistance. The studies performed so far are quite relative because each tooth lining can withstand depending on the component of the constructive elements.

Self-Supervised Health Index Curve Generation for Condition-Based Predictive Maintenance

Modern machine degradation trend evaluation relies on the unsupervised model-based estimation of a health index (HI) from asset measurement data. This minimizes the need for timely human evaluation and avoids assumptions on the degradation shape. However, the comparability of multiple HI curves over time generated by unsupervised methods suffers from a scaling mismatch (non-coherent HIs) caused by the slightly different asset initial conditions and distinct HI model training. In this paper, we propose a novel self-supervised approach to obtain HI curves without suffering from the scale mismatch. Our approach uses an unsupervised autoencoder based on a convolutional neural network (CNN) to detect initial faults and autonomously label measurement samples. The resulting self-labeled data is used to train a 1D-CNN health predictor, effectively eliminating the scaling mismatch problem. On the basis of a bearing test-to-failure experiment, we show that our self-supervised scheme offers a promising solution for the non-coherent HI problem. In addition, we observed that our method indicates the gradual wear affecting the bearing prior to the independent analysis of a human expert.

A reduced-order machine-learning-based method for fault recognition in tool condition monitoring

The application of Machine Learning methodologies has been particularly noteworthy and abundant in pattern and symptom recognition across various research areas. However, Tool Condition Monitoring remains a challenging subject due to the gradual wearing out of cutting tools during the machining process. Such failure leads to reduced accuracy and quality of the machined surface of the workpiece, resulting in increased costs. This research proposes an innovative ML-based method to clarify failure symptoms of cutting tools in the frequency and time–frequency domains. The study involves five cutting tools as experimental case studies during a 200-minute machining operation. The results are validated using the Fast Fourier Transform, Short-time Fourier Transform, Empirical Mode Decomposition, and Variational Mode Decomposition methods, to demonstrate that the suggested methodology better identifies failure symptoms compared to other mentioned methods. One advantage of the proposed method is that considering a lower order of the system results in clearer frequency and time–frequency domain diagrams without sacrificing accuracy.

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning.

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable. However, the varying process conditions complicate this as the correlation between the sensor signals and tool condition is not directly apparent. Furthermore, machine learning (ML) knowledge is limited on the shop floor, preventing a manual adaption of the models to changing conditions. Therefore, this paper introduces a new method for remaining tool life prediction in individualized production using automated machine learning (AutoML). The method enables the incorporation of machining expert knowledge via the model inputs and outputs. It automatically creates end-to-end ML pipelines based on optimized ensembles of regression and forecasting models. An explainability algorithm visualizes the relevance of the model inputs for the decision making. The method is analyzed and compared to a manual state-of-the-art approach for series production in a comprehensive evaluation using a new milling dataset. The dataset represents gradual tool wear under changing workpieces and process parameters. Our AutoML method outperforms the state-of-the-art approach and the evaluation indicates that a transfer of methods designed for series production to variable process conditions is not easily possible. Overall, the new method optimizes individualized production economically and in terms of resources. Machining experts with limited ML knowledge can leverage their domain knowledge to develop, validate and adapt tool life models.

Acoustic roughness outcomes from rail maintenance processes

Rail roughness is a key determinant of the rolling noise emissions of many railway systems, particularly those systems where wheel roughness is relatively low. Rail roughness can vary considerably. The noise emissions from trains operating on very smooth, quiet, worn in systems can be 25 dBA less than the noise from identical trains operating on the same trackform but with rough or corrugated rails. Rail roughness varies over time, with changes resulting from gradual wear during normal operations but also from maintenance interventions. Rail grinding, milling or acoustic polishing can cause immediate and sometimes dramatic changes in rail acoustic surface condition, and noticeable changes in noise character. This paper documents the acoustic roughness outcomes achieved by various rail maintenance processes, in comparison to acoustic roughness from worn in tracks. The data presented includes measurements of surface finish from conventional rail grinding, rail milling, and from newer specialist acoustic grinding technologies. The rail roughness results are used to demonstrate the differences in noise emissions following rail maintenance activities. The results confirm the importance of maintaining low rail roughness as a means of noise control at source.

Remaining useful life prediction for complex systems with multiple indicators of stochastic correlation considering random shocks

The remaining useful life (RUL) prediction in complex systems is subject to complex working conditions, such as overloads, random vibration and shocks, gradual corrosion and wearing, that cause the complex and variable performance degradation of the mechanical system. Accurate prediction of RUL requires the extraction of multiple performance indicators strongly correlated to system degradation. Accordingly, we propose an RUL prediction framework for a multi-indicator system that considers the effects of random shocks and stochastic dependence. First, considering the influence of random shocks and measurement errors, a degradation state-space model is established using the stochastic correlation of multiple performance indicators. Next, the hidden state and unknown parameters of the state-space model are jointly estimated using the expectation–maximization algorithm in conjunction with the strong tracking filter algorithm based on online monitoring data. Subsequently, the probability density function expression of the RUL is derived for a multi-indicator system with three failure modes. Finally, the accuracy of the RUL prediction model is verified for a system affected by random shocks via numerical experiments. Compared with other prediction methods, the effectiveness and adaptability of the proposed method are verified using the C-MAPSS dataset and a high-temperature furnace case.

Evolution of the microstructure of amorphous polyethylene under friction-induced plastic flows: A reactive molecular investigation.

The plastic flow of ultra-high molecular weight polyethylene (UHMWPE) at a frictional interface, which is critical to the wear behavior, was investigated by reactive molecular dynamics simulations. The UHMWPE substrate was found to experience various deformations during the friction process. First, some polyethylene (PE) chains could detach from the substrate because of their rapid movement. Second, the frequent motion of PE chains also resulted in the intermittent formation and breaking of cavities between intermolecular PE chains. These deformations were more obvious on a surface with a convex protrusion, where the plowing effect exacerbated the cavitation and elastic deformation of PE chains. Correspondingly, the plastic flow in turn reconstructed the convex protrusion by displacing the surface atoms on the Fe slab. The plastic flow of PE chains broke the C-C bonds, and the carbon moieties were then chemically bonded onto the metal surface. A rapid change of atomic charge, hence, happened when the bonds broke. Meanwhile, PE chains release short alkyl radicals gradually after bond breakage, indicating gradual wear of the substrate during friction. This work provides molecular insight into the evolution of interfacial microstructure under plastic flow on a UHMWPE substrate.

Predictive diagnostics of RBMK reactor flowmeters

At the moment, there are 8 rectors of the RBMK type in operation. The safety of their operation is ensured by monitoring devices for key indicators. In this article, the main attention is paid to the control of the coolant flow through the fuel channel, carried out with the help of ball flow meters of the STORM-32M and STORM-8A types. During operation, there is a gradual wear of the raceway and the ball, which leads to the appearance of a negative error in the device, in other words, the readings become underestimated. In this regard, as well as due to the widespread use of ball flowmeters at nuclear power plants, the task of diagnosing the state of flowmeters is urgent. One of the approaches to determining the flow rate of the coolant is an approach based on the measurement and use of information about the activity of the coolant by the tightness control system of the fuel element shells (SKGO). The use of this radiation approach to control the flow of coolant has its advantages and difficulties. The advantage is that real experimental information about the presence of a coolant in the fuel channel is involved, and the difficulties are associated with receiving and processing the signals of the SCGO in real time and, consequently, with low efficiency of solving the problem. In this paper, we propose a method for predictive diagnostics of the flow meter condition using the design features of the multiple forced circulation circuit (CMPC) of the reactor. The adaptive parameter of the model was selected as a diagnostic parameter. Studies have shown that the increase in time variance in experimental flow values has a clear effect on the selected diagnostic feature , as a result of which it can be said that the observation of this kind of variance change in experimental data can serve as a sign of a malfunction of the flow meter.

Research on Cutting Layer Characteristics of Superalloy under High-Pressure Cooling.

Superalloys are widely used in the aerospace field and are a typical difficult-to-cut material. When the PCBN tool is used to cut superalloys, there will be problems such as a large cutting force, a high cutting temperature, and gradual tool wear. High-pressure cooling technology can effectively solve these problems. Therefore, this paper carried out an experimental study of a PCBN tool cutting superalloys under high-pressure cooling and analyzed the influence of high-pressure coolant on the characteristics of the cutting layer. The results show that the main cutting force can be reduced by 19~45% and 11~39% when cutting superalloys under high-pressure cooling compared with dry cutting and atmospheric pressure cutting, respectively, in the range of test parameters. The surface roughness of the machined workpiece is less affected by the high-pressure coolant, but the high-pressure coolant can help reduce the surface residual stress. The high-pressure coolant can effectively improve the chip's breaking ability. In order to ensure the service life of PCBN tools, when cutting superalloys under high-pressure cooling the coolant pressure should not be too high, and 50 bar is more appropriate. This provides a certain technical basis for the efficient cutting of superalloys under high-pressure cooling conditions.

Insights in α-Al2O3 degradation in multilayer CVD coated carbide tools when turning IN718

The degradation behaviour of textured CVD α-alumina has received significant attention within academic literature with regard to steel turning. In contrast, the research performed on Ni-based superalloys is yet to be brought up to the same level of understanding. Hence, the objective of this report will be to offer insights with regard to the degradation mechanisms of the textured alumina layer of CVD Ti(C,N)/α- Al2O3 coated carbides when turning a Ni-based superalloy, and to explore the role of different wear mechanisms that contribute to cutting tool failure. Adhesive wear was observed to be the primary wear mechanism responsible for coating failure, which can result in sudden mechanically driven grain pull-out and delamination events. Grain-pull out was observed across the entire contact zone; delamination was observed to occur most frequently around the trailing edge and depth-of-cut notches, and towards the end of the contact zone as the chip separates from the tool. Evidence of characteristic plastic deformation induced ridges in the alumina coating surface were also revealed within the sliding zone, leading to gradual wear of the alumina layer. These findings demonstrate that there are both similarities and differences in alumina degradation behaviour when compared to previous studies conducted on steel turning.

Field Modeling of the Influence of Defects Caused by Bending of Conductive Textronic Layers on Their Electrical Conductivity.

One of the critical parameters of thin-film electrically conductive structures in wearable electronics systems is their conductivity. In the process of using such structures, especially during bending, defects and microcracks appear that affect their electrical parameters. Understanding these phenomena in the case of thin layers made on flexible substrates, including textile ones, which are incorporated in sensors that monitor vital functions, is a key aspect when applying such solutions. Cracks and defects in such structures appearing during their use may be critical for the correct operation of such systems. In this study, the influence of defects resulting from the repeated bending of the conductive layer on its conductivity is analyzed. The anisotropic and partly stochastic characteristics of the defects are also taken into account. The defects are modeled in the form of broken lines, whose segments are generated in successive iterative steps, thus simulating the gradual wear of the layer material. The lengths and inclinations of these sections are determined randomly, which makes it possible to consider the irregularity of real defects of this type. It was found that near the percolation threshold, defects with a more irregular shape have a dominant effect on the reduction of conductivity due to the greater probability of their connection. The simulation results were compared with the experimental data. It was found that the dependence of the conductivity of the conductive layer on the number of bends is logarithmic. This allowed for the derivation of a formula linking the iteration number of the simulation procedure with the number of bends. Improving the strength of such layers is a technological challenge for researchers.

Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

Measurement of the Machined Surface Diameter by a Laser Triangulation Sensor and Optimalization of Turning Conditions Based on the Diameter Deviation and Tool Wear by GRA and ANOVA

One of the most important operations in the technological production process is the inspection of the manufactured product. The gradual wear of the tool affects the achievement of the required quality of the functional surfaces. In this research, we present the results of measuring the diameter deviation with a new generation laser triangulation sensor (LTS). At the same time, we have performed parametric optimization of several multi-responses, such as insert wear on the VBB flank side of cutting edge and diameter deviation Δd for a C45 steel sample during dry turning and using a sintered carbide insert, using the method of grey relational analysis (GRA) in combination with the Taguchi L16 orthogonal array. The optimal setting of input factors for multi-response parameters is ap 4-f 4-vc 1 i.e., depth of cut 0.5 mm, feed 0.4 mm per revolution, and a cutting speed of 70 m/min. At the same time, we present an evaluation of the significance of input factors using the method ANOVA.